From 7b340229bf5244d2dbe4ab55f17b1fc6f41c2714 Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Thu, 27 Nov 2025 18:23:47 +0530
Subject: [PATCH 01/24] ENH: addition of bella lui based 3 dof and 6 dof
 comparison notebook

- ENH: a new notebook bella_lui_3dof_vs_6dof.ipynb which uses new implementations of weathercocking model on 3dof
---
 .../bella_lui_3dof_vs_6dof_comparison.ipynb   | 1073 +++++++++++++++++
 1 file changed, 1073 insertions(+)
 create mode 100644 docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb

diff --git a/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb b/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
new file mode 100644
index 000000000..21c6835c8
--- /dev/null
+++ b/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
@@ -0,0 +1,1073 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f98dfaf3",
+   "metadata": {},
+   "source": [
+    "# Bella Lui 3-DOF vs 6-DOF Flight Simulation Comparison\n",
+    "\n",
+    "This notebook demonstrates the differences between the 3-DOF and 6-DOF simulation modes using the Bella Lui rocket from EPFL Rocket Team. It compares the trajectory, apogee, and other flight parameters between both simulation modes, including the effect of the weathercocking model on 3-DOF simulations.\n",
+    "\n",
+    "**Permission to use flight data given by Antoine Scardigli, 2020**\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "The 3-DOF simulation mode with the weathercocking model allows for:\n",
+    "- Faster simulations compared to 6-DOF\n",
+    "- Evolving attitude that aligns with the relative wind direction\n",
+    "- Configurable alignment rate via the `weathercock_coeff` parameter\n",
+    "\n",
+    "This example compares:\n",
+    "1. **6-DOF**: Full rotational and translational dynamics (reference)\n",
+    "2. **3-DOF (wc=0)**: Fixed attitude, no quaternion evolution\n",
+    "3. **3-DOF (wc=1)**: Default weathercocking, moderate alignment rate\n",
+    "4. **3-DOF (wc=5)**: High weathercocking, faster alignment rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ea1e6c69",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:54.280159Z",
+     "iopub.status.busy": "2025-11-27T11:34:54.279959Z",
+     "iopub.status.idle": "2025-11-27T11:34:56.811688Z",
+     "shell.execute_reply": "2025-11-27T11:34:56.810918Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "from rocketpy import Environment, Flight, Function, Rocket, SolidMotor\n",
+    "from rocketpy.rocket.point_mass_rocket import PointMassRocket\n",
+    "from rocketpy.motors.point_mass_motor import PointMassMotor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80fe381c",
+   "metadata": {},
+   "source": [
+    "## Define Bella Lui Rocket Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "000025f1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:56.814456Z",
+     "iopub.status.busy": "2025-11-27T11:34:56.814146Z",
+     "iopub.status.idle": "2025-11-27T11:34:56.819436Z",
+     "shell.execute_reply": "2025-11-27T11:34:56.818652Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "parameters = {\n",
+    "    # Mass Details\n",
+    "    \"rocket_mass\": (18.227 - 1, 0.010),  # 1.373 = propellant mass\n",
+    "    # propulsion details\n",
+    "    \"impulse\": (2157, 0.03 * 2157),\n",
+    "    \"burn_time\": (2.43, 0.1),\n",
+    "    \"nozzle_radius\": (44.45 / 1000, 0.001),\n",
+    "    \"throat_radius\": (21.4376 / 1000, 0.001),\n",
+    "    \"grain_separation\": (3 / 1000, 1 / 1000),\n",
+    "    \"grain_density\": (782.4, 30),\n",
+    "    \"grain_outer_radius\": (85.598 / 2000, 0.001),\n",
+    "    \"grain_initial_inner_radius\": (33.147 / 1000, 0.002),\n",
+    "    \"grain_initial_height\": (152.4 / 1000, 0.001),\n",
+    "    # Aerodynamic Details\n",
+    "    \"inertia_i\": (0.78267, 0.03 * 0.78267),\n",
+    "    \"inertia_z\": (0.064244, 0.03 * 0.064244),\n",
+    "    \"radius\": (156 / 2000, 0.001),\n",
+    "    \"distance_rocket_nozzle\": (-1.1356, 0.100),\n",
+    "    \"distance_rocket_propellant\": (-1, 0.100),\n",
+    "    \"power_off_drag\": (1, 0.05),\n",
+    "    \"power_on_drag\": (1, 0.05),\n",
+    "    \"nose_length\": (0.242, 0.001),\n",
+    "    \"nose_distance_to_cm\": (1.3, 0.100),\n",
+    "    \"fin_span\": (0.200, 0.001),\n",
+    "    \"fin_root_chord\": (0.280, 0.001),\n",
+    "    \"fin_tip_chord\": (0.125, 0.001),\n",
+    "    \"fin_distance_to_cm\": (-0.75, 0.100),\n",
+    "    \"tail_top_radius\": (156 / 2000, 0.001),\n",
+    "    \"tail_bottom_radius\": (135 / 2000, 0.001),\n",
+    "    \"tail_length\": (0.050, 0.001),\n",
+    "    \"tail_distance_to_cm\": (-1.0856, 0.001),\n",
+    "    # Launch and Environment Details\n",
+    "    \"wind_direction\": (0, 5),\n",
+    "    \"wind_speed\": (1, 0.05),\n",
+    "    \"inclination\": (89, 1),\n",
+    "    \"heading\": (45, 5),\n",
+    "    \"rail_length\": (4.2, 0.001),\n",
+    "    # Parachute Details\n",
+    "    \"CdS_drogue\": (np.pi / 4, 0.20 * np.pi / 4),\n",
+    "    \"lag_rec\": (1, 0.020),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c8dc3d4",
+   "metadata": {},
+   "source": [
+    "## Create Environment\n",
+    "\n",
+    "Set up the environment for the Bella Lui mission at Kaltbrunn, Switzerland."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "72bb4e4b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:56.821245Z",
+     "iopub.status.busy": "2025-11-27T11:34:56.821083Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.039301Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.038427Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Gravity Details\n",
+      "\n",
+      "Acceleration of gravity at surface level:    9.8100 m/s²\n",
+      "Acceleration of gravity at   2.000 km (ASL): 9.8100 m/s²\n",
+      "\n",
+      "\n",
+      "Launch Site Details\n",
+      "\n",
+      "Launch Date: 2020-02-22 13:00:00 UTC\n",
+      "Launch Site Latitude: 47.21348°\n",
+      "Launch Site Longitude: 9.00334°\n",
+      "Reference Datum: SIRGAS2000\n",
+      "Launch Site UTM coordinates: 500252.61 E    5228887.37 N\n",
+      "Launch Site UTM zone: 32T\n",
+      "Launch Site Surface Elevation: 407.0 m\n",
+      "\n",
+      "\n",
+      "Atmospheric Model Details\n",
+      "\n",
+      "Atmospheric Model Type: Reanalysis\n",
+      "Reanalysis Maximum Height: 2.000 km\n",
+      "Reanalysis Time Period: from 2020-02-22 00:00:00 to 2020-02-22 18:00:00 utc\n",
+      "Reanalysis Hour Interval: 4 hrs\n",
+      "Reanalysis Latitude Range: From 48.0° to 46.0°\n",
+      "Reanalysis Longitude Range: From 8.0° to 10.0°\n",
+      "\n",
+      "Surface Atmospheric Conditions\n",
+      "\n",
+      "Surface Wind Speed: 1.26 m/s\n",
+      "Surface Wind Direction: 213.21°\n",
+      "Surface Wind Heading: 33.21°\n",
+      "Surface Pressure: 980.43 hPa\n",
+      "Surface Temperature: 286.63 K\n",
+      "Surface Air Density: 1.192 kg/m³\n",
+      "Surface Speed of Sound: 339.39 m/s\n",
+      "\n",
+      "\n",
+      "Earth Model Details\n",
+      "\n",
+      "Earth Radius at Launch site: 6366.66 km\n",
+      "Semi-major Axis: 6378.14 km\n",
+      "Semi-minor Axis: 6356.75 km\n",
+      "Flattening: 0.0034\n",
+      "\n",
+      "\n",
+      "Atmospheric Model Plots\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 900x450 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "env = Environment(\n",
+    "    gravity=9.81,\n",
+    "    latitude=47.213476,\n",
+    "    longitude=9.003336,\n",
+    "    date=(2020, 2, 22, 13),\n",
+    "    elevation=407,\n",
+    ")\n",
+    "env.set_atmospheric_model(\n",
+    "    type=\"Reanalysis\",\n",
+    "    file=\"../../data/weather/bella_lui_weather_data_ERA5.nc\",\n",
+    "    dictionary=\"ECMWF\",\n",
+    ")\n",
+    "env.max_expected_height = 2000\n",
+    "env.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0fb238c",
+   "metadata": {},
+   "source": [
+    "## Create Motor\n",
+    "\n",
+    "Create the AeroTech K828FJ solid motor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cf4fdb34",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.041021Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.040840Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.160719Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.159902Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nozzle Details\n",
+      "Nozzle Radius: 0.04445 m\n",
+      "Nozzle Throat Radius: 0.0214376 m\n",
+      "\n",
+      "Grain Details\n",
+      "Number of Grains: 3\n",
+      "Grain Spacing: 0.003 m\n",
+      "Grain Density: 782.4 kg/m3\n",
+      "Grain Outer Radius: 0.042799 m\n",
+      "Grain Inner Radius: 0.033146999999999996 m\n",
+      "Grain Height: 0.1524 m\n",
+      "Grain Volume: 0.000 m3\n",
+      "Grain Mass: 0.275 kg\n",
+      "\n",
+      "Motor Details\n",
+      "Total Burning Time: 2.43 s\n",
+      "Total Propellant Mass: 0.824 kg\n",
+      "Structural Mass Ratio: 0.548\n",
+      "Average Propellant Exhaust Velocity: 2514.035 m/s\n",
+      "Average Thrust: 852.260 N\n",
+      "Maximum Thrust: 1303.79 N at 0.04 s after ignition.\n",
+      "Total Impulse: 2070.992 Ns\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "motor = SolidMotor(\n",
+    "    thrust_source=\"../../data/motors/aerotech/AeroTech_K828FJ.eng\",\n",
+    "    burn_time=parameters.get(\"burn_time\")[0],\n",
+    "    dry_mass=1,\n",
+    "    dry_inertia=(0, 0, 0),\n",
+    "    center_of_dry_mass_position=0,\n",
+    "    grains_center_of_mass_position=parameters.get(\"distance_rocket_propellant\")[0],\n",
+    "    grain_number=3,\n",
+    "    grain_separation=parameters.get(\"grain_separation\")[0],\n",
+    "    grain_density=parameters.get(\"grain_density\")[0],\n",
+    "    grain_outer_radius=parameters.get(\"grain_outer_radius\")[0],\n",
+    "    grain_initial_inner_radius=parameters.get(\"grain_initial_inner_radius\")[0],\n",
+    "    grain_initial_height=parameters.get(\"grain_initial_height\")[0],\n",
+    "    nozzle_radius=parameters.get(\"nozzle_radius\")[0],\n",
+    "    throat_radius=parameters.get(\"throat_radius\")[0],\n",
+    "    interpolation_method=\"linear\",\n",
+    "    nozzle_position=parameters.get(\"distance_rocket_nozzle\")[0],\n",
+    ")\n",
+    "motor.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07bb497d",
+   "metadata": {},
+   "source": [
+    "## Create Rocket\n",
+    "\n",
+    "Create the Bella Lui rocket with aerodynamic surfaces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9b602caf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.162423Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.162242Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.194919Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.194082Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Inertia Details\n",
+      "\n",
+      "Rocket Mass: 17.227 kg (without motor)\n",
+      "Rocket Dry Mass: 18.227 kg (with unloaded motor)\n",
+      "Rocket Loaded Mass: 19.051 kg\n",
+      "Rocket Structural Mass Ratio: 0.957\n",
+      "Rocket Inertia (with unloaded motor) 11: 2.002 kg*m2\n",
+      "Rocket Inertia (with unloaded motor) 22: 2.002 kg*m2\n",
+      "Rocket Inertia (with unloaded motor) 33: 0.064 kg*m2\n",
+      "Rocket Inertia (with unloaded motor) 12: 0.000 kg*m2\n",
+      "Rocket Inertia (with unloaded motor) 13: 0.000 kg*m2\n",
+      "Rocket Inertia (with unloaded motor) 23: 0.000 kg*m2\n",
+      "\n",
+      "Geometrical Parameters\n",
+      "\n",
+      "Rocket Maximum Radius: 0.078 m\n",
+      "Rocket Frontal Area: 0.019113 m2\n",
+      "\n",
+      "Rocket Distances\n",
+      "Rocket Center of Dry Mass - Center of Mass without Motor: 0.062 m\n",
+      "Rocket Center of Dry Mass - Nozzle Exit: 2.209 m\n",
+      "Rocket Center of Dry Mass - Center of Propellant Mass: 2.073 m\n",
+      "Rocket Center of Mass - Rocket Loaded Center of Mass: 0.090 m\n",
+      "\n",
+      "\n",
+      "Aerodynamics Lift Coefficient Derivatives\n",
+      "\n",
+      "Nose Cone Lift Coefficient Derivative: 2.000/rad\n",
+      "Fins Lift Coefficient Derivative: 10.281/rad\n",
+      "Tail Lift Coefficient Derivative: -0.502/rad\n",
+      "\n",
+      "Center of Pressure\n",
+      "\n",
+      "Nose Cone Center of Pressure position: 1.433 m\n",
+      "Fins Center of Pressure position: -0.871 m\n",
+      "Tail Center of Pressure position: -1.110 m\n",
+      "\n",
+      "Stability\n",
+      "\n",
+      "Center of Mass position (time=0): -0.152 m\n",
+      "Center of Pressure position (time=0): -0.469 m\n",
+      "Initial Static Margin (mach=0, time=0): 2.035 c\n",
+      "Final Static Margin (mach=0, time=burn_out): 2.609 c\n",
+      "Rocket Center of Mass (time=0) - Center of Pressure (mach=0): 0.317 m\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Parachute Drogue (cd_s = 0.7854 m2, trigger = <function drogue_trigger at 0x7f938f23d440>)>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bella_lui = Rocket(\n",
+    "    radius=parameters.get(\"radius\")[0],\n",
+    "    mass=parameters.get(\"rocket_mass\")[0],\n",
+    "    inertia=(\n",
+    "        parameters.get(\"inertia_i\")[0],\n",
+    "        parameters.get(\"inertia_i\")[0],\n",
+    "        parameters.get(\"inertia_z\")[0],\n",
+    "    ),\n",
+    "    power_off_drag=0.43,\n",
+    "    power_on_drag=0.43,\n",
+    "    center_of_mass_without_motor=0,\n",
+    ")\n",
+    "bella_lui.set_rail_buttons(0.1, -0.5)\n",
+    "bella_lui.add_motor(motor, parameters.get(\"distance_rocket_nozzle\")[0])\n",
+    "bella_lui.add_nose(\n",
+    "    length=parameters.get(\"nose_length\")[0],\n",
+    "    kind=\"tangent\",\n",
+    "    position=parameters.get(\"nose_distance_to_cm\")[0]\n",
+    "    + parameters.get(\"nose_length\")[0],\n",
+    ")\n",
+    "bella_lui.add_trapezoidal_fins(\n",
+    "    3,\n",
+    "    span=parameters.get(\"fin_span\")[0],\n",
+    "    root_chord=parameters.get(\"fin_root_chord\")[0],\n",
+    "    tip_chord=parameters.get(\"fin_tip_chord\")[0],\n",
+    "    position=parameters.get(\"fin_distance_to_cm\")[0],\n",
+    ")\n",
+    "bella_lui.add_tail(\n",
+    "    top_radius=parameters.get(\"tail_top_radius\")[0],\n",
+    "    bottom_radius=parameters.get(\"tail_bottom_radius\")[0],\n",
+    "    length=parameters.get(\"tail_length\")[0],\n",
+    "    position=parameters.get(\"tail_distance_to_cm\")[0],\n",
+    ")\n",
+    "\n",
+    "# Define aerodynamic drag coefficients\n",
+    "bella_lui.power_off_drag = Function(\n",
+    "    [\n",
+    "        (0.01, 0.51),\n",
+    "        (0.02, 0.46),\n",
+    "        (0.04, 0.43),\n",
+    "        (0.28, 0.43),\n",
+    "        (0.29, 0.44),\n",
+    "        (0.45, 0.44),\n",
+    "        (0.49, 0.46),\n",
+    "    ],\n",
+    "    \"Mach Number\",\n",
+    "    \"Drag Coefficient with Power Off\",\n",
+    "    \"linear\",\n",
+    "    \"constant\",\n",
+    ")\n",
+    "bella_lui.power_on_drag = Function(\n",
+    "    [\n",
+    "        (0.01, 0.51),\n",
+    "        (0.02, 0.46),\n",
+    "        (0.04, 0.43),\n",
+    "        (0.28, 0.43),\n",
+    "        (0.29, 0.44),\n",
+    "        (0.45, 0.44),\n",
+    "        (0.49, 0.46),\n",
+    "    ],\n",
+    "    \"Mach Number\",\n",
+    "    \"Drag Coefficient with Power On\",\n",
+    "    \"linear\",\n",
+    "    \"constant\",\n",
+    ")\n",
+    "bella_lui.power_off_drag *= parameters.get(\"power_off_drag\")[0]\n",
+    "bella_lui.power_on_drag *= parameters.get(\"power_on_drag\")[0]\n",
+    "\n",
+    "bella_lui.info()\n",
+    "\n",
+    "# Add parachute for landing\n",
+    "def drogue_trigger(p, h, y):\n",
+    "    # Deploy drogue when vertical velocity is negative (descending)\n",
+    "    return True if y[5] < 0 else False\n",
+    "\n",
+    "bella_lui.add_parachute(\n",
+    "    name=\"Drogue\",\n",
+    "    cd_s=np.pi / 4,  # CdS = pi/4 m²\n",
+    "    trigger=drogue_trigger,\n",
+    "    sampling_rate=105,\n",
+    "    lag=1.0,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dcc6f352",
+   "metadata": {},
+   "source": [
+    "## Create Point Mass Rocket for 3-DOF Simulations\n",
+    "\n",
+    "For 3-DOF simulations, we use `PointMassRocket` and `PointMassMotor` which are simplified models that don't require full inertia and aerodynamic surface definitions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "7036d5c3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.196683Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.196471Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.213056Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.212302Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Point Mass Rocket mass (without motor): 17.227 kg\n",
+      "Point Mass Rocket radius: 0.078 m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Parachute Drogue (cd_s = 0.7854 m2, trigger = <function drogue_trigger_3dof at 0x7f938f27fba0>)>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a PointMassMotor with similar characteristics to the K828FJ\n",
+    "point_mass_motor = PointMassMotor(\n",
+    "    thrust_source=\"../../data/motors/aerotech/AeroTech_K828FJ.eng\",\n",
+    "    dry_mass=1.0,\n",
+    "    propellant_initial_mass=1.373,  # propellant mass\n",
+    ")\n",
+    "\n",
+    "# Create a PointMassRocket with similar properties to Bella Lui\n",
+    "point_mass_rocket = PointMassRocket(\n",
+    "    radius=parameters.get(\"radius\")[0],\n",
+    "    mass=parameters.get(\"rocket_mass\")[0],\n",
+    "    center_of_mass_without_motor=0,\n",
+    "    power_off_drag=0.43,\n",
+    "    power_on_drag=0.43,\n",
+    ")\n",
+    "point_mass_rocket.add_motor(point_mass_motor, parameters.get(\"distance_rocket_nozzle\")[0])\n",
+    "\n",
+    "print(f\"Point Mass Rocket mass (without motor): {point_mass_rocket.mass} kg\")\n",
+    "print(f\"Point Mass Rocket radius: {point_mass_rocket.radius} m\")\n",
+    "\n",
+    "# Add parachute for landing (same as 6-DOF)\n",
+    "def drogue_trigger_3dof(p, h, y):\n",
+    "    # Deploy drogue when vertical velocity is negative (descending)\n",
+    "    return True if y[5] < 0 else False\n",
+    "\n",
+    "point_mass_rocket.add_parachute(\n",
+    "    name=\"Drogue\",\n",
+    "    cd_s=np.pi / 4,  # CdS = pi/4 m²\n",
+    "    trigger=drogue_trigger_3dof,\n",
+    "    sampling_rate=105,\n",
+    "    lag=1.0,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de018d06",
+   "metadata": {},
+   "source": [
+    "## Run Flight Simulations\n",
+    "\n",
+    "Now we run four different flight simulations to compare 6-DOF and 3-DOF modes with different weathercocking coefficients."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7822b89d",
+   "metadata": {},
+   "source": [
+    "### 6-DOF Flight Simulation (Reference)\n",
+    "\n",
+    "This is the full 6-DOF simulation that serves as our reference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3f1d6acd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.214830Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.214662Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.469085Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.467978Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6-DOF Apogee: 460.91 m AGL\n",
+      "6-DOF Apogee Time: 10.61 s\n",
+      "6-DOF Simulation Runtime: 0.250 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "\n",
+    "start_time = time.time()\n",
+    "flight_6dof = Flight(\n",
+    "    rocket=bella_lui,\n",
+    "    environment=env,\n",
+    "    rail_length=parameters.get(\"rail_length\")[0],\n",
+    "    inclination=parameters.get(\"inclination\")[0],\n",
+    "    heading=parameters.get(\"heading\")[0],\n",
+    "    terminate_on_apogee=False,\n",
+    ")\n",
+    "time_6dof = time.time() - start_time\n",
+    "\n",
+    "print(f\"6-DOF Apogee: {flight_6dof.apogee - env.elevation:.2f} m AGL\")\n",
+    "print(f\"6-DOF Apogee Time: {flight_6dof.apogee_time:.2f} s\")\n",
+    "print(f\"6-DOF Simulation Runtime: {time_6dof:.3f} s\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d1dcdf6",
+   "metadata": {},
+   "source": [
+    "### 3-DOF Flight (No Weathercocking, wc=0)\n",
+    "\n",
+    "Using `PointMassRocket` and `PointMassMotor` for the 3-DOF simulation with fixed attitude mode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "40c0ca4d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.470862Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.470675Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.509940Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.509185Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3-DOF (wc=0) Apogee: 448.24 m AGL\n",
+      "3-DOF (wc=0) Apogee Time: 10.49 s\n",
+      "3-DOF (wc=0) Simulation Runtime: 0.035 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = time.time()\n",
+    "flight_3dof_fixed = Flight(\n",
+    "    rocket=point_mass_rocket,\n",
+    "    environment=env,\n",
+    "    rail_length=parameters.get(\"rail_length\")[0],\n",
+    "    inclination=parameters.get(\"inclination\")[0],\n",
+    "    heading=parameters.get(\"heading\")[0],\n",
+    "    terminate_on_apogee=False,\n",
+    "    simulation_mode=\"3 DOF\",\n",
+    "    weathercock_coeff=0.0,\n",
+    ")\n",
+    "time_3dof_fixed = time.time() - start_time\n",
+    "\n",
+    "print(f\"3-DOF (wc=0) Apogee: {flight_3dof_fixed.apogee - env.elevation:.2f} m AGL\")\n",
+    "print(f\"3-DOF (wc=0) Apogee Time: {flight_3dof_fixed.apogee_time:.2f} s\")\n",
+    "print(f\"3-DOF (wc=0) Simulation Runtime: {time_3dof_fixed:.3f} s\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ad9f12f",
+   "metadata": {},
+   "source": [
+    "### 3-DOF Flight (Default Weathercocking, wc=1)\n",
+    "\n",
+    "Using `PointMassRocket` and `PointMassMotor` with default weathercocking - moderate alignment toward the relative wind."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e2c13b7b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.511706Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.511504Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.565330Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.564561Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3-DOF (wc=1) Apogee: 447.90 m AGL\n",
+      "3-DOF (wc=1) Apogee Time: 10.49 s\n",
+      "3-DOF (wc=1) Simulation Runtime: 0.050 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = time.time()\n",
+    "flight_3dof_wc1 = Flight(\n",
+    "    rocket=point_mass_rocket,\n",
+    "    environment=env,\n",
+    "    rail_length=parameters.get(\"rail_length\")[0],\n",
+    "    inclination=parameters.get(\"inclination\")[0],\n",
+    "    heading=parameters.get(\"heading\")[0],\n",
+    "    terminate_on_apogee=False,\n",
+    "    simulation_mode=\"3 DOF\",\n",
+    "    weathercock_coeff=1.0,\n",
+    ")\n",
+    "time_3dof_wc1 = time.time() - start_time\n",
+    "\n",
+    "print(f\"3-DOF (wc=1) Apogee: {flight_3dof_wc1.apogee - env.elevation:.2f} m AGL\")\n",
+    "print(f\"3-DOF (wc=1) Apogee Time: {flight_3dof_wc1.apogee_time:.2f} s\")\n",
+    "print(f\"3-DOF (wc=1) Simulation Runtime: {time_3dof_wc1:.3f} s\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4fe0e13",
+   "metadata": {},
+   "source": [
+    "### 3-DOF Flight (High Weathercocking, wc=5)\n",
+    "\n",
+    "Using `PointMassRocket` and `PointMassMotor` with high weathercocking - faster alignment toward the relative wind."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "ee3cbf2b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.567146Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.566967Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.627368Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.626658Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3-DOF (wc=5) Apogee: 447.61 m AGL\n",
+      "3-DOF (wc=5) Apogee Time: 10.48 s\n",
+      "3-DOF (wc=5) Simulation Runtime: 0.056 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "start_time = time.time()\n",
+    "flight_3dof_wc5 = Flight(\n",
+    "    rocket=point_mass_rocket,\n",
+    "    environment=env,\n",
+    "    rail_length=parameters.get(\"rail_length\")[0],\n",
+    "    inclination=parameters.get(\"inclination\")[0],\n",
+    "    heading=parameters.get(\"heading\")[0],\n",
+    "    terminate_on_apogee=False,\n",
+    "    simulation_mode=\"3 DOF\",\n",
+    "    weathercock_coeff=5.0,\n",
+    ")\n",
+    "time_3dof_wc5 = time.time() - start_time\n",
+    "\n",
+    "print(f\"3-DOF (wc=5) Apogee: {flight_3dof_wc5.apogee - env.elevation:.2f} m AGL\")\n",
+    "print(f\"3-DOF (wc=5) Apogee Time: {flight_3dof_wc5.apogee_time:.2f} s\")\n",
+    "print(f\"3-DOF (wc=5) Simulation Runtime: {time_3dof_wc5:.3f} s\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a4c92ff",
+   "metadata": {},
+   "source": [
+    "## Results Comparison\n",
+    "\n",
+    "### Summary Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "96b61b24",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.629178Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.629004Z",
+     "iopub.status.idle": "2025-11-27T11:34:57.716007Z",
+     "shell.execute_reply": "2025-11-27T11:34:57.715211Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "================================================================================\n",
+      "SIMULATION RESULTS COMPARISON\n",
+      "================================================================================\n",
+      "\n",
+      "Parameter                                     6-DOF 3DOF(wc=0) 3DOF(wc=1) 3DOF(wc=5)\n",
+      "--------------------------------------------------------------------------------\n",
+      "Apogee (m AGL)                               460.91     448.24     447.90     447.61\n",
+      "Apogee Time (s)                               10.61      10.49      10.49      10.48\n",
+      "Max Speed (m/s)                               86.24      84.78      84.72      84.71\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Max Acceleration (m/s²)                       58.47        N/A        N/A        N/A\n",
+      "  (Note: Max acceleration not yet available for 3-DOF with parachute)\n",
+      "Impact X (m)                                   5.95      30.32      18.82       2.45\n",
+      "Impact Y (m)                                   1.62      38.52      20.74      -4.76\n",
+      "Simulation Runtime (s)                        0.250      0.035      0.050      0.056\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "PERFORMANCE COMPARISON:\n",
+      "--------------------------------------------------------------------------------\n",
+      "Speedup vs 6-DOF                                  -        7.1x        5.0x        4.4x\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "PERCENTAGE DIFFERENCE FROM 6-DOF REFERENCE:\n",
+      "--------------------------------------------------------------------------------\n",
+      "Apogee Difference                                 -      -1.46%      -1.50%      -1.53%\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"=\" * 80)\n",
+    "print(\"SIMULATION RESULTS COMPARISON\")\n",
+    "print(\"=\" * 80)\n",
+    "\n",
+    "print(\"\\n{:<40} {:>10} {:>10} {:>10} {:>10}\".format(\n",
+    "    \"Parameter\", \"6-DOF\", \"3DOF(wc=0)\", \"3DOF(wc=1)\", \"3DOF(wc=5)\"\n",
+    "))\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "    \"Apogee (m AGL)\",\n",
+    "    flight_6dof.apogee - env.elevation,\n",
+    "    flight_3dof_fixed.apogee - env.elevation,\n",
+    "    flight_3dof_wc1.apogee - env.elevation,\n",
+    "    flight_3dof_wc5.apogee - env.elevation,\n",
+    "))\n",
+    "\n",
+    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "    \"Apogee Time (s)\",\n",
+    "    flight_6dof.apogee_time,\n",
+    "    flight_3dof_fixed.apogee_time,\n",
+    "    flight_3dof_wc1.apogee_time,\n",
+    "    flight_3dof_wc5.apogee_time,\n",
+    "))\n",
+    "\n",
+    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "    \"Max Speed (m/s)\",\n",
+    "    flight_6dof.max_speed,\n",
+    "    flight_3dof_fixed.max_speed,\n",
+    "    flight_3dof_wc1.max_speed,\n",
+    "    flight_3dof_wc5.max_speed,\n",
+    "))\n",
+    "\n",
+    "# Max acceleration only available for 6-DOF with parachute descent\n",
+    "print(\"{:<40} {:>10.2f} {:>10} {:>10} {:>10}\".format(\n",
+    "    \"Max Acceleration (m/s²)\",\n",
+    "    flight_6dof.max_acceleration,\n",
+    "    \"N/A\",\n",
+    "    \"N/A\",\n",
+    "    \"N/A\",\n",
+    "))\n",
+    "print(\"  (Note: Max acceleration not yet available for 3-DOF with parachute)\")\n",
+    "\n",
+    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "    \"Impact X (m)\",\n",
+    "    flight_6dof.x_impact,\n",
+    "    flight_3dof_fixed.x_impact,\n",
+    "    flight_3dof_wc1.x_impact,\n",
+    "    flight_3dof_wc5.x_impact,\n",
+    "))\n",
+    "\n",
+    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "    \"Impact Y (m)\",\n",
+    "    flight_6dof.y_impact,\n",
+    "    flight_3dof_fixed.y_impact,\n",
+    "    flight_3dof_wc1.y_impact,\n",
+    "    flight_3dof_wc5.y_impact,\n",
+    "))\n",
+    "\n",
+    "print(\"{:<40} {:>10.3f} {:>10.3f} {:>10.3f} {:>10.3f}\".format(\n",
+    "    \"Simulation Runtime (s)\",\n",
+    "    time_6dof,\n",
+    "    time_3dof_fixed,\n",
+    "    time_3dof_wc1,\n",
+    "    time_3dof_wc5,\n",
+    "))\n",
+    "\n",
+    "# Performance comparison\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"PERFORMANCE COMPARISON:\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "speedup_fixed = time_6dof / time_3dof_fixed if time_3dof_fixed > 0 else 0\n",
+    "speedup_wc1 = time_6dof / time_3dof_wc1 if time_3dof_wc1 > 0 else 0\n",
+    "speedup_wc5 = time_6dof / time_3dof_wc5 if time_3dof_wc5 > 0 else 0\n",
+    "\n",
+    "print(\"{:<40} {:>10} {:>10.1f}x {:>10.1f}x {:>10.1f}x\".format(\n",
+    "    \"Speedup vs 6-DOF\", \"-\", speedup_fixed, speedup_wc1, speedup_wc5\n",
+    "))\n",
+    "\n",
+    "# Percentage differences\n",
+    "print(\"\\n\" + \"-\" * 80)\n",
+    "print(\"PERCENTAGE DIFFERENCE FROM 6-DOF REFERENCE:\")\n",
+    "print(\"-\" * 80)\n",
+    "\n",
+    "apogee_diff_fixed = (flight_3dof_fixed.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    "apogee_diff_wc1 = (flight_3dof_wc1.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    "apogee_diff_wc5 = (flight_3dof_wc5.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    "\n",
+    "print(\"{:<40} {:>10} {:>10.2f}% {:>10.2f}% {:>10.2f}%\".format(\n",
+    "    \"Apogee Difference\", \"-\", apogee_diff_fixed, apogee_diff_wc1, apogee_diff_wc5\n",
+    "))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9af958c4",
+   "metadata": {},
+   "source": [
+    "## Comparison Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9e97cc02",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-11-27T11:34:57.717862Z",
+     "iopub.status.busy": "2025-11-27T11:34:57.717686Z",
+     "iopub.status.idle": "2025-11-27T11:34:58.262675Z",
+     "shell.execute_reply": "2025-11-27T11:34:58.261762Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
+    "\n",
+    "# Plot 1: Altitude vs Time\n",
+    "ax1 = axes[0, 0]\n",
+    "ax1.plot(flight_6dof.z[:, 0], flight_6dof.z[:, 1] - env.elevation, label=\"6-DOF\", linewidth=2)\n",
+    "ax1.plot(flight_3dof_fixed.z[:, 0], flight_3dof_fixed.z[:, 1] - env.elevation, label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
+    "ax1.plot(flight_3dof_wc1.z[:, 0], flight_3dof_wc1.z[:, 1] - env.elevation, label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
+    "ax1.plot(flight_3dof_wc5.z[:, 0], flight_3dof_wc5.z[:, 1] - env.elevation, label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax1.set_xlabel(\"Time (s)\")\n",
+    "ax1.set_ylabel(\"Altitude AGL (m)\")\n",
+    "ax1.set_title(\"Altitude vs Time Comparison\")\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 2: Speed vs Time\n",
+    "ax2 = axes[0, 1]\n",
+    "ax2.plot(flight_6dof.speed[:, 0], flight_6dof.speed[:, 1], label=\"6-DOF\", linewidth=2)\n",
+    "ax2.plot(flight_3dof_fixed.speed[:, 0], flight_3dof_fixed.speed[:, 1], label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
+    "ax2.plot(flight_3dof_wc1.speed[:, 0], flight_3dof_wc1.speed[:, 1], label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
+    "ax2.plot(flight_3dof_wc5.speed[:, 0], flight_3dof_wc5.speed[:, 1], label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax2.set_xlabel(\"Time (s)\")\n",
+    "ax2.set_ylabel(\"Speed (m/s)\")\n",
+    "ax2.set_title(\"Speed vs Time Comparison\")\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Plot 3: Horizontal Trajectory (X-Y)\n",
+    "ax3 = axes[1, 0]\n",
+    "ax3.plot(flight_6dof.x[:, 1], flight_6dof.y[:, 1], label=\"6-DOF\", linewidth=2)\n",
+    "ax3.plot(flight_3dof_fixed.x[:, 1], flight_3dof_fixed.y[:, 1], label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
+    "ax3.plot(flight_3dof_wc1.x[:, 1], flight_3dof_wc1.y[:, 1], label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
+    "ax3.plot(flight_3dof_wc5.x[:, 1], flight_3dof_wc5.y[:, 1], label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax3.set_xlabel(\"X Position (m)\")\n",
+    "ax3.set_ylabel(\"Y Position (m)\")\n",
+    "ax3.set_title(\"Horizontal Trajectory Comparison\")\n",
+    "ax3.legend()\n",
+    "ax3.grid(True, alpha=0.3)\n",
+    "ax3.set_aspect(\"equal\")\n",
+    "\n",
+    "# Plot 4: 3D Trajectory\n",
+    "ax4 = fig.add_subplot(2, 2, 4, projection=\"3d\")\n",
+    "ax4.plot(flight_6dof.x[:, 1], flight_6dof.y[:, 1], flight_6dof.z[:, 1] - env.elevation, label=\"6-DOF\", linewidth=2)\n",
+    "ax4.plot(flight_3dof_fixed.x[:, 1], flight_3dof_fixed.y[:, 1], flight_3dof_fixed.z[:, 1] - env.elevation, label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
+    "ax4.plot(flight_3dof_wc1.x[:, 1], flight_3dof_wc1.y[:, 1], flight_3dof_wc1.z[:, 1] - env.elevation, label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
+    "ax4.plot(flight_3dof_wc5.x[:, 1], flight_3dof_wc5.y[:, 1], flight_3dof_wc5.z[:, 1] - env.elevation, label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax4.set_xlabel(\"X (m)\")\n",
+    "ax4.set_ylabel(\"Y (m)\")\n",
+    "ax4.set_zlabel(\"Altitude AGL (m)\")\n",
+    "ax4.set_title(\"3D Trajectory Comparison\")\n",
+    "ax4.legend()\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e0b4327",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook demonstrates the differences between 6-DOF and 3-DOF simulation modes:\n",
+    "\n",
+    "### Simulation Modes\n",
+    "\n",
+    "1. **6-DOF (Full Dynamics)**:\n",
+    "   - Full rotational and translational dynamics\n",
+    "   - Quaternions evolve based on angular momentum conservation\n",
+    "   - Most accurate but computationally expensive\n",
+    "\n",
+    "2. **3-DOF with weathercock_coeff=0 (Fixed Attitude)**:\n",
+    "   - Only translational dynamics\n",
+    "   - Attitude remains fixed (no quaternion evolution)\n",
+    "   - Fastest but may not capture lateral motion accurately\n",
+    "\n",
+    "3. **3-DOF with weathercock_coeff=1 (Default Weathercocking)**:\n",
+    "   - Translational dynamics with quasi-static attitude evolution\n",
+    "   - Body axis aligns toward relative wind direction\n",
+    "   - Good balance between accuracy and speed\n",
+    "\n",
+    "4. **3-DOF with weathercock_coeff=5 (High Weathercocking)**:\n",
+    "   - Faster alignment toward relative wind\n",
+    "   - Useful when rocket is expected to quickly align with velocity\n",
+    "\n",
+    "### Key Observations\n",
+    "\n",
+    "- The weathercocking model helps the 3-DOF simulation better approximate the 6-DOF behavior by allowing the attitude to evolve\n",
+    "- Higher `weathercock_coeff` values result in faster alignment with the wind\n",
+    "- The 3-DOF mode is significantly faster and suitable for Monte Carlo simulations where many runs are needed"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 3e4423f3d9137efa1d6f0bf2890eba500dcee5f4 Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Thu, 27 Nov 2025 18:30:07 +0530
Subject: [PATCH 02/24] ENH: addition of weathercocking model to flight.py

- ENH: introduced new weathercock_coeff parameter in Flight.init (default: 1.0)

- ENH: updated u_dot_generalized_3dof to compute quaternion derivatives proportional to misalignment with relative wind

- ENH: angular velocity = weathercock_coeff * sin(misalignment_angle)
---
 rocketpy/simulation/flight.py | 77 ++++++++++++++++++++++++++++++++++-
 1 file changed, 75 insertions(+), 2 deletions(-)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 30ea66466..12493a020 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -492,6 +492,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         equations_of_motion="standard",
         ode_solver="LSODA",
         simulation_mode="6 DOF",
+        weathercock_coeff=1.0,
     ):
         """Run a trajectory simulation.
 
@@ -575,6 +576,13 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
             A custom ``scipy.integrate.OdeSolver`` can be passed as well.
             For more information on the integration methods, see the scipy
             documentation [1]_.
+        weathercock_coeff : float, optional
+            Coefficient that controls the rate at which the rocket's body axis
+            aligns with the relative wind direction in 3-DOF simulations.
+            A higher value means faster alignment (quasi-static weathercocking).
+            This parameter is only used when simulation_mode is '3 DOF'.
+            Default is 1.0, which provides moderate alignment. Set to 0 to
+            disable weathercocking (fixed attitude).
 
 
         Returns
@@ -606,7 +614,8 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         self.equations_of_motion = equations_of_motion
         self.simulation_mode = simulation_mode
         self.ode_solver = ode_solver
-
+        self.weathercock_coeff = weathercock_coeff
+        
         # Controller initialization
         self.__init_controllers()
 
@@ -1793,7 +1802,8 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
     def u_dot_generalized_3dof(self, t, u, post_processing=False):
         """Calculates derivative of u state vector with respect to time when the
         rocket is flying in 3 DOF motion in space and significant mass variation
-        effects exist.
+        effects exist.Includes a weathercocking model that evolves the body axis
+        direction toward the relative wind direction.
 
         Parameters
         ----------
@@ -1898,7 +1908,70 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
         e_dot = [0, 0, 0, 0]  # Euler derivatives unused in 3DOF
         w_dot = [0, 0, 0]  # No angular dynamics in 3DOF
         r_dot = [vx, vy, vz]
+        # Weathercocking: evolve body axis direction toward relative wind
+        # The body z-axis (attitude vector) should align with -freestream_velocity
+        if self.weathercock_coeff > 0 and free_stream_speed > 1e-6:
+            # Current body z-axis in inertial frame (attitude vector)
+            # From rotation matrix: column 3 gives the body z-axis in inertial frame
+            body_z_inertial = Vector(
+                [
+                    2 * (e1 * e3 + e0 * e2),
+                    2 * (e2 * e3 - e0 * e1),
+                    1 - 2 * (e1**2 + e2**2),
+                ]
+            )
+
+            # Desired direction: opposite of freestream velocity (into the wind)
+            # This is the direction the rocket nose should point
+            # Division by free_stream_speed ensures the result is a unit vector
+            desired_direction = -free_stream_velocity / free_stream_speed
+
+            # Compute rotation axis (cross product of current and desired)
+            rotation_axis = body_z_inertial ^ desired_direction
+            rotation_axis_mag = abs(rotation_axis)
+
+            if rotation_axis_mag > 1e-8:
+                # Normalize rotation axis
+                rotation_axis = rotation_axis / rotation_axis_mag
 
+                # The magnitude of the cross product of two unit vectors equals
+                # the sine of the angle between them
+                sin_angle = rotation_axis_mag
+
+                # Clamp sin_angle to valid range
+                sin_angle = min(1.0, max(-1.0, sin_angle))
+
+                # Angular velocity magnitude proportional to misalignment angle
+                # Using sin(angle) as approximation for small angles: sin(theta) ≈ theta
+                omega_mag = self.weathercock_coeff * sin_angle
+
+                # Angular velocity in inertial frame
+                omega_inertial = rotation_axis * omega_mag
+
+                # Transform angular velocity to body frame
+                omega_body = Kt @ omega_inertial
+
+                # Quaternion derivative from angular velocity in body frame
+                omega1_wc, omega2_wc, omega3_wc = (
+                    omega_body.x,
+                    omega_body.y,
+                    omega_body.z,
+                )
+                e0_dot = 0.5 * (-omega1_wc * e1 - omega2_wc * e2 - omega3_wc * e3)
+                e1_dot = 0.5 * (omega1_wc * e0 + omega3_wc * e2 - omega2_wc * e3)
+                e2_dot = 0.5 * (omega2_wc * e0 - omega3_wc * e1 + omega1_wc * e3)
+                e3_dot = 0.5 * (omega3_wc * e0 + omega2_wc * e1 - omega1_wc * e2)
+                e_dot = [e0_dot, e1_dot, e2_dot, e3_dot]
+                w_dot = [0, 0, 0]  # No angular acceleration in 3DOF model
+            else:
+                # Already aligned or anti-aligned
+                e_dot = [0, 0, 0, 0]
+                w_dot = [0, 0, 0]
+        else:
+            # No weathercocking or negligible freestream speed
+            e_dot = [0, 0, 0, 0]
+            w_dot = [0, 0, 0]
+            
         u_dot = [*r_dot, *v_dot, *e_dot, *w_dot]
 
         if post_processing:

From ef1d003e4751a75805f149550f377a78a85ed56e Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Thu, 27 Nov 2025 19:59:02 +0530
Subject: [PATCH 03/24] ENH: added tests for weathercocking to
 test_flight_3dof.py

- ENH: unit tests added for weathercocking to check whether weathercock_coeff=0 results in fixed attitude (no quaternion change).

- MNT: format and lint updates for new additions
---
 .../bella_lui_3dof_vs_6dof_comparison.ipynb   | 283 ++++++++++++------
 rocketpy/simulation/flight.py                 |   4 +-
 tests/conftest.py                             |   2 +-
 tests/unit/simulation/test_flight_3dof.py     | 127 ++++++++
 4 files changed, 329 insertions(+), 87 deletions(-)

diff --git a/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb b/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
index 21c6835c8..fdda90f3b 100644
--- a/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
+++ b/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
@@ -43,8 +43,8 @@
     "import numpy as np\n",
     "\n",
     "from rocketpy import Environment, Flight, Function, Rocket, SolidMotor\n",
-    "from rocketpy.rocket.point_mass_rocket import PointMassRocket\n",
-    "from rocketpy.motors.point_mass_motor import PointMassMotor"
+    "from rocketpy.motors.point_mass_motor import PointMassMotor\n",
+    "from rocketpy.rocket.point_mass_rocket import PointMassRocket"
    ]
   },
   {
@@ -459,18 +459,20 @@
     "\n",
     "bella_lui.info()\n",
     "\n",
+    "\n",
     "# Add parachute for landing\n",
     "def drogue_trigger(p, h, y):\n",
     "    # Deploy drogue when vertical velocity is negative (descending)\n",
     "    return True if y[5] < 0 else False\n",
     "\n",
+    "\n",
     "bella_lui.add_parachute(\n",
     "    name=\"Drogue\",\n",
     "    cd_s=np.pi / 4,  # CdS = pi/4 m²\n",
     "    trigger=drogue_trigger,\n",
     "    sampling_rate=105,\n",
     "    lag=1.0,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -531,23 +533,27 @@
     "    power_off_drag=0.43,\n",
     "    power_on_drag=0.43,\n",
     ")\n",
-    "point_mass_rocket.add_motor(point_mass_motor, parameters.get(\"distance_rocket_nozzle\")[0])\n",
+    "point_mass_rocket.add_motor(\n",
+    "    point_mass_motor, parameters.get(\"distance_rocket_nozzle\")[0]\n",
+    ")\n",
     "\n",
     "print(f\"Point Mass Rocket mass (without motor): {point_mass_rocket.mass} kg\")\n",
     "print(f\"Point Mass Rocket radius: {point_mass_rocket.radius} m\")\n",
     "\n",
+    "\n",
     "# Add parachute for landing (same as 6-DOF)\n",
     "def drogue_trigger_3dof(p, h, y):\n",
     "    # Deploy drogue when vertical velocity is negative (descending)\n",
     "    return True if y[5] < 0 else False\n",
     "\n",
+    "\n",
     "point_mass_rocket.add_parachute(\n",
     "    name=\"Drogue\",\n",
     "    cd_s=np.pi / 4,  # CdS = pi/4 m²\n",
     "    trigger=drogue_trigger_3dof,\n",
     "    sampling_rate=105,\n",
     "    lag=1.0,\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -609,7 +615,7 @@
     "\n",
     "print(f\"6-DOF Apogee: {flight_6dof.apogee - env.elevation:.2f} m AGL\")\n",
     "print(f\"6-DOF Apogee Time: {flight_6dof.apogee_time:.2f} s\")\n",
-    "print(f\"6-DOF Simulation Runtime: {time_6dof:.3f} s\")\n"
+    "print(f\"6-DOF Simulation Runtime: {time_6dof:.3f} s\")"
    ]
   },
   {
@@ -661,7 +667,7 @@
     "\n",
     "print(f\"3-DOF (wc=0) Apogee: {flight_3dof_fixed.apogee - env.elevation:.2f} m AGL\")\n",
     "print(f\"3-DOF (wc=0) Apogee Time: {flight_3dof_fixed.apogee_time:.2f} s\")\n",
-    "print(f\"3-DOF (wc=0) Simulation Runtime: {time_3dof_fixed:.3f} s\")\n"
+    "print(f\"3-DOF (wc=0) Simulation Runtime: {time_3dof_fixed:.3f} s\")"
    ]
   },
   {
@@ -713,7 +719,7 @@
     "\n",
     "print(f\"3-DOF (wc=1) Apogee: {flight_3dof_wc1.apogee - env.elevation:.2f} m AGL\")\n",
     "print(f\"3-DOF (wc=1) Apogee Time: {flight_3dof_wc1.apogee_time:.2f} s\")\n",
-    "print(f\"3-DOF (wc=1) Simulation Runtime: {time_3dof_wc1:.3f} s\")\n"
+    "print(f\"3-DOF (wc=1) Simulation Runtime: {time_3dof_wc1:.3f} s\")"
    ]
   },
   {
@@ -765,7 +771,7 @@
     "\n",
     "print(f\"3-DOF (wc=5) Apogee: {flight_3dof_wc5.apogee - env.elevation:.2f} m AGL\")\n",
     "print(f\"3-DOF (wc=5) Apogee Time: {flight_3dof_wc5.apogee_time:.2f} s\")\n",
-    "print(f\"3-DOF (wc=5) Simulation Runtime: {time_3dof_wc5:.3f} s\")\n"
+    "print(f\"3-DOF (wc=5) Simulation Runtime: {time_3dof_wc5:.3f} s\")"
    ]
   },
   {
@@ -833,68 +839,84 @@
     "print(\"SIMULATION RESULTS COMPARISON\")\n",
     "print(\"=\" * 80)\n",
     "\n",
-    "print(\"\\n{:<40} {:>10} {:>10} {:>10} {:>10}\".format(\n",
-    "    \"Parameter\", \"6-DOF\", \"3DOF(wc=0)\", \"3DOF(wc=1)\", \"3DOF(wc=5)\"\n",
-    "))\n",
+    "print(\n",
+    "    \"\\n{:<40} {:>10} {:>10} {:>10} {:>10}\".format(\n",
+    "        \"Parameter\", \"6-DOF\", \"3DOF(wc=0)\", \"3DOF(wc=1)\", \"3DOF(wc=5)\"\n",
+    "    )\n",
+    ")\n",
     "print(\"-\" * 80)\n",
     "\n",
-    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "    \"Apogee (m AGL)\",\n",
-    "    flight_6dof.apogee - env.elevation,\n",
-    "    flight_3dof_fixed.apogee - env.elevation,\n",
-    "    flight_3dof_wc1.apogee - env.elevation,\n",
-    "    flight_3dof_wc5.apogee - env.elevation,\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "        \"Apogee (m AGL)\",\n",
+    "        flight_6dof.apogee - env.elevation,\n",
+    "        flight_3dof_fixed.apogee - env.elevation,\n",
+    "        flight_3dof_wc1.apogee - env.elevation,\n",
+    "        flight_3dof_wc5.apogee - env.elevation,\n",
+    "    )\n",
+    ")\n",
     "\n",
-    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "    \"Apogee Time (s)\",\n",
-    "    flight_6dof.apogee_time,\n",
-    "    flight_3dof_fixed.apogee_time,\n",
-    "    flight_3dof_wc1.apogee_time,\n",
-    "    flight_3dof_wc5.apogee_time,\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "        \"Apogee Time (s)\",\n",
+    "        flight_6dof.apogee_time,\n",
+    "        flight_3dof_fixed.apogee_time,\n",
+    "        flight_3dof_wc1.apogee_time,\n",
+    "        flight_3dof_wc5.apogee_time,\n",
+    "    )\n",
+    ")\n",
     "\n",
-    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "    \"Max Speed (m/s)\",\n",
-    "    flight_6dof.max_speed,\n",
-    "    flight_3dof_fixed.max_speed,\n",
-    "    flight_3dof_wc1.max_speed,\n",
-    "    flight_3dof_wc5.max_speed,\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "        \"Max Speed (m/s)\",\n",
+    "        flight_6dof.max_speed,\n",
+    "        flight_3dof_fixed.max_speed,\n",
+    "        flight_3dof_wc1.max_speed,\n",
+    "        flight_3dof_wc5.max_speed,\n",
+    "    )\n",
+    ")\n",
     "\n",
     "# Max acceleration only available for 6-DOF with parachute descent\n",
-    "print(\"{:<40} {:>10.2f} {:>10} {:>10} {:>10}\".format(\n",
-    "    \"Max Acceleration (m/s²)\",\n",
-    "    flight_6dof.max_acceleration,\n",
-    "    \"N/A\",\n",
-    "    \"N/A\",\n",
-    "    \"N/A\",\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.2f} {:>10} {:>10} {:>10}\".format(\n",
+    "        \"Max Acceleration (m/s²)\",\n",
+    "        flight_6dof.max_acceleration,\n",
+    "        \"N/A\",\n",
+    "        \"N/A\",\n",
+    "        \"N/A\",\n",
+    "    )\n",
+    ")\n",
     "print(\"  (Note: Max acceleration not yet available for 3-DOF with parachute)\")\n",
     "\n",
-    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "    \"Impact X (m)\",\n",
-    "    flight_6dof.x_impact,\n",
-    "    flight_3dof_fixed.x_impact,\n",
-    "    flight_3dof_wc1.x_impact,\n",
-    "    flight_3dof_wc5.x_impact,\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "        \"Impact X (m)\",\n",
+    "        flight_6dof.x_impact,\n",
+    "        flight_3dof_fixed.x_impact,\n",
+    "        flight_3dof_wc1.x_impact,\n",
+    "        flight_3dof_wc5.x_impact,\n",
+    "    )\n",
+    ")\n",
     "\n",
-    "print(\"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "    \"Impact Y (m)\",\n",
-    "    flight_6dof.y_impact,\n",
-    "    flight_3dof_fixed.y_impact,\n",
-    "    flight_3dof_wc1.y_impact,\n",
-    "    flight_3dof_wc5.y_impact,\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
+    "        \"Impact Y (m)\",\n",
+    "        flight_6dof.y_impact,\n",
+    "        flight_3dof_fixed.y_impact,\n",
+    "        flight_3dof_wc1.y_impact,\n",
+    "        flight_3dof_wc5.y_impact,\n",
+    "    )\n",
+    ")\n",
     "\n",
-    "print(\"{:<40} {:>10.3f} {:>10.3f} {:>10.3f} {:>10.3f}\".format(\n",
-    "    \"Simulation Runtime (s)\",\n",
-    "    time_6dof,\n",
-    "    time_3dof_fixed,\n",
-    "    time_3dof_wc1,\n",
-    "    time_3dof_wc5,\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10.3f} {:>10.3f} {:>10.3f} {:>10.3f}\".format(\n",
+    "        \"Simulation Runtime (s)\",\n",
+    "        time_6dof,\n",
+    "        time_3dof_fixed,\n",
+    "        time_3dof_wc1,\n",
+    "        time_3dof_wc5,\n",
+    "    )\n",
+    ")\n",
     "\n",
     "# Performance comparison\n",
     "print(\"\\n\" + \"-\" * 80)\n",
@@ -905,22 +927,32 @@
     "speedup_wc1 = time_6dof / time_3dof_wc1 if time_3dof_wc1 > 0 else 0\n",
     "speedup_wc5 = time_6dof / time_3dof_wc5 if time_3dof_wc5 > 0 else 0\n",
     "\n",
-    "print(\"{:<40} {:>10} {:>10.1f}x {:>10.1f}x {:>10.1f}x\".format(\n",
-    "    \"Speedup vs 6-DOF\", \"-\", speedup_fixed, speedup_wc1, speedup_wc5\n",
-    "))\n",
+    "print(\n",
+    "    \"{:<40} {:>10} {:>10.1f}x {:>10.1f}x {:>10.1f}x\".format(\n",
+    "        \"Speedup vs 6-DOF\", \"-\", speedup_fixed, speedup_wc1, speedup_wc5\n",
+    "    )\n",
+    ")\n",
     "\n",
     "# Percentage differences\n",
     "print(\"\\n\" + \"-\" * 80)\n",
     "print(\"PERCENTAGE DIFFERENCE FROM 6-DOF REFERENCE:\")\n",
     "print(\"-\" * 80)\n",
     "\n",
-    "apogee_diff_fixed = (flight_3dof_fixed.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
-    "apogee_diff_wc1 = (flight_3dof_wc1.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
-    "apogee_diff_wc5 = (flight_3dof_wc5.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    "apogee_diff_fixed = (\n",
+    "    (flight_3dof_fixed.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    ")\n",
+    "apogee_diff_wc1 = (\n",
+    "    (flight_3dof_wc1.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    ")\n",
+    "apogee_diff_wc5 = (\n",
+    "    (flight_3dof_wc5.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
+    ")\n",
     "\n",
-    "print(\"{:<40} {:>10} {:>10.2f}% {:>10.2f}% {:>10.2f}%\".format(\n",
-    "    \"Apogee Difference\", \"-\", apogee_diff_fixed, apogee_diff_wc1, apogee_diff_wc5\n",
-    "))\n"
+    "print(\n",
+    "    \"{:<40} {:>10} {:>10.2f}% {:>10.2f}% {:>10.2f}%\".format(\n",
+    "        \"Apogee Difference\", \"-\", apogee_diff_fixed, apogee_diff_wc1, apogee_diff_wc5\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -960,10 +992,30 @@
     "\n",
     "# Plot 1: Altitude vs Time\n",
     "ax1 = axes[0, 0]\n",
-    "ax1.plot(flight_6dof.z[:, 0], flight_6dof.z[:, 1] - env.elevation, label=\"6-DOF\", linewidth=2)\n",
-    "ax1.plot(flight_3dof_fixed.z[:, 0], flight_3dof_fixed.z[:, 1] - env.elevation, label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
-    "ax1.plot(flight_3dof_wc1.z[:, 0], flight_3dof_wc1.z[:, 1] - env.elevation, label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
-    "ax1.plot(flight_3dof_wc5.z[:, 0], flight_3dof_wc5.z[:, 1] - env.elevation, label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax1.plot(\n",
+    "    flight_6dof.z[:, 0], flight_6dof.z[:, 1] - env.elevation, label=\"6-DOF\", linewidth=2\n",
+    ")\n",
+    "ax1.plot(\n",
+    "    flight_3dof_fixed.z[:, 0],\n",
+    "    flight_3dof_fixed.z[:, 1] - env.elevation,\n",
+    "    label=\"3-DOF (wc=0)\",\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax1.plot(\n",
+    "    flight_3dof_wc1.z[:, 0],\n",
+    "    flight_3dof_wc1.z[:, 1] - env.elevation,\n",
+    "    label=\"3-DOF (wc=1)\",\n",
+    "    linestyle=\"-.\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax1.plot(\n",
+    "    flight_3dof_wc5.z[:, 0],\n",
+    "    flight_3dof_wc5.z[:, 1] - env.elevation,\n",
+    "    label=\"3-DOF (wc=5)\",\n",
+    "    linestyle=\":\",\n",
+    "    linewidth=2,\n",
+    ")\n",
     "ax1.set_xlabel(\"Time (s)\")\n",
     "ax1.set_ylabel(\"Altitude AGL (m)\")\n",
     "ax1.set_title(\"Altitude vs Time Comparison\")\n",
@@ -973,9 +1025,27 @@
     "# Plot 2: Speed vs Time\n",
     "ax2 = axes[0, 1]\n",
     "ax2.plot(flight_6dof.speed[:, 0], flight_6dof.speed[:, 1], label=\"6-DOF\", linewidth=2)\n",
-    "ax2.plot(flight_3dof_fixed.speed[:, 0], flight_3dof_fixed.speed[:, 1], label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
-    "ax2.plot(flight_3dof_wc1.speed[:, 0], flight_3dof_wc1.speed[:, 1], label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
-    "ax2.plot(flight_3dof_wc5.speed[:, 0], flight_3dof_wc5.speed[:, 1], label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax2.plot(\n",
+    "    flight_3dof_fixed.speed[:, 0],\n",
+    "    flight_3dof_fixed.speed[:, 1],\n",
+    "    label=\"3-DOF (wc=0)\",\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax2.plot(\n",
+    "    flight_3dof_wc1.speed[:, 0],\n",
+    "    flight_3dof_wc1.speed[:, 1],\n",
+    "    label=\"3-DOF (wc=1)\",\n",
+    "    linestyle=\"-.\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax2.plot(\n",
+    "    flight_3dof_wc5.speed[:, 0],\n",
+    "    flight_3dof_wc5.speed[:, 1],\n",
+    "    label=\"3-DOF (wc=5)\",\n",
+    "    linestyle=\":\",\n",
+    "    linewidth=2,\n",
+    ")\n",
     "ax2.set_xlabel(\"Time (s)\")\n",
     "ax2.set_ylabel(\"Speed (m/s)\")\n",
     "ax2.set_title(\"Speed vs Time Comparison\")\n",
@@ -985,9 +1055,27 @@
     "# Plot 3: Horizontal Trajectory (X-Y)\n",
     "ax3 = axes[1, 0]\n",
     "ax3.plot(flight_6dof.x[:, 1], flight_6dof.y[:, 1], label=\"6-DOF\", linewidth=2)\n",
-    "ax3.plot(flight_3dof_fixed.x[:, 1], flight_3dof_fixed.y[:, 1], label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
-    "ax3.plot(flight_3dof_wc1.x[:, 1], flight_3dof_wc1.y[:, 1], label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
-    "ax3.plot(flight_3dof_wc5.x[:, 1], flight_3dof_wc5.y[:, 1], label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax3.plot(\n",
+    "    flight_3dof_fixed.x[:, 1],\n",
+    "    flight_3dof_fixed.y[:, 1],\n",
+    "    label=\"3-DOF (wc=0)\",\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax3.plot(\n",
+    "    flight_3dof_wc1.x[:, 1],\n",
+    "    flight_3dof_wc1.y[:, 1],\n",
+    "    label=\"3-DOF (wc=1)\",\n",
+    "    linestyle=\"-.\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax3.plot(\n",
+    "    flight_3dof_wc5.x[:, 1],\n",
+    "    flight_3dof_wc5.y[:, 1],\n",
+    "    label=\"3-DOF (wc=5)\",\n",
+    "    linestyle=\":\",\n",
+    "    linewidth=2,\n",
+    ")\n",
     "ax3.set_xlabel(\"X Position (m)\")\n",
     "ax3.set_ylabel(\"Y Position (m)\")\n",
     "ax3.set_title(\"Horizontal Trajectory Comparison\")\n",
@@ -997,10 +1085,37 @@
     "\n",
     "# Plot 4: 3D Trajectory\n",
     "ax4 = fig.add_subplot(2, 2, 4, projection=\"3d\")\n",
-    "ax4.plot(flight_6dof.x[:, 1], flight_6dof.y[:, 1], flight_6dof.z[:, 1] - env.elevation, label=\"6-DOF\", linewidth=2)\n",
-    "ax4.plot(flight_3dof_fixed.x[:, 1], flight_3dof_fixed.y[:, 1], flight_3dof_fixed.z[:, 1] - env.elevation, label=\"3-DOF (wc=0)\", linestyle=\"--\", linewidth=2)\n",
-    "ax4.plot(flight_3dof_wc1.x[:, 1], flight_3dof_wc1.y[:, 1], flight_3dof_wc1.z[:, 1] - env.elevation, label=\"3-DOF (wc=1)\", linestyle=\"-.\", linewidth=2)\n",
-    "ax4.plot(flight_3dof_wc5.x[:, 1], flight_3dof_wc5.y[:, 1], flight_3dof_wc5.z[:, 1] - env.elevation, label=\"3-DOF (wc=5)\", linestyle=\":\", linewidth=2)\n",
+    "ax4.plot(\n",
+    "    flight_6dof.x[:, 1],\n",
+    "    flight_6dof.y[:, 1],\n",
+    "    flight_6dof.z[:, 1] - env.elevation,\n",
+    "    label=\"6-DOF\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    flight_3dof_fixed.x[:, 1],\n",
+    "    flight_3dof_fixed.y[:, 1],\n",
+    "    flight_3dof_fixed.z[:, 1] - env.elevation,\n",
+    "    label=\"3-DOF (wc=0)\",\n",
+    "    linestyle=\"--\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    flight_3dof_wc1.x[:, 1],\n",
+    "    flight_3dof_wc1.y[:, 1],\n",
+    "    flight_3dof_wc1.z[:, 1] - env.elevation,\n",
+    "    label=\"3-DOF (wc=1)\",\n",
+    "    linestyle=\"-.\",\n",
+    "    linewidth=2,\n",
+    ")\n",
+    "ax4.plot(\n",
+    "    flight_3dof_wc5.x[:, 1],\n",
+    "    flight_3dof_wc5.y[:, 1],\n",
+    "    flight_3dof_wc5.z[:, 1] - env.elevation,\n",
+    "    label=\"3-DOF (wc=5)\",\n",
+    "    linestyle=\":\",\n",
+    "    linewidth=2,\n",
+    ")\n",
     "ax4.set_xlabel(\"X (m)\")\n",
     "ax4.set_ylabel(\"Y (m)\")\n",
     "ax4.set_zlabel(\"Altitude AGL (m)\")\n",
diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 12493a020..08fc26768 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -615,7 +615,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         self.simulation_mode = simulation_mode
         self.ode_solver = ode_solver
         self.weathercock_coeff = weathercock_coeff
-        
+
         # Controller initialization
         self.__init_controllers()
 
@@ -1971,7 +1971,7 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
             # No weathercocking or negligible freestream speed
             e_dot = [0, 0, 0, 0]
             w_dot = [0, 0, 0]
-            
+
         u_dot = [*r_dot, *v_dot, *e_dot, *w_dot]
 
         if post_processing:
diff --git a/tests/conftest.py b/tests/conftest.py
index 12d07c334..456de43ca 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -1,6 +1,6 @@
+import matplotlib
 import netCDF4
 import numpy as np
-import matplotlib
 import pytest
 
 # Configure matplotlib to use non-interactive backend for tests
diff --git a/tests/unit/simulation/test_flight_3dof.py b/tests/unit/simulation/test_flight_3dof.py
index a44448baa..3bd79fa51 100644
--- a/tests/unit/simulation/test_flight_3dof.py
+++ b/tests/unit/simulation/test_flight_3dof.py
@@ -137,3 +137,130 @@ def test_invalid_simulation_mode(example_plain_env, calisto):
             rail_length=1,
             simulation_mode="2 DOF",
         )
+
+
+def test_weathercock_coeff_stored(example_plain_env, point_mass_rocket):
+    """Tests that the weathercock_coeff parameter is correctly stored.
+    Parameters
+    ----------
+    example_plain_env : rocketpy.Environment
+        A basic environment fixture for flight simulation.
+    point_mass_rocket : rocketpy.PointMassRocket
+        A point mass rocket fixture for 3-DOF simulation.
+    """
+    flight = Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=2.5,
+    )
+    assert flight.weathercock_coeff == 2.5
+
+
+def test_weathercock_coeff_default(example_plain_env, point_mass_rocket):
+    """Tests that the default weathercock_coeff is 1.0.
+    Parameters
+    ----------
+    example_plain_env : rocketpy.Environment
+        A basic environment fixture for flight simulation.
+    point_mass_rocket : rocketpy.PointMassRocket
+        A point mass rocket fixture for 3-DOF simulation.
+    """
+    flight = Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+    )
+    assert flight.weathercock_coeff == 1.0
+
+
+def test_weathercock_zero_gives_fixed_attitude(example_plain_env, point_mass_rocket):
+    """Tests that weathercock_coeff=0 results in fixed attitude (no quaternion change).
+    When weathercock_coeff is 0, the quaternion derivatives should be zero,
+    meaning the attitude does not evolve.
+    Parameters
+    ----------
+    example_plain_env : rocketpy.Environment
+        A basic environment fixture for flight simulation.
+    point_mass_rocket : rocketpy.PointMassRocket
+        A point mass rocket fixture for 3-DOF simulation.
+    """
+    flight = Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=0.0,
+    )
+    # Create a state vector with non-zero velocity (to have freestream)
+    # [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
+    u = [0, 0, 100, 10, 5, 50, 1, 0, 0, 0, 0, 0, 0]
+    result = flight.u_dot_generalized_3dof(0, u)
+
+    # Quaternion derivatives (indices 6-9) should be zero
+    e_dot = result[6:10]
+    assert all(abs(ed) < 1e-10 for ed in e_dot), "Quaternion derivatives should be zero"
+
+
+def test_weathercock_nonzero_evolves_attitude(example_plain_env, point_mass_rocket):
+    """Tests that non-zero weathercock_coeff causes attitude evolution.
+    When the body axis is misaligned with the relative wind and weathercock_coeff
+    is positive, the quaternion derivatives should be non-zero.
+    Parameters
+    ----------
+    example_plain_env : rocketpy.Environment
+        A basic environment fixture for flight simulation.
+    point_mass_rocket : rocketpy.PointMassRocket
+        A point mass rocket fixture for 3-DOF simulation.
+    """
+    flight = Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=1.0,
+    )
+    # Create a state with misaligned body axis
+    # Body pointing straight up (e0=1, e1=e2=e3=0) but velocity is horizontal
+    # [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
+    u = [0, 0, 100, 50, 0, 0, 1, 0, 0, 0, 0, 0, 0]
+    result = flight.u_dot_generalized_3dof(0, u)
+
+    # With misalignment, quaternion derivatives should be non-zero
+    e_dot = result[6:10]
+    e_dot_magnitude = sum(ed**2 for ed in e_dot) ** 0.5
+    assert e_dot_magnitude > 1e-6, "Quaternion derivatives should be non-zero"
+
+
+def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
+    """Tests that when body axis is aligned with relative wind, no rotation occurs.
+    When the rocket's body z-axis is already aligned with the negative of the
+    freestream velocity, the quaternion derivatives should be approximately zero.
+    Parameters
+    ----------
+    example_plain_env : rocketpy.Environment
+        A basic environment fixture for flight simulation.
+    point_mass_rocket : rocketpy.PointMassRocket
+        A point mass rocket fixture for 3-DOF simulation.
+    """
+    flight = Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=1.0,
+    )
+    # Body pointing in -x direction (into the wind for vx=50)
+    # Quaternion for 90 degree rotation about y-axis uses half-angle:
+    # e0=cos(90°/2)=cos(45°), e2=sin(90°/2)=sin(45°)
+    sqrt2_2 = np.sqrt(2) / 2
+    # [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
+    u = [0, 0, 100, 50, 0, 0, sqrt2_2, 0, sqrt2_2, 0, 0, 0, 0]
+    result = flight.u_dot_generalized_3dof(0, u)
+
+    # With alignment, quaternion derivatives should be very small
+    e_dot = result[6:10]
+    e_dot_magnitude = sum(ed**2 for ed in e_dot) ** 0.5
+    assert e_dot_magnitude < 0.1, "Quaternion derivatives should be small when aligned"

From 194943a58529b776b6516383917be86618730c8a Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Thu, 27 Nov 2025 21:14:14 +0530
Subject: [PATCH 04/24] DOC: updating 3 dof documentation and corresponding
 index.rst

- DOC: added 3 dof and 6 dof comparison analysis to three_dof_simulation.rst

- DOC: updated iusers/index.rst to build three_dof_simulation.rst

- MNT: deleted the bella_lui_3dof_vs_6dof_comparison.ipynb as the doc now covers this section
---
 .../bella_lui_3dof_vs_6dof_comparison.ipynb   | 1188 -----------------
 docs/user/index.rst                           |    2 +-
 docs/user/three_dof_simulation.rst            |  325 ++++-
 3 files changed, 318 insertions(+), 1197 deletions(-)
 delete mode 100644 docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb

diff --git a/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb b/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
deleted file mode 100644
index fdda90f3b..000000000
--- a/docs/examples/bella_lui_3dof_vs_6dof_comparison.ipynb
+++ /dev/null
@@ -1,1188 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "f98dfaf3",
-   "metadata": {},
-   "source": [
-    "# Bella Lui 3-DOF vs 6-DOF Flight Simulation Comparison\n",
-    "\n",
-    "This notebook demonstrates the differences between the 3-DOF and 6-DOF simulation modes using the Bella Lui rocket from EPFL Rocket Team. It compares the trajectory, apogee, and other flight parameters between both simulation modes, including the effect of the weathercocking model on 3-DOF simulations.\n",
-    "\n",
-    "**Permission to use flight data given by Antoine Scardigli, 2020**\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "The 3-DOF simulation mode with the weathercocking model allows for:\n",
-    "- Faster simulations compared to 6-DOF\n",
-    "- Evolving attitude that aligns with the relative wind direction\n",
-    "- Configurable alignment rate via the `weathercock_coeff` parameter\n",
-    "\n",
-    "This example compares:\n",
-    "1. **6-DOF**: Full rotational and translational dynamics (reference)\n",
-    "2. **3-DOF (wc=0)**: Fixed attitude, no quaternion evolution\n",
-    "3. **3-DOF (wc=1)**: Default weathercocking, moderate alignment rate\n",
-    "4. **3-DOF (wc=5)**: High weathercocking, faster alignment rate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "ea1e6c69",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:54.280159Z",
-     "iopub.status.busy": "2025-11-27T11:34:54.279959Z",
-     "iopub.status.idle": "2025-11-27T11:34:56.811688Z",
-     "shell.execute_reply": "2025-11-27T11:34:56.810918Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "from rocketpy import Environment, Flight, Function, Rocket, SolidMotor\n",
-    "from rocketpy.motors.point_mass_motor import PointMassMotor\n",
-    "from rocketpy.rocket.point_mass_rocket import PointMassRocket"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "80fe381c",
-   "metadata": {},
-   "source": [
-    "## Define Bella Lui Rocket Parameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "000025f1",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:56.814456Z",
-     "iopub.status.busy": "2025-11-27T11:34:56.814146Z",
-     "iopub.status.idle": "2025-11-27T11:34:56.819436Z",
-     "shell.execute_reply": "2025-11-27T11:34:56.818652Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "parameters = {\n",
-    "    # Mass Details\n",
-    "    \"rocket_mass\": (18.227 - 1, 0.010),  # 1.373 = propellant mass\n",
-    "    # propulsion details\n",
-    "    \"impulse\": (2157, 0.03 * 2157),\n",
-    "    \"burn_time\": (2.43, 0.1),\n",
-    "    \"nozzle_radius\": (44.45 / 1000, 0.001),\n",
-    "    \"throat_radius\": (21.4376 / 1000, 0.001),\n",
-    "    \"grain_separation\": (3 / 1000, 1 / 1000),\n",
-    "    \"grain_density\": (782.4, 30),\n",
-    "    \"grain_outer_radius\": (85.598 / 2000, 0.001),\n",
-    "    \"grain_initial_inner_radius\": (33.147 / 1000, 0.002),\n",
-    "    \"grain_initial_height\": (152.4 / 1000, 0.001),\n",
-    "    # Aerodynamic Details\n",
-    "    \"inertia_i\": (0.78267, 0.03 * 0.78267),\n",
-    "    \"inertia_z\": (0.064244, 0.03 * 0.064244),\n",
-    "    \"radius\": (156 / 2000, 0.001),\n",
-    "    \"distance_rocket_nozzle\": (-1.1356, 0.100),\n",
-    "    \"distance_rocket_propellant\": (-1, 0.100),\n",
-    "    \"power_off_drag\": (1, 0.05),\n",
-    "    \"power_on_drag\": (1, 0.05),\n",
-    "    \"nose_length\": (0.242, 0.001),\n",
-    "    \"nose_distance_to_cm\": (1.3, 0.100),\n",
-    "    \"fin_span\": (0.200, 0.001),\n",
-    "    \"fin_root_chord\": (0.280, 0.001),\n",
-    "    \"fin_tip_chord\": (0.125, 0.001),\n",
-    "    \"fin_distance_to_cm\": (-0.75, 0.100),\n",
-    "    \"tail_top_radius\": (156 / 2000, 0.001),\n",
-    "    \"tail_bottom_radius\": (135 / 2000, 0.001),\n",
-    "    \"tail_length\": (0.050, 0.001),\n",
-    "    \"tail_distance_to_cm\": (-1.0856, 0.001),\n",
-    "    # Launch and Environment Details\n",
-    "    \"wind_direction\": (0, 5),\n",
-    "    \"wind_speed\": (1, 0.05),\n",
-    "    \"inclination\": (89, 1),\n",
-    "    \"heading\": (45, 5),\n",
-    "    \"rail_length\": (4.2, 0.001),\n",
-    "    # Parachute Details\n",
-    "    \"CdS_drogue\": (np.pi / 4, 0.20 * np.pi / 4),\n",
-    "    \"lag_rec\": (1, 0.020),\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8c8dc3d4",
-   "metadata": {},
-   "source": [
-    "## Create Environment\n",
-    "\n",
-    "Set up the environment for the Bella Lui mission at Kaltbrunn, Switzerland."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "72bb4e4b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:56.821245Z",
-     "iopub.status.busy": "2025-11-27T11:34:56.821083Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.039301Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.038427Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Gravity Details\n",
-      "\n",
-      "Acceleration of gravity at surface level:    9.8100 m/s²\n",
-      "Acceleration of gravity at   2.000 km (ASL): 9.8100 m/s²\n",
-      "\n",
-      "\n",
-      "Launch Site Details\n",
-      "\n",
-      "Launch Date: 2020-02-22 13:00:00 UTC\n",
-      "Launch Site Latitude: 47.21348°\n",
-      "Launch Site Longitude: 9.00334°\n",
-      "Reference Datum: SIRGAS2000\n",
-      "Launch Site UTM coordinates: 500252.61 E    5228887.37 N\n",
-      "Launch Site UTM zone: 32T\n",
-      "Launch Site Surface Elevation: 407.0 m\n",
-      "\n",
-      "\n",
-      "Atmospheric Model Details\n",
-      "\n",
-      "Atmospheric Model Type: Reanalysis\n",
-      "Reanalysis Maximum Height: 2.000 km\n",
-      "Reanalysis Time Period: from 2020-02-22 00:00:00 to 2020-02-22 18:00:00 utc\n",
-      "Reanalysis Hour Interval: 4 hrs\n",
-      "Reanalysis Latitude Range: From 48.0° to 46.0°\n",
-      "Reanalysis Longitude Range: From 8.0° to 10.0°\n",
-      "\n",
-      "Surface Atmospheric Conditions\n",
-      "\n",
-      "Surface Wind Speed: 1.26 m/s\n",
-      "Surface Wind Direction: 213.21°\n",
-      "Surface Wind Heading: 33.21°\n",
-      "Surface Pressure: 980.43 hPa\n",
-      "Surface Temperature: 286.63 K\n",
-      "Surface Air Density: 1.192 kg/m³\n",
-      "Surface Speed of Sound: 339.39 m/s\n",
-      "\n",
-      "\n",
-      "Earth Model Details\n",
-      "\n",
-      "Earth Radius at Launch site: 6366.66 km\n",
-      "Semi-major Axis: 6378.14 km\n",
-      "Semi-minor Axis: 6356.75 km\n",
-      "Flattening: 0.0034\n",
-      "\n",
-      "\n",
-      "Atmospheric Model Plots\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 900x450 with 4 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "env = Environment(\n",
-    "    gravity=9.81,\n",
-    "    latitude=47.213476,\n",
-    "    longitude=9.003336,\n",
-    "    date=(2020, 2, 22, 13),\n",
-    "    elevation=407,\n",
-    ")\n",
-    "env.set_atmospheric_model(\n",
-    "    type=\"Reanalysis\",\n",
-    "    file=\"../../data/weather/bella_lui_weather_data_ERA5.nc\",\n",
-    "    dictionary=\"ECMWF\",\n",
-    ")\n",
-    "env.max_expected_height = 2000\n",
-    "env.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0fb238c",
-   "metadata": {},
-   "source": [
-    "## Create Motor\n",
-    "\n",
-    "Create the AeroTech K828FJ solid motor."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "cf4fdb34",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.041021Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.040840Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.160719Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.159902Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Nozzle Details\n",
-      "Nozzle Radius: 0.04445 m\n",
-      "Nozzle Throat Radius: 0.0214376 m\n",
-      "\n",
-      "Grain Details\n",
-      "Number of Grains: 3\n",
-      "Grain Spacing: 0.003 m\n",
-      "Grain Density: 782.4 kg/m3\n",
-      "Grain Outer Radius: 0.042799 m\n",
-      "Grain Inner Radius: 0.033146999999999996 m\n",
-      "Grain Height: 0.1524 m\n",
-      "Grain Volume: 0.000 m3\n",
-      "Grain Mass: 0.275 kg\n",
-      "\n",
-      "Motor Details\n",
-      "Total Burning Time: 2.43 s\n",
-      "Total Propellant Mass: 0.824 kg\n",
-      "Structural Mass Ratio: 0.548\n",
-      "Average Propellant Exhaust Velocity: 2514.035 m/s\n",
-      "Average Thrust: 852.260 N\n",
-      "Maximum Thrust: 1303.79 N at 0.04 s after ignition.\n",
-      "Total Impulse: 2070.992 Ns\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "motor = SolidMotor(\n",
-    "    thrust_source=\"../../data/motors/aerotech/AeroTech_K828FJ.eng\",\n",
-    "    burn_time=parameters.get(\"burn_time\")[0],\n",
-    "    dry_mass=1,\n",
-    "    dry_inertia=(0, 0, 0),\n",
-    "    center_of_dry_mass_position=0,\n",
-    "    grains_center_of_mass_position=parameters.get(\"distance_rocket_propellant\")[0],\n",
-    "    grain_number=3,\n",
-    "    grain_separation=parameters.get(\"grain_separation\")[0],\n",
-    "    grain_density=parameters.get(\"grain_density\")[0],\n",
-    "    grain_outer_radius=parameters.get(\"grain_outer_radius\")[0],\n",
-    "    grain_initial_inner_radius=parameters.get(\"grain_initial_inner_radius\")[0],\n",
-    "    grain_initial_height=parameters.get(\"grain_initial_height\")[0],\n",
-    "    nozzle_radius=parameters.get(\"nozzle_radius\")[0],\n",
-    "    throat_radius=parameters.get(\"throat_radius\")[0],\n",
-    "    interpolation_method=\"linear\",\n",
-    "    nozzle_position=parameters.get(\"distance_rocket_nozzle\")[0],\n",
-    ")\n",
-    "motor.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "07bb497d",
-   "metadata": {},
-   "source": [
-    "## Create Rocket\n",
-    "\n",
-    "Create the Bella Lui rocket with aerodynamic surfaces."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "9b602caf",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.162423Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.162242Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.194919Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.194082Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "Inertia Details\n",
-      "\n",
-      "Rocket Mass: 17.227 kg (without motor)\n",
-      "Rocket Dry Mass: 18.227 kg (with unloaded motor)\n",
-      "Rocket Loaded Mass: 19.051 kg\n",
-      "Rocket Structural Mass Ratio: 0.957\n",
-      "Rocket Inertia (with unloaded motor) 11: 2.002 kg*m2\n",
-      "Rocket Inertia (with unloaded motor) 22: 2.002 kg*m2\n",
-      "Rocket Inertia (with unloaded motor) 33: 0.064 kg*m2\n",
-      "Rocket Inertia (with unloaded motor) 12: 0.000 kg*m2\n",
-      "Rocket Inertia (with unloaded motor) 13: 0.000 kg*m2\n",
-      "Rocket Inertia (with unloaded motor) 23: 0.000 kg*m2\n",
-      "\n",
-      "Geometrical Parameters\n",
-      "\n",
-      "Rocket Maximum Radius: 0.078 m\n",
-      "Rocket Frontal Area: 0.019113 m2\n",
-      "\n",
-      "Rocket Distances\n",
-      "Rocket Center of Dry Mass - Center of Mass without Motor: 0.062 m\n",
-      "Rocket Center of Dry Mass - Nozzle Exit: 2.209 m\n",
-      "Rocket Center of Dry Mass - Center of Propellant Mass: 2.073 m\n",
-      "Rocket Center of Mass - Rocket Loaded Center of Mass: 0.090 m\n",
-      "\n",
-      "\n",
-      "Aerodynamics Lift Coefficient Derivatives\n",
-      "\n",
-      "Nose Cone Lift Coefficient Derivative: 2.000/rad\n",
-      "Fins Lift Coefficient Derivative: 10.281/rad\n",
-      "Tail Lift Coefficient Derivative: -0.502/rad\n",
-      "\n",
-      "Center of Pressure\n",
-      "\n",
-      "Nose Cone Center of Pressure position: 1.433 m\n",
-      "Fins Center of Pressure position: -0.871 m\n",
-      "Tail Center of Pressure position: -1.110 m\n",
-      "\n",
-      "Stability\n",
-      "\n",
-      "Center of Mass position (time=0): -0.152 m\n",
-      "Center of Pressure position (time=0): -0.469 m\n",
-      "Initial Static Margin (mach=0, time=0): 2.035 c\n",
-      "Final Static Margin (mach=0, time=burn_out): 2.609 c\n",
-      "Rocket Center of Mass (time=0) - Center of Pressure (mach=0): 0.317 m\n",
-      "\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Parachute Drogue (cd_s = 0.7854 m2, trigger = <function drogue_trigger at 0x7f938f23d440>)>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bella_lui = Rocket(\n",
-    "    radius=parameters.get(\"radius\")[0],\n",
-    "    mass=parameters.get(\"rocket_mass\")[0],\n",
-    "    inertia=(\n",
-    "        parameters.get(\"inertia_i\")[0],\n",
-    "        parameters.get(\"inertia_i\")[0],\n",
-    "        parameters.get(\"inertia_z\")[0],\n",
-    "    ),\n",
-    "    power_off_drag=0.43,\n",
-    "    power_on_drag=0.43,\n",
-    "    center_of_mass_without_motor=0,\n",
-    ")\n",
-    "bella_lui.set_rail_buttons(0.1, -0.5)\n",
-    "bella_lui.add_motor(motor, parameters.get(\"distance_rocket_nozzle\")[0])\n",
-    "bella_lui.add_nose(\n",
-    "    length=parameters.get(\"nose_length\")[0],\n",
-    "    kind=\"tangent\",\n",
-    "    position=parameters.get(\"nose_distance_to_cm\")[0]\n",
-    "    + parameters.get(\"nose_length\")[0],\n",
-    ")\n",
-    "bella_lui.add_trapezoidal_fins(\n",
-    "    3,\n",
-    "    span=parameters.get(\"fin_span\")[0],\n",
-    "    root_chord=parameters.get(\"fin_root_chord\")[0],\n",
-    "    tip_chord=parameters.get(\"fin_tip_chord\")[0],\n",
-    "    position=parameters.get(\"fin_distance_to_cm\")[0],\n",
-    ")\n",
-    "bella_lui.add_tail(\n",
-    "    top_radius=parameters.get(\"tail_top_radius\")[0],\n",
-    "    bottom_radius=parameters.get(\"tail_bottom_radius\")[0],\n",
-    "    length=parameters.get(\"tail_length\")[0],\n",
-    "    position=parameters.get(\"tail_distance_to_cm\")[0],\n",
-    ")\n",
-    "\n",
-    "# Define aerodynamic drag coefficients\n",
-    "bella_lui.power_off_drag = Function(\n",
-    "    [\n",
-    "        (0.01, 0.51),\n",
-    "        (0.02, 0.46),\n",
-    "        (0.04, 0.43),\n",
-    "        (0.28, 0.43),\n",
-    "        (0.29, 0.44),\n",
-    "        (0.45, 0.44),\n",
-    "        (0.49, 0.46),\n",
-    "    ],\n",
-    "    \"Mach Number\",\n",
-    "    \"Drag Coefficient with Power Off\",\n",
-    "    \"linear\",\n",
-    "    \"constant\",\n",
-    ")\n",
-    "bella_lui.power_on_drag = Function(\n",
-    "    [\n",
-    "        (0.01, 0.51),\n",
-    "        (0.02, 0.46),\n",
-    "        (0.04, 0.43),\n",
-    "        (0.28, 0.43),\n",
-    "        (0.29, 0.44),\n",
-    "        (0.45, 0.44),\n",
-    "        (0.49, 0.46),\n",
-    "    ],\n",
-    "    \"Mach Number\",\n",
-    "    \"Drag Coefficient with Power On\",\n",
-    "    \"linear\",\n",
-    "    \"constant\",\n",
-    ")\n",
-    "bella_lui.power_off_drag *= parameters.get(\"power_off_drag\")[0]\n",
-    "bella_lui.power_on_drag *= parameters.get(\"power_on_drag\")[0]\n",
-    "\n",
-    "bella_lui.info()\n",
-    "\n",
-    "\n",
-    "# Add parachute for landing\n",
-    "def drogue_trigger(p, h, y):\n",
-    "    # Deploy drogue when vertical velocity is negative (descending)\n",
-    "    return True if y[5] < 0 else False\n",
-    "\n",
-    "\n",
-    "bella_lui.add_parachute(\n",
-    "    name=\"Drogue\",\n",
-    "    cd_s=np.pi / 4,  # CdS = pi/4 m²\n",
-    "    trigger=drogue_trigger,\n",
-    "    sampling_rate=105,\n",
-    "    lag=1.0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcc6f352",
-   "metadata": {},
-   "source": [
-    "## Create Point Mass Rocket for 3-DOF Simulations\n",
-    "\n",
-    "For 3-DOF simulations, we use `PointMassRocket` and `PointMassMotor` which are simplified models that don't require full inertia and aerodynamic surface definitions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "7036d5c3",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.196683Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.196471Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.213056Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.212302Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Point Mass Rocket mass (without motor): 17.227 kg\n",
-      "Point Mass Rocket radius: 0.078 m\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Parachute Drogue (cd_s = 0.7854 m2, trigger = <function drogue_trigger_3dof at 0x7f938f27fba0>)>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Create a PointMassMotor with similar characteristics to the K828FJ\n",
-    "point_mass_motor = PointMassMotor(\n",
-    "    thrust_source=\"../../data/motors/aerotech/AeroTech_K828FJ.eng\",\n",
-    "    dry_mass=1.0,\n",
-    "    propellant_initial_mass=1.373,  # propellant mass\n",
-    ")\n",
-    "\n",
-    "# Create a PointMassRocket with similar properties to Bella Lui\n",
-    "point_mass_rocket = PointMassRocket(\n",
-    "    radius=parameters.get(\"radius\")[0],\n",
-    "    mass=parameters.get(\"rocket_mass\")[0],\n",
-    "    center_of_mass_without_motor=0,\n",
-    "    power_off_drag=0.43,\n",
-    "    power_on_drag=0.43,\n",
-    ")\n",
-    "point_mass_rocket.add_motor(\n",
-    "    point_mass_motor, parameters.get(\"distance_rocket_nozzle\")[0]\n",
-    ")\n",
-    "\n",
-    "print(f\"Point Mass Rocket mass (without motor): {point_mass_rocket.mass} kg\")\n",
-    "print(f\"Point Mass Rocket radius: {point_mass_rocket.radius} m\")\n",
-    "\n",
-    "\n",
-    "# Add parachute for landing (same as 6-DOF)\n",
-    "def drogue_trigger_3dof(p, h, y):\n",
-    "    # Deploy drogue when vertical velocity is negative (descending)\n",
-    "    return True if y[5] < 0 else False\n",
-    "\n",
-    "\n",
-    "point_mass_rocket.add_parachute(\n",
-    "    name=\"Drogue\",\n",
-    "    cd_s=np.pi / 4,  # CdS = pi/4 m²\n",
-    "    trigger=drogue_trigger_3dof,\n",
-    "    sampling_rate=105,\n",
-    "    lag=1.0,\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "de018d06",
-   "metadata": {},
-   "source": [
-    "## Run Flight Simulations\n",
-    "\n",
-    "Now we run four different flight simulations to compare 6-DOF and 3-DOF modes with different weathercocking coefficients."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7822b89d",
-   "metadata": {},
-   "source": [
-    "### 6-DOF Flight Simulation (Reference)\n",
-    "\n",
-    "This is the full 6-DOF simulation that serves as our reference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3f1d6acd",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.214830Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.214662Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.469085Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.467978Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "6-DOF Apogee: 460.91 m AGL\n",
-      "6-DOF Apogee Time: 10.61 s\n",
-      "6-DOF Simulation Runtime: 0.250 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "import time\n",
-    "\n",
-    "start_time = time.time()\n",
-    "flight_6dof = Flight(\n",
-    "    rocket=bella_lui,\n",
-    "    environment=env,\n",
-    "    rail_length=parameters.get(\"rail_length\")[0],\n",
-    "    inclination=parameters.get(\"inclination\")[0],\n",
-    "    heading=parameters.get(\"heading\")[0],\n",
-    "    terminate_on_apogee=False,\n",
-    ")\n",
-    "time_6dof = time.time() - start_time\n",
-    "\n",
-    "print(f\"6-DOF Apogee: {flight_6dof.apogee - env.elevation:.2f} m AGL\")\n",
-    "print(f\"6-DOF Apogee Time: {flight_6dof.apogee_time:.2f} s\")\n",
-    "print(f\"6-DOF Simulation Runtime: {time_6dof:.3f} s\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8d1dcdf6",
-   "metadata": {},
-   "source": [
-    "### 3-DOF Flight (No Weathercocking, wc=0)\n",
-    "\n",
-    "Using `PointMassRocket` and `PointMassMotor` for the 3-DOF simulation with fixed attitude mode."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "40c0ca4d",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.470862Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.470675Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.509940Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.509185Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3-DOF (wc=0) Apogee: 448.24 m AGL\n",
-      "3-DOF (wc=0) Apogee Time: 10.49 s\n",
-      "3-DOF (wc=0) Simulation Runtime: 0.035 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "start_time = time.time()\n",
-    "flight_3dof_fixed = Flight(\n",
-    "    rocket=point_mass_rocket,\n",
-    "    environment=env,\n",
-    "    rail_length=parameters.get(\"rail_length\")[0],\n",
-    "    inclination=parameters.get(\"inclination\")[0],\n",
-    "    heading=parameters.get(\"heading\")[0],\n",
-    "    terminate_on_apogee=False,\n",
-    "    simulation_mode=\"3 DOF\",\n",
-    "    weathercock_coeff=0.0,\n",
-    ")\n",
-    "time_3dof_fixed = time.time() - start_time\n",
-    "\n",
-    "print(f\"3-DOF (wc=0) Apogee: {flight_3dof_fixed.apogee - env.elevation:.2f} m AGL\")\n",
-    "print(f\"3-DOF (wc=0) Apogee Time: {flight_3dof_fixed.apogee_time:.2f} s\")\n",
-    "print(f\"3-DOF (wc=0) Simulation Runtime: {time_3dof_fixed:.3f} s\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9ad9f12f",
-   "metadata": {},
-   "source": [
-    "### 3-DOF Flight (Default Weathercocking, wc=1)\n",
-    "\n",
-    "Using `PointMassRocket` and `PointMassMotor` with default weathercocking - moderate alignment toward the relative wind."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "e2c13b7b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.511706Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.511504Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.565330Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.564561Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3-DOF (wc=1) Apogee: 447.90 m AGL\n",
-      "3-DOF (wc=1) Apogee Time: 10.49 s\n",
-      "3-DOF (wc=1) Simulation Runtime: 0.050 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "start_time = time.time()\n",
-    "flight_3dof_wc1 = Flight(\n",
-    "    rocket=point_mass_rocket,\n",
-    "    environment=env,\n",
-    "    rail_length=parameters.get(\"rail_length\")[0],\n",
-    "    inclination=parameters.get(\"inclination\")[0],\n",
-    "    heading=parameters.get(\"heading\")[0],\n",
-    "    terminate_on_apogee=False,\n",
-    "    simulation_mode=\"3 DOF\",\n",
-    "    weathercock_coeff=1.0,\n",
-    ")\n",
-    "time_3dof_wc1 = time.time() - start_time\n",
-    "\n",
-    "print(f\"3-DOF (wc=1) Apogee: {flight_3dof_wc1.apogee - env.elevation:.2f} m AGL\")\n",
-    "print(f\"3-DOF (wc=1) Apogee Time: {flight_3dof_wc1.apogee_time:.2f} s\")\n",
-    "print(f\"3-DOF (wc=1) Simulation Runtime: {time_3dof_wc1:.3f} s\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4fe0e13",
-   "metadata": {},
-   "source": [
-    "### 3-DOF Flight (High Weathercocking, wc=5)\n",
-    "\n",
-    "Using `PointMassRocket` and `PointMassMotor` with high weathercocking - faster alignment toward the relative wind."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "ee3cbf2b",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.567146Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.566967Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.627368Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.626658Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3-DOF (wc=5) Apogee: 447.61 m AGL\n",
-      "3-DOF (wc=5) Apogee Time: 10.48 s\n",
-      "3-DOF (wc=5) Simulation Runtime: 0.056 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "start_time = time.time()\n",
-    "flight_3dof_wc5 = Flight(\n",
-    "    rocket=point_mass_rocket,\n",
-    "    environment=env,\n",
-    "    rail_length=parameters.get(\"rail_length\")[0],\n",
-    "    inclination=parameters.get(\"inclination\")[0],\n",
-    "    heading=parameters.get(\"heading\")[0],\n",
-    "    terminate_on_apogee=False,\n",
-    "    simulation_mode=\"3 DOF\",\n",
-    "    weathercock_coeff=5.0,\n",
-    ")\n",
-    "time_3dof_wc5 = time.time() - start_time\n",
-    "\n",
-    "print(f\"3-DOF (wc=5) Apogee: {flight_3dof_wc5.apogee - env.elevation:.2f} m AGL\")\n",
-    "print(f\"3-DOF (wc=5) Apogee Time: {flight_3dof_wc5.apogee_time:.2f} s\")\n",
-    "print(f\"3-DOF (wc=5) Simulation Runtime: {time_3dof_wc5:.3f} s\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a4c92ff",
-   "metadata": {},
-   "source": [
-    "## Results Comparison\n",
-    "\n",
-    "### Summary Table"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "96b61b24",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.629178Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.629004Z",
-     "iopub.status.idle": "2025-11-27T11:34:57.716007Z",
-     "shell.execute_reply": "2025-11-27T11:34:57.715211Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "================================================================================\n",
-      "SIMULATION RESULTS COMPARISON\n",
-      "================================================================================\n",
-      "\n",
-      "Parameter                                     6-DOF 3DOF(wc=0) 3DOF(wc=1) 3DOF(wc=5)\n",
-      "--------------------------------------------------------------------------------\n",
-      "Apogee (m AGL)                               460.91     448.24     447.90     447.61\n",
-      "Apogee Time (s)                               10.61      10.49      10.49      10.48\n",
-      "Max Speed (m/s)                               86.24      84.78      84.72      84.71\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Max Acceleration (m/s²)                       58.47        N/A        N/A        N/A\n",
-      "  (Note: Max acceleration not yet available for 3-DOF with parachute)\n",
-      "Impact X (m)                                   5.95      30.32      18.82       2.45\n",
-      "Impact Y (m)                                   1.62      38.52      20.74      -4.76\n",
-      "Simulation Runtime (s)                        0.250      0.035      0.050      0.056\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "PERFORMANCE COMPARISON:\n",
-      "--------------------------------------------------------------------------------\n",
-      "Speedup vs 6-DOF                                  -        7.1x        5.0x        4.4x\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "PERCENTAGE DIFFERENCE FROM 6-DOF REFERENCE:\n",
-      "--------------------------------------------------------------------------------\n",
-      "Apogee Difference                                 -      -1.46%      -1.50%      -1.53%\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"=\" * 80)\n",
-    "print(\"SIMULATION RESULTS COMPARISON\")\n",
-    "print(\"=\" * 80)\n",
-    "\n",
-    "print(\n",
-    "    \"\\n{:<40} {:>10} {:>10} {:>10} {:>10}\".format(\n",
-    "        \"Parameter\", \"6-DOF\", \"3DOF(wc=0)\", \"3DOF(wc=1)\", \"3DOF(wc=5)\"\n",
-    "    )\n",
-    ")\n",
-    "print(\"-\" * 80)\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "        \"Apogee (m AGL)\",\n",
-    "        flight_6dof.apogee - env.elevation,\n",
-    "        flight_3dof_fixed.apogee - env.elevation,\n",
-    "        flight_3dof_wc1.apogee - env.elevation,\n",
-    "        flight_3dof_wc5.apogee - env.elevation,\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "        \"Apogee Time (s)\",\n",
-    "        flight_6dof.apogee_time,\n",
-    "        flight_3dof_fixed.apogee_time,\n",
-    "        flight_3dof_wc1.apogee_time,\n",
-    "        flight_3dof_wc5.apogee_time,\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "        \"Max Speed (m/s)\",\n",
-    "        flight_6dof.max_speed,\n",
-    "        flight_3dof_fixed.max_speed,\n",
-    "        flight_3dof_wc1.max_speed,\n",
-    "        flight_3dof_wc5.max_speed,\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "# Max acceleration only available for 6-DOF with parachute descent\n",
-    "print(\n",
-    "    \"{:<40} {:>10.2f} {:>10} {:>10} {:>10}\".format(\n",
-    "        \"Max Acceleration (m/s²)\",\n",
-    "        flight_6dof.max_acceleration,\n",
-    "        \"N/A\",\n",
-    "        \"N/A\",\n",
-    "        \"N/A\",\n",
-    "    )\n",
-    ")\n",
-    "print(\"  (Note: Max acceleration not yet available for 3-DOF with parachute)\")\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "        \"Impact X (m)\",\n",
-    "        flight_6dof.x_impact,\n",
-    "        flight_3dof_fixed.x_impact,\n",
-    "        flight_3dof_wc1.x_impact,\n",
-    "        flight_3dof_wc5.x_impact,\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10.2f} {:>10.2f} {:>10.2f} {:>10.2f}\".format(\n",
-    "        \"Impact Y (m)\",\n",
-    "        flight_6dof.y_impact,\n",
-    "        flight_3dof_fixed.y_impact,\n",
-    "        flight_3dof_wc1.y_impact,\n",
-    "        flight_3dof_wc5.y_impact,\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10.3f} {:>10.3f} {:>10.3f} {:>10.3f}\".format(\n",
-    "        \"Simulation Runtime (s)\",\n",
-    "        time_6dof,\n",
-    "        time_3dof_fixed,\n",
-    "        time_3dof_wc1,\n",
-    "        time_3dof_wc5,\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "# Performance comparison\n",
-    "print(\"\\n\" + \"-\" * 80)\n",
-    "print(\"PERFORMANCE COMPARISON:\")\n",
-    "print(\"-\" * 80)\n",
-    "\n",
-    "speedup_fixed = time_6dof / time_3dof_fixed if time_3dof_fixed > 0 else 0\n",
-    "speedup_wc1 = time_6dof / time_3dof_wc1 if time_3dof_wc1 > 0 else 0\n",
-    "speedup_wc5 = time_6dof / time_3dof_wc5 if time_3dof_wc5 > 0 else 0\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10} {:>10.1f}x {:>10.1f}x {:>10.1f}x\".format(\n",
-    "        \"Speedup vs 6-DOF\", \"-\", speedup_fixed, speedup_wc1, speedup_wc5\n",
-    "    )\n",
-    ")\n",
-    "\n",
-    "# Percentage differences\n",
-    "print(\"\\n\" + \"-\" * 80)\n",
-    "print(\"PERCENTAGE DIFFERENCE FROM 6-DOF REFERENCE:\")\n",
-    "print(\"-\" * 80)\n",
-    "\n",
-    "apogee_diff_fixed = (\n",
-    "    (flight_3dof_fixed.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
-    ")\n",
-    "apogee_diff_wc1 = (\n",
-    "    (flight_3dof_wc1.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
-    ")\n",
-    "apogee_diff_wc5 = (\n",
-    "    (flight_3dof_wc5.apogee - flight_6dof.apogee) / flight_6dof.apogee * 100\n",
-    ")\n",
-    "\n",
-    "print(\n",
-    "    \"{:<40} {:>10} {:>10.2f}% {:>10.2f}% {:>10.2f}%\".format(\n",
-    "        \"Apogee Difference\", \"-\", apogee_diff_fixed, apogee_diff_wc1, apogee_diff_wc5\n",
-    "    )\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9af958c4",
-   "metadata": {},
-   "source": [
-    "## Comparison Plots"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "9e97cc02",
-   "metadata": {
-    "execution": {
-     "iopub.execute_input": "2025-11-27T11:34:57.717862Z",
-     "iopub.status.busy": "2025-11-27T11:34:57.717686Z",
-     "iopub.status.idle": "2025-11-27T11:34:58.262675Z",
-     "shell.execute_reply": "2025-11-27T11:34:58.261762Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1400x1000 with 5 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, axes = plt.subplots(2, 2, figsize=(14, 10))\n",
-    "\n",
-    "# Plot 1: Altitude vs Time\n",
-    "ax1 = axes[0, 0]\n",
-    "ax1.plot(\n",
-    "    flight_6dof.z[:, 0], flight_6dof.z[:, 1] - env.elevation, label=\"6-DOF\", linewidth=2\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    flight_3dof_fixed.z[:, 0],\n",
-    "    flight_3dof_fixed.z[:, 1] - env.elevation,\n",
-    "    label=\"3-DOF (wc=0)\",\n",
-    "    linestyle=\"--\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    flight_3dof_wc1.z[:, 0],\n",
-    "    flight_3dof_wc1.z[:, 1] - env.elevation,\n",
-    "    label=\"3-DOF (wc=1)\",\n",
-    "    linestyle=\"-.\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax1.plot(\n",
-    "    flight_3dof_wc5.z[:, 0],\n",
-    "    flight_3dof_wc5.z[:, 1] - env.elevation,\n",
-    "    label=\"3-DOF (wc=5)\",\n",
-    "    linestyle=\":\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax1.set_xlabel(\"Time (s)\")\n",
-    "ax1.set_ylabel(\"Altitude AGL (m)\")\n",
-    "ax1.set_title(\"Altitude vs Time Comparison\")\n",
-    "ax1.legend()\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Plot 2: Speed vs Time\n",
-    "ax2 = axes[0, 1]\n",
-    "ax2.plot(flight_6dof.speed[:, 0], flight_6dof.speed[:, 1], label=\"6-DOF\", linewidth=2)\n",
-    "ax2.plot(\n",
-    "    flight_3dof_fixed.speed[:, 0],\n",
-    "    flight_3dof_fixed.speed[:, 1],\n",
-    "    label=\"3-DOF (wc=0)\",\n",
-    "    linestyle=\"--\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax2.plot(\n",
-    "    flight_3dof_wc1.speed[:, 0],\n",
-    "    flight_3dof_wc1.speed[:, 1],\n",
-    "    label=\"3-DOF (wc=1)\",\n",
-    "    linestyle=\"-.\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax2.plot(\n",
-    "    flight_3dof_wc5.speed[:, 0],\n",
-    "    flight_3dof_wc5.speed[:, 1],\n",
-    "    label=\"3-DOF (wc=5)\",\n",
-    "    linestyle=\":\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax2.set_xlabel(\"Time (s)\")\n",
-    "ax2.set_ylabel(\"Speed (m/s)\")\n",
-    "ax2.set_title(\"Speed vs Time Comparison\")\n",
-    "ax2.legend()\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Plot 3: Horizontal Trajectory (X-Y)\n",
-    "ax3 = axes[1, 0]\n",
-    "ax3.plot(flight_6dof.x[:, 1], flight_6dof.y[:, 1], label=\"6-DOF\", linewidth=2)\n",
-    "ax3.plot(\n",
-    "    flight_3dof_fixed.x[:, 1],\n",
-    "    flight_3dof_fixed.y[:, 1],\n",
-    "    label=\"3-DOF (wc=0)\",\n",
-    "    linestyle=\"--\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax3.plot(\n",
-    "    flight_3dof_wc1.x[:, 1],\n",
-    "    flight_3dof_wc1.y[:, 1],\n",
-    "    label=\"3-DOF (wc=1)\",\n",
-    "    linestyle=\"-.\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax3.plot(\n",
-    "    flight_3dof_wc5.x[:, 1],\n",
-    "    flight_3dof_wc5.y[:, 1],\n",
-    "    label=\"3-DOF (wc=5)\",\n",
-    "    linestyle=\":\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax3.set_xlabel(\"X Position (m)\")\n",
-    "ax3.set_ylabel(\"Y Position (m)\")\n",
-    "ax3.set_title(\"Horizontal Trajectory Comparison\")\n",
-    "ax3.legend()\n",
-    "ax3.grid(True, alpha=0.3)\n",
-    "ax3.set_aspect(\"equal\")\n",
-    "\n",
-    "# Plot 4: 3D Trajectory\n",
-    "ax4 = fig.add_subplot(2, 2, 4, projection=\"3d\")\n",
-    "ax4.plot(\n",
-    "    flight_6dof.x[:, 1],\n",
-    "    flight_6dof.y[:, 1],\n",
-    "    flight_6dof.z[:, 1] - env.elevation,\n",
-    "    label=\"6-DOF\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax4.plot(\n",
-    "    flight_3dof_fixed.x[:, 1],\n",
-    "    flight_3dof_fixed.y[:, 1],\n",
-    "    flight_3dof_fixed.z[:, 1] - env.elevation,\n",
-    "    label=\"3-DOF (wc=0)\",\n",
-    "    linestyle=\"--\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax4.plot(\n",
-    "    flight_3dof_wc1.x[:, 1],\n",
-    "    flight_3dof_wc1.y[:, 1],\n",
-    "    flight_3dof_wc1.z[:, 1] - env.elevation,\n",
-    "    label=\"3-DOF (wc=1)\",\n",
-    "    linestyle=\"-.\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax4.plot(\n",
-    "    flight_3dof_wc5.x[:, 1],\n",
-    "    flight_3dof_wc5.y[:, 1],\n",
-    "    flight_3dof_wc5.z[:, 1] - env.elevation,\n",
-    "    label=\"3-DOF (wc=5)\",\n",
-    "    linestyle=\":\",\n",
-    "    linewidth=2,\n",
-    ")\n",
-    "ax4.set_xlabel(\"X (m)\")\n",
-    "ax4.set_ylabel(\"Y (m)\")\n",
-    "ax4.set_zlabel(\"Altitude AGL (m)\")\n",
-    "ax4.set_title(\"3D Trajectory Comparison\")\n",
-    "ax4.legend()\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e0b4327",
-   "metadata": {},
-   "source": [
-    "## Summary\n",
-    "\n",
-    "This notebook demonstrates the differences between 6-DOF and 3-DOF simulation modes:\n",
-    "\n",
-    "### Simulation Modes\n",
-    "\n",
-    "1. **6-DOF (Full Dynamics)**:\n",
-    "   - Full rotational and translational dynamics\n",
-    "   - Quaternions evolve based on angular momentum conservation\n",
-    "   - Most accurate but computationally expensive\n",
-    "\n",
-    "2. **3-DOF with weathercock_coeff=0 (Fixed Attitude)**:\n",
-    "   - Only translational dynamics\n",
-    "   - Attitude remains fixed (no quaternion evolution)\n",
-    "   - Fastest but may not capture lateral motion accurately\n",
-    "\n",
-    "3. **3-DOF with weathercock_coeff=1 (Default Weathercocking)**:\n",
-    "   - Translational dynamics with quasi-static attitude evolution\n",
-    "   - Body axis aligns toward relative wind direction\n",
-    "   - Good balance between accuracy and speed\n",
-    "\n",
-    "4. **3-DOF with weathercock_coeff=5 (High Weathercocking)**:\n",
-    "   - Faster alignment toward relative wind\n",
-    "   - Useful when rocket is expected to quickly align with velocity\n",
-    "\n",
-    "### Key Observations\n",
-    "\n",
-    "- The weathercocking model helps the 3-DOF simulation better approximate the 6-DOF behavior by allowing the attitude to evolve\n",
-    "- Higher `weathercock_coeff` values result in faster alignment with the wind\n",
-    "- The 3-DOF mode is significantly faster and suitable for Monte Carlo simulations where many runs are needed"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.12.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/docs/user/index.rst b/docs/user/index.rst
index 113afc3aa..792324dd7 100644
--- a/docs/user/index.rst
+++ b/docs/user/index.rst
@@ -28,7 +28,7 @@ RocketPy's User Guide
    Air Brakes Example <airbrakes.rst>
    ../notebooks/sensors.ipynb
    ../matlab/matlab.rst
-
+   3 DOF Simulations and comparison<three_dof_simulation.rst>
 .. toctree::
    :maxdepth: 2
    :caption: Monte Carlo Simulations
diff --git a/docs/user/three_dof_simulation.rst b/docs/user/three_dof_simulation.rst
index 381890294..432a55ecc 100644
--- a/docs/user/three_dof_simulation.rst
+++ b/docs/user/three_dof_simulation.rst
@@ -340,6 +340,316 @@ Here's a complete 3-DOF simulation from start to finish:
 
     flight.plots.trajectory_3d()
 
+Weathercocking Model
+--------------------
+
+RocketPy's 3-DOF simulation mode includes a weathercocking model that allows
+the rocket's attitude to evolve during flight. This feature simulates how a
+statically stable rocket naturally aligns with the relative wind direction.
+
+Understanding Weathercocking
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Weathercocking is the tendency of a rocket to align its body axis with the
+direction of the relative wind. In reality, this occurs due to aerodynamic
+restoring moments from fins and other stabilizing surfaces. The 3-DOF
+weathercocking model provides a simplified representation of this behavior
+without requiring full 6-DOF rotational dynamics.
+
+The ``weathercock_coeff`` Parameter
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The weathercocking behavior is controlled by the ``weathercock_coeff`` parameter
+in the :class:`rocketpy.Flight` class:
+
+.. jupyter-execute::
+
+    from rocketpy import Environment, PointMassMotor, PointMassRocket, Flight
+
+    env = Environment(
+        latitude=32.990254,
+        longitude=-106.974998,
+        elevation=1400
+    )
+    env.set_atmospheric_model(type="StandardAtmosphere")
+
+    motor = PointMassMotor(
+        thrust_source=1500,
+        dry_mass=1.5,
+        propellant_initial_mass=2.5,
+        burn_time=3.5,
+    )
+
+    rocket = PointMassRocket(
+        radius=0.078,
+        mass=15.0,
+        center_of_mass_without_motor=0.0,
+        power_off_drag=0.43,
+        power_on_drag=0.43,
+    )
+    rocket.add_motor(motor, position=0)
+
+    # Flight with weathercocking enabled
+    flight = Flight(
+        rocket=rocket,
+        environment=env,
+        rail_length=4.2,
+        inclination=85,
+        heading=45,
+        simulation_mode="3 DOF",
+        weathercock_coeff=1.0,  # Default value
+    )
+
+    print(f"Apogee: {flight.apogee - env.elevation:.2f} m")
+
+The ``weathercock_coeff`` parameter controls the rate at which the rocket
+aligns with the relative wind:
+
+- ``weathercock_coeff=0``: No weathercocking (original fixed-attitude behavior)
+- ``weathercock_coeff=1.0``: Default moderate alignment rate
+- ``weathercock_coeff>1.0``: Faster alignment (more stable rocket)
+
+Effect of Weathercocking Coefficient
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Higher values of ``weathercock_coeff`` result in faster alignment with the
+relative wind. This affects the lateral motion and impact point:
+
+.. list-table:: Weathercocking Coefficient Effects
+   :header-rows: 1
+   :widths: 25 25 50
+
+   * - Coefficient
+     - Alignment Speed
+     - Typical Use Case
+   * - 0
+     - None (fixed attitude)
+     - Original 3-DOF behavior
+   * - 1.0
+     - Moderate
+     - Default, general purpose
+   * - 2.0-5.0
+     - Fast
+     - Highly stable rockets
+   * - >5.0
+     - Very fast
+     - Rockets with large fins
+
+3-DOF vs 6-DOF Comparison Results
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The following example compares a 6-DOF simulation using the full Bella Lui rocket
+with 3-DOF simulations using ``PointMassRocket`` and different weathercocking
+coefficients. This demonstrates the trade-off between computational speed and
+accuracy.
+
+**Setup the simulations:**
+
+.. jupyter-execute::
+
+    import numpy as np
+    import time
+    from rocketpy import Environment, Flight, Rocket, SolidMotor
+    from rocketpy.rocket.point_mass_rocket import PointMassRocket
+    from rocketpy.motors.point_mass_motor import PointMassMotor
+
+    # Environment
+    env = Environment(
+        gravity=9.81,
+        latitude=47.213476,
+        longitude=9.003336,
+        elevation=407,
+    )
+    env.set_atmospheric_model(type="StandardAtmosphere")
+    env.max_expected_height = 2000
+
+    # Full 6-DOF Motor
+    motor_6dof = SolidMotor(
+        thrust_source="../data/motors/aerotech/AeroTech_K828FJ.eng",
+        burn_time=2.43,
+        dry_mass=1,
+        dry_inertia=(0, 0, 0),
+        center_of_dry_mass_position=0,
+        grains_center_of_mass_position=-1,
+        grain_number=3,
+        grain_separation=0.003,
+        grain_density=782.4,
+        grain_outer_radius=0.042799,
+        grain_initial_inner_radius=0.033147,
+        grain_initial_height=0.1524,
+        nozzle_radius=0.04445,
+        throat_radius=0.0214376,
+        nozzle_position=-1.1356,
+    )
+
+    # Full 6-DOF Rocket
+    rocket_6dof = Rocket(
+        radius=0.078,
+        mass=17.227,
+        inertia=(0.78267, 0.78267, 0.064244),
+        power_off_drag=0.43,
+        power_on_drag=0.43,
+        center_of_mass_without_motor=0,
+    )
+    rocket_6dof.set_rail_buttons(0.1, -0.5)
+    rocket_6dof.add_motor(motor_6dof, -1.1356)
+    rocket_6dof.add_nose(length=0.242, kind="tangent", position=1.542)
+    rocket_6dof.add_trapezoidal_fins(3, span=0.200, root_chord=0.280, tip_chord=0.125, position=-0.75)
+
+    # Point Mass Motor for 3-DOF
+    motor_3dof = PointMassMotor(
+        thrust_source="../data/motors/aerotech/AeroTech_K828FJ.eng",
+        dry_mass=1.0,
+        propellant_initial_mass=1.373,
+    )
+
+    # Point Mass Rocket for 3-DOF
+    rocket_3dof = PointMassRocket(
+        radius=0.078,
+        mass=17.227,
+        center_of_mass_without_motor=0,
+        power_off_drag=0.43,
+        power_on_drag=0.43,
+    )
+    rocket_3dof.add_motor(motor_3dof, -1.1356)
+
+**Run simulations and compare results:**
+
+.. jupyter-execute::
+
+    # 6-DOF Flight
+    start = time.time()
+    flight_6dof = Flight(
+        rocket=rocket_6dof,
+        environment=env,
+        rail_length=4.2,
+        inclination=89,
+        heading=45,
+        terminate_on_apogee=True,
+    )
+    time_6dof = time.time() - start
+
+    # 3-DOF with no weathercocking
+    start = time.time()
+    flight_3dof_0 = Flight(
+        rocket=rocket_3dof,
+        environment=env,
+        rail_length=4.2,
+        inclination=89,
+        heading=45,
+        terminate_on_apogee=True,
+        simulation_mode="3 DOF",
+        weathercock_coeff=0.0,
+    )
+    time_3dof_0 = time.time() - start
+
+    # 3-DOF with default weathercocking
+    start = time.time()
+    flight_3dof_1 = Flight(
+        rocket=rocket_3dof,
+        environment=env,
+        rail_length=4.2,
+        inclination=89,
+        heading=45,
+        terminate_on_apogee=True,
+        simulation_mode="3 DOF",
+        weathercock_coeff=1.0,
+    )
+    time_3dof_1 = time.time() - start
+
+    # 3-DOF with high weathercocking
+    start = time.time()
+    flight_3dof_5 = Flight(
+        rocket=rocket_3dof,
+        environment=env,
+        rail_length=4.2,
+        inclination=89,
+        heading=45,
+        terminate_on_apogee=True,
+        simulation_mode="3 DOF",
+        weathercock_coeff=5.0,
+    )
+    time_3dof_5 = time.time() - start
+
+    # Print comparison table
+    print("=" * 80)
+    print("SIMULATION RESULTS COMPARISON")
+    print("=" * 80)
+    print("\n{:<30} {:>12} {:>12} {:>12} {:>12}".format(
+        "Parameter", "6-DOF", "3DOF(wc=0)", "3DOF(wc=1)", "3DOF(wc=5)"
+    ))
+    print("-" * 80)
+    print("{:<30} {:>12.2f} {:>12.2f} {:>12.2f} {:>12.2f}".format(
+        "Apogee (m AGL)",
+        flight_6dof.apogee - env.elevation,
+        flight_3dof_0.apogee - env.elevation,
+        flight_3dof_1.apogee - env.elevation,
+        flight_3dof_5.apogee - env.elevation,
+    ))
+    print("{:<30} {:>12.2f} {:>12.2f} {:>12.2f} {:>12.2f}".format(
+        "Apogee Time (s)",
+        flight_6dof.apogee_time,
+        flight_3dof_0.apogee_time,
+        flight_3dof_1.apogee_time,
+        flight_3dof_5.apogee_time,
+    ))
+    print("{:<30} {:>12.2f} {:>12.2f} {:>12.2f} {:>12.2f}".format(
+        "Max Speed (m/s)",
+        flight_6dof.max_speed,
+        flight_3dof_0.max_speed,
+        flight_3dof_1.max_speed,
+        flight_3dof_5.max_speed,
+    ))
+    print("{:<30} {:>12.3f} {:>12.3f} {:>12.3f} {:>12.3f}".format(
+        "Runtime (s)",
+        time_6dof,
+        time_3dof_0,
+        time_3dof_1,
+        time_3dof_5,
+    ))
+    print("-" * 80)
+    print("Speedup vs 6-DOF:             {:>12} {:>12.1f}x {:>12.1f}x {:>12.1f}x".format(
+        "-",
+        time_6dof / time_3dof_0 if time_3dof_0 > 0 else 0,
+        time_6dof / time_3dof_1 if time_3dof_1 > 0 else 0,
+        time_6dof / time_3dof_5 if time_3dof_5 > 0 else 0,
+    ))
+
+**3D Trajectory Comparison:**
+
+.. jupyter-execute::
+
+    import matplotlib.pyplot as plt
+    from mpl_toolkits.mplot3d import Axes3D
+
+    fig = plt.figure(figsize=(12, 8))
+    ax = fig.add_subplot(111, projection="3d")
+
+    # Plot all trajectories
+    ax.plot(flight_6dof.x[:, 1], flight_6dof.y[:, 1], flight_6dof.z[:, 1] - env.elevation,
+            "b-", linewidth=2, label="6-DOF")
+    ax.plot(flight_3dof_0.x[:, 1], flight_3dof_0.y[:, 1], flight_3dof_0.z[:, 1] - env.elevation,
+            "r--", linewidth=2, label="3-DOF (wc=0)")
+    ax.plot(flight_3dof_1.x[:, 1], flight_3dof_1.y[:, 1], flight_3dof_1.z[:, 1] - env.elevation,
+            "g--", linewidth=2, label="3-DOF (wc=1)")
+    ax.plot(flight_3dof_5.x[:, 1], flight_3dof_5.y[:, 1], flight_3dof_5.z[:, 1] - env.elevation,
+            "m--", linewidth=2, label="3-DOF (wc=5)")
+
+    ax.set_xlabel("X (m)")
+    ax.set_ylabel("Y (m)")
+    ax.set_zlabel("Altitude AGL (m)")
+    ax.set_title("3-DOF vs 6-DOF Trajectory Comparison with Weathercocking")
+    ax.legend()
+    plt.tight_layout()
+    plt.show()
+
+The results show that:
+
+- **3-DOF is 5-7x faster** than 6-DOF simulations
+- **Apogee prediction** is within 1-3% of 6-DOF
+- **Weathercocking** improves trajectory accuracy by aligning the rocket with relative wind
+- **Higher weathercock_coeff** values result in trajectories closer to 6-DOF
+
 Comparison: 3-DOF vs 6-DOF
 ---------------------------
 
@@ -353,10 +663,10 @@ Understanding the differences between simulation modes:
      - 3-DOF
      - 6-DOF
    * - Computational Speed
-     - Fast
-     - Slower
+     - 5-7x faster
+     - Slower (more accurate)
    * - Rocket Orientation
-     - Fixed (no rotation)
+     - Weathercocking model
      - Full attitude dynamics
    * - Stability Analysis
      - ❌ Not available
@@ -371,10 +681,10 @@ Understanding the differences between simulation modes:
      - ❌ Not needed
      - ✅ Required
    * - Use Cases
-     - Quick estimates, education
+     - Quick estimates, Monte Carlo
      - Detailed design, stability
    * - Trajectory Accuracy
-     - Good for stable rockets
+     - Good (~1.5% error)
      - Highly accurate
 
 Best Practices
@@ -401,8 +711,7 @@ Limitations and Warnings
 
     - **No stability checking** - The simulation cannot detect unstable rockets
     - **No attitude control** - Air brakes and thrust vectoring are not supported
-    - **Assumes perfect alignment** - Rocket always points along velocity vector
-    - **No wind weathercocking** - Wind effects on orientation are ignored
+    - **Simplified weathercocking** - Uses proportional alignment model, not full dynamics
 
 .. warning::
 
@@ -428,4 +737,4 @@ For more information about point mass trajectory simulations:
 
 - `Trajectory Optimization <https://en.wikipedia.org/wiki/Trajectory_optimization>`_
 - `Equations of Motion <https://en.wikipedia.org/wiki/Equations_of_motion>`_
-- `Point Mass Model <https://www.grc.nasa.gov/www/k-12/airplane/flteqs.html>`_
+- `Point Mass Model <https://www.grc.nasa.gov/www/k-12/airplane/flteqs.html>`_
\ No newline at end of file

From 7cd00503c0e82430e981f9061ce1a9e297a3ba41 Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Fri, 28 Nov 2025 00:33:38 +0530
Subject: [PATCH 05/24] MNT: corrections to test_flight_3dof.py

- MNT: corrected doc string to represent correct orientation

- MNT: improved tolerance of check on quaternion derivative which should be ideally very small when axes are aligned
---
 tests/unit/simulation/test_flight_3dof.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/unit/simulation/test_flight_3dof.py b/tests/unit/simulation/test_flight_3dof.py
index 3bd79fa51..37b529e40 100644
--- a/tests/unit/simulation/test_flight_3dof.py
+++ b/tests/unit/simulation/test_flight_3dof.py
@@ -252,7 +252,7 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
         simulation_mode="3 DOF",
         weathercock_coeff=1.0,
     )
-    # Body pointing in -x direction (into the wind for vx=50)
+    # Body pointing in +x direction (into the wind for vx=50)
     # Quaternion for 90 degree rotation about y-axis uses half-angle:
     # e0=cos(90°/2)=cos(45°), e2=sin(90°/2)=sin(45°)
     sqrt2_2 = np.sqrt(2) / 2
@@ -263,4 +263,4 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
     # With alignment, quaternion derivatives should be very small
     e_dot = result[6:10]
     e_dot_magnitude = sum(ed**2 for ed in e_dot) ** 0.5
-    assert e_dot_magnitude < 0.1, "Quaternion derivatives should be small when aligned"
+    assert e_dot_magnitude < 1e-8, "Quaternion derivatives should be very small when aligned"

From 5ff03cdda22d030e9660ad1b1052b0e1daa03043 Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Fri, 28 Nov 2025 00:39:51 +0530
Subject: [PATCH 06/24] MNT: docstring corrections to flight.py around new
 weathercocking implementation

---
 rocketpy/simulation/flight.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 08fc26768..92b2c6a7a 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -578,7 +578,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
             documentation [1]_.
         weathercock_coeff : float, optional
             Coefficient that controls the rate at which the rocket's body axis
-            aligns with the relative wind direction in 3-DOF simulations.
+            aligns with the relative wind direction in 3-DOF simulations, in rad/s.
             A higher value means faster alignment (quasi-static weathercocking).
             This parameter is only used when simulation_mode is '3 DOF'.
             Default is 1.0, which provides moderate alignment. Set to 0 to
@@ -1802,7 +1802,7 @@ def u_dot(self, t, u, post_processing=False):  # pylint: disable=too-many-locals
     def u_dot_generalized_3dof(self, t, u, post_processing=False):
         """Calculates derivative of u state vector with respect to time when the
         rocket is flying in 3 DOF motion in space and significant mass variation
-        effects exist.Includes a weathercocking model that evolves the body axis
+        effects exist. Includes a weathercocking model that evolves the body axis
         direction toward the relative wind direction.
 
         Parameters
@@ -1942,7 +1942,7 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                 sin_angle = min(1.0, max(-1.0, sin_angle))
 
                 # Angular velocity magnitude proportional to misalignment angle
-                # Using sin(angle) as approximation for small angles: sin(theta) ≈ theta
+                # Angular velocity magnitude proportional to sin(angle)
                 omega_mag = self.weathercock_coeff * sin_angle
 
                 # Angular velocity in inertial frame

From 14d199d95b66227f5c70e5936eec8a482f38a685 Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Fri, 28 Nov 2025 00:49:36 +0530
Subject: [PATCH 07/24] BUG: correction of singularity bug during align on
 vectors in weathercocking

- BUG: implemented a dot product check to ensure that singularity bug is avoided when rocket body and wind velocity are anti aligned to make rocket statically stable (in u_dot_generalized_3dof)

- MNT: removed redundant double assignment of e0 and w0 vectors within u_dot_generalized_3dof

- MNT: format correction to test_flight_3dof.py
---
 rocketpy/simulation/flight.py             | 39 ++++++++++++++++++++---
 tests/unit/simulation/test_flight_3dof.py |  4 ++-
 2 files changed, 37 insertions(+), 6 deletions(-)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 92b2c6a7a..f8dea4401 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -1905,8 +1905,6 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
 
         # Dynamics
         v_dot = K @ (total_force / total_mass)
-        e_dot = [0, 0, 0, 0]  # Euler derivatives unused in 3DOF
-        w_dot = [0, 0, 0]  # No angular dynamics in 3DOF
         r_dot = [vx, vy, vz]
         # Weathercocking: evolve body axis direction toward relative wind
         # The body z-axis (attitude vector) should align with -freestream_velocity
@@ -1964,9 +1962,40 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                 e_dot = [e0_dot, e1_dot, e2_dot, e3_dot]
                 w_dot = [0, 0, 0]  # No angular acceleration in 3DOF model
             else:
-                # Already aligned or anti-aligned
-                e_dot = [0, 0, 0, 0]
-                w_dot = [0, 0, 0]
+                # Check if aligned or anti-aligned using dot product
+                dot = body_z_inertial @ desired_direction  # Vector dot product
+                if dot > 0.999:  # Aligned
+                    e_dot = [0, 0, 0, 0]
+                    w_dot = [0, 0, 0]
+                elif dot < -0.999:  # Anti-aligned
+                    # Choose an arbitrary perpendicular axis
+                    # Try [1,0,0] unless body_z_inertial is parallel to it
+                    x_axis = Vector([1.0, 0.0, 0.0])
+                    perp_axis = body_z_inertial ^ x_axis
+                    if abs(perp_axis) < 1e-6:
+                        # If parallel, use y axis
+                        y_axis = Vector([0.0, 1.0, 0.0])
+                        perp_axis = body_z_inertial ^ y_axis
+                    rotation_axis = perp_axis.normalized()
+                    # 180 degree rotation: sin(angle) = 1
+                    omega_mag = self.weathercock_coeff * 1.0
+                    omega_inertial = rotation_axis * omega_mag
+                    omega_body = Kt @ omega_inertial
+                    omega1_wc, omega2_wc, omega3_wc = (
+                        omega_body.x,
+                        omega_body.y,
+                        omega_body.z,
+                    )
+                    e0_dot = 0.5 * (-omega1_wc * e1 - omega2_wc * e2 - omega3_wc * e3)
+                    e1_dot = 0.5 * (omega1_wc * e0 + omega3_wc * e2 - omega2_wc * e3)
+                    e2_dot = 0.5 * (omega2_wc * e0 - omega3_wc * e1 + omega1_wc * e3)
+                    e3_dot = 0.5 * (omega3_wc * e0 + omega2_wc * e1 - omega1_wc * e2)
+                    e_dot = [e0_dot, e1_dot, e2_dot, e3_dot]
+                    w_dot = [0, 0, 0]
+                else:
+                    # Vectors are nearly aligned, treat as aligned
+                    e_dot = [0, 0, 0, 0]
+                    w_dot = [0, 0, 0]
         else:
             # No weathercocking or negligible freestream speed
             e_dot = [0, 0, 0, 0]
diff --git a/tests/unit/simulation/test_flight_3dof.py b/tests/unit/simulation/test_flight_3dof.py
index 37b529e40..9a372fb43 100644
--- a/tests/unit/simulation/test_flight_3dof.py
+++ b/tests/unit/simulation/test_flight_3dof.py
@@ -263,4 +263,6 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
     # With alignment, quaternion derivatives should be very small
     e_dot = result[6:10]
     e_dot_magnitude = sum(ed**2 for ed in e_dot) ** 0.5
-    assert e_dot_magnitude < 1e-8, "Quaternion derivatives should be very small when aligned"
+    assert e_dot_magnitude < 1e-8, (
+        "Quaternion derivatives should be very small when aligned"
+    )

From fc4228c57de17995c08b0ea15e579635091436da Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Fri, 28 Nov 2025 01:30:23 +0530
Subject: [PATCH 08/24] MNT: updating location of 3 dof doc in users index.rst

- MNT: 3 dof documentation only referred in users section/getting started

- MNT: correction of docstring in flight.py

- MNT: corrected unit_vector call when defining rotation axis in u_dot_generalized_3dof

- MNT:  docstring correction in test_flight_3dof.py

- ENH: test coverage added for anti alignment case in weathercock model to test_flight_3dof.py
---
 docs/user/index.rst                       |  4 +--
 rocketpy/simulation/flight.py             |  5 ++-
 tests/unit/simulation/test_flight_3dof.py | 44 +++++++++++++++++++++++
 3 files changed, 48 insertions(+), 5 deletions(-)

diff --git a/docs/user/index.rst b/docs/user/index.rst
index 792324dd7..ee5cb3a67 100644
--- a/docs/user/index.rst
+++ b/docs/user/index.rst
@@ -7,7 +7,7 @@ RocketPy's User Guide
 
    Installation and Requirements <installation.rst>
    First Simulation <first_simulation.rst>
-   3-DOF Simulations <three_dof_simulation.rst>
+   3 DOF Simulations and comparison <three_dof_simulation.rst>
 
 .. toctree::
    :maxdepth: 1
@@ -28,7 +28,7 @@ RocketPy's User Guide
    Air Brakes Example <airbrakes.rst>
    ../notebooks/sensors.ipynb
    ../matlab/matlab.rst
-   3 DOF Simulations and comparison<three_dof_simulation.rst>
+
 .. toctree::
    :maxdepth: 2
    :caption: Monte Carlo Simulations
diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index f8dea4401..0055bddfb 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -1810,7 +1810,7 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
         t : float
             Time in seconds.
         u : list
-            State vector: [x, y, z, vx, vy, vz, q0, q1, q2, q3, omega1, omega2, omega3].
+            State vector: [x, y, z, vx, vy, vz, e0, e1, e2, e3, omega1, omega2, omega3].
         post_processing : bool, optional
             If True, adds flight data to self variables like self.angle_of_attack.
 
@@ -1940,7 +1940,6 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                 sin_angle = min(1.0, max(-1.0, sin_angle))
 
                 # Angular velocity magnitude proportional to misalignment angle
-                # Angular velocity magnitude proportional to sin(angle)
                 omega_mag = self.weathercock_coeff * sin_angle
 
                 # Angular velocity in inertial frame
@@ -1976,7 +1975,7 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                         # If parallel, use y axis
                         y_axis = Vector([0.0, 1.0, 0.0])
                         perp_axis = body_z_inertial ^ y_axis
-                    rotation_axis = perp_axis.normalized()
+                    rotation_axis = perp_axis.unit_vector
                     # 180 degree rotation: sin(angle) = 1
                     omega_mag = self.weathercock_coeff * 1.0
                     omega_inertial = rotation_axis * omega_mag
diff --git a/tests/unit/simulation/test_flight_3dof.py b/tests/unit/simulation/test_flight_3dof.py
index 9a372fb43..2de3dd328 100644
--- a/tests/unit/simulation/test_flight_3dof.py
+++ b/tests/unit/simulation/test_flight_3dof.py
@@ -141,6 +141,7 @@ def test_invalid_simulation_mode(example_plain_env, calisto):
 
 def test_weathercock_coeff_stored(example_plain_env, point_mass_rocket):
     """Tests that the weathercock_coeff parameter is correctly stored.
+
     Parameters
     ----------
     example_plain_env : rocketpy.Environment
@@ -160,6 +161,7 @@ def test_weathercock_coeff_stored(example_plain_env, point_mass_rocket):
 
 def test_weathercock_coeff_default(example_plain_env, point_mass_rocket):
     """Tests that the default weathercock_coeff is 1.0.
+
     Parameters
     ----------
     example_plain_env : rocketpy.Environment
@@ -180,6 +182,7 @@ def test_weathercock_zero_gives_fixed_attitude(example_plain_env, point_mass_roc
     """Tests that weathercock_coeff=0 results in fixed attitude (no quaternion change).
     When weathercock_coeff is 0, the quaternion derivatives should be zero,
     meaning the attitude does not evolve.
+
     Parameters
     ----------
     example_plain_env : rocketpy.Environment
@@ -208,6 +211,7 @@ def test_weathercock_nonzero_evolves_attitude(example_plain_env, point_mass_rock
     """Tests that non-zero weathercock_coeff causes attitude evolution.
     When the body axis is misaligned with the relative wind and weathercock_coeff
     is positive, the quaternion derivatives should be non-zero.
+
     Parameters
     ----------
     example_plain_env : rocketpy.Environment
@@ -238,6 +242,7 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
     """Tests that when body axis is aligned with relative wind, no rotation occurs.
     When the rocket's body z-axis is already aligned with the negative of the
     freestream velocity, the quaternion derivatives should be approximately zero.
+
     Parameters
     ----------
     example_plain_env : rocketpy.Environment
@@ -266,3 +271,42 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
     assert e_dot_magnitude < 1e-8, (
         "Quaternion derivatives should be very small when aligned"
     )
+
+
+def test_weathercock_anti_aligned_uses_perp_axis_and_evolves(
+    example_plain_env, point_mass_rocket
+):
+    """Tests the anti-aligned case where body z-axis is opposite freestream.
+
+    This should exercise the branch that selects a perpendicular axis (y-axis)
+    when the cross with x-axis is nearly zero, producing a non-zero quaternion
+    derivative.
+    """
+    flight = Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=1.0,
+    )
+
+    sqrt2_2 = np.sqrt(2) / 2
+    # Build quaternion that makes body z-axis = [-1, 0, 0]
+    # This corresponds to a -90 deg rotation about the y-axis: e0=cos(45°), e2=-sin(45°)
+    e0 = sqrt2_2
+    e1 = 0
+    e2 = -sqrt2_2
+    e3 = 0
+
+    # State: [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
+    # Set velocity so desired_direction becomes [1,0,0]
+    u = [0, 0, 100, 50, 0, 0, e0, e1, e2, e3, 0, 0, 0]
+
+    result = flight.u_dot_generalized_3dof(0, u)
+
+    # Quaternion derivatives (indices 6-9) should be non-zero in anti-aligned case
+    e_dot = result[6:10]
+    e_dot_magnitude = sum(ed**2 for ed in e_dot) ** 0.5
+    assert e_dot_magnitude > 1e-6, (
+        "Quaternion derivatives should be non-zero for anti-aligned"
+    )

From a56439759116ead7ec02af195b0ec9fc20e74e9d Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Mon, 1 Dec 2025 00:21:07 +0530
Subject: [PATCH 09/24] DOC: three_dof_simulation.rst update to add explanation
 of weather cocking coeff usage and value.

---
 docs/user/three_dof_simulation.rst | 25 +++++++++++++++++++++++--
 1 file changed, 23 insertions(+), 2 deletions(-)

diff --git a/docs/user/three_dof_simulation.rst b/docs/user/three_dof_simulation.rst
index 432a55ecc..c90fa5298 100644
--- a/docs/user/three_dof_simulation.rst
+++ b/docs/user/three_dof_simulation.rst
@@ -356,6 +356,27 @@ restoring moments from fins and other stabilizing surfaces. The 3-DOF
 weathercocking model provides a simplified representation of this behavior
 without requiring full 6-DOF rotational dynamics.
 
+The weathercocking coefficient (``weathercock_coeff``, often abbreviated
+``wc``) represents the rate at which the rocket's body axis aligns with
+the relative wind. This simplified model does not consider aerodynamic
+surfaces (for example, fins) or compute aerodynamic torques. In a
+full 6-DOF model, weathercocking depends on quantities such as the
+static margin and the normal-force coefficient, which produce restoring
+moments that turn the rocket into the wind. A 3-DOF point-mass
+simulation cannot compute those moments, so the model enforces
+alignment of the body axis toward the freestream with a proportional
+law.
+
+Treat ``weathercock_coeff`` as a tuning parameter that approximates the
+combined effect of static stability and restoring moments. It has no
+direct physical units; designers typically select values by trial and
+error and validate them later against full 6-DOF simulations.
+
+Sources:
+
+- `Weathercocking (NASA Bottle Rocket tutorial) <https://www.grc.nasa.gov/www/k-12/VirtualAero/BottleRocket/airplane/rktcock.html>`_
+- `Rocket weather-cocking (NASA beginners guide) <https://www1.grc.nasa.gov/beginners-guide-to-aeronautics/rocket-weather-cocking/#new-flight-path>`_
+ 
 The ``weathercock_coeff`` Parameter
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
@@ -371,7 +392,7 @@ in the :class:`rocketpy.Flight` class:
         longitude=-106.974998,
         elevation=1400
     )
-    env.set_atmospheric_model(type="StandardAtmosphere")
+    env.set_atmospheric_model(type="standard_atmosphere")
 
     motor = PointMassMotor(
         thrust_source=1500,
@@ -460,7 +481,7 @@ accuracy.
         longitude=9.003336,
         elevation=407,
     )
-    env.set_atmospheric_model(type="StandardAtmosphere")
+    env.set_atmospheric_model(type="standard_atmosphere")
     env.max_expected_height = 2000
 
     # Full 6-DOF Motor

From 174efa9b7803597ecf34e2b5371b8302cd63ec0a Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Mon, 1 Dec 2025 00:36:33 +0530
Subject: [PATCH 10/24] MNT: changed default value of weather_coeff in
 flight.py and added fixtures to test_flight_3dof.py

---
 rocketpy/simulation/flight.py             |   2 +-
 tests/unit/simulation/test_flight_3dof.py | 110 ++++++++++------------
 2 files changed, 50 insertions(+), 62 deletions(-)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 0055bddfb..1c52feadf 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -492,7 +492,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         equations_of_motion="standard",
         ode_solver="LSODA",
         simulation_mode="6 DOF",
-        weathercock_coeff=1.0,
+        weathercock_coeff=None,
     ):
         """Run a trajectory simulation.
 
diff --git a/tests/unit/simulation/test_flight_3dof.py b/tests/unit/simulation/test_flight_3dof.py
index 2de3dd328..3dc3e55be 100644
--- a/tests/unit/simulation/test_flight_3dof.py
+++ b/tests/unit/simulation/test_flight_3dof.py
@@ -50,9 +50,42 @@ def point_mass_rocket(point_mass_motor):
     return rocket
 
 
-def test_simulation_mode_sets_3dof_with_point_mass_rocket(
-    example_plain_env, point_mass_rocket
-):
+@pytest.fixture
+def flight_weathercock_zero(example_plain_env, point_mass_rocket):
+    """Creates a Flight fixture with weathercock_coeff set to 0.0.
+
+    Returns
+    -------
+    rocketpy.simulation.flight.Flight
+        A Flight object configured for 3-DOF with zero weathercock coefficient.
+    """
+    return Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=0.0,
+    )
+
+
+@pytest.fixture
+def flight_3dof(example_plain_env, point_mass_rocket):
+    """Creates a standard 3-DOF Flight fixture with default weathercock_coeff=1.0.
+
+    Returns
+    -------
+    rocketpy.simulation.flight.Flight
+        A Flight object configured for 3-DOF with default weathercock coefficient.
+    """
+    return Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+    )
+
+
+def test_simulation_mode_sets_3dof_with_point_mass_rocket(flight_3dof):
     """Tests that simulation mode is correctly set to 3 DOF for PointMassRocket.
 
     Parameters
@@ -62,13 +95,7 @@ def test_simulation_mode_sets_3dof_with_point_mass_rocket(
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-    )
-    assert flight.simulation_mode == "3 DOF"
+    assert flight_3dof.simulation_mode == "3 DOF"
 
 
 def test_3dof_simulation_mode_warning(example_plain_env, point_mass_rocket):
@@ -94,9 +121,7 @@ class should emit a UserWarning and automatically switch to 3 DOF mode.
         assert flight.simulation_mode == "3 DOF"
 
 
-def test_u_dot_generalized_3dof_returns_valid_result(
-    example_plain_env, point_mass_rocket
-):
+def test_u_dot_generalized_3dof_returns_valid_result(flight_3dof):
     """Tests that 3-DOF equations of motion return valid derivative results.
 
     Verifies that the u_dot_generalized_3dof method returns a list or numpy
@@ -109,12 +134,7 @@ def test_u_dot_generalized_3dof_returns_valid_result(
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-    )
+    flight = flight_3dof
     u = [0] * 13  # Generalized state vector size
     result = flight.u_dot_generalized_3dof(0, u)
     assert isinstance(result, (list, np.ndarray))
@@ -159,7 +179,7 @@ def test_weathercock_coeff_stored(example_plain_env, point_mass_rocket):
     assert flight.weathercock_coeff == 2.5
 
 
-def test_weathercock_coeff_default(example_plain_env, point_mass_rocket):
+def test_weathercock_coeff_default(flight_3dof):
     """Tests that the default weathercock_coeff is 1.0.
 
     Parameters
@@ -169,16 +189,10 @@ def test_weathercock_coeff_default(example_plain_env, point_mass_rocket):
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-    )
-    assert flight.weathercock_coeff == 1.0
+    assert flight_3dof.weathercock_coeff == 1.0
 
 
-def test_weathercock_zero_gives_fixed_attitude(example_plain_env, point_mass_rocket):
+def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero):
     """Tests that weathercock_coeff=0 results in fixed attitude (no quaternion change).
     When weathercock_coeff is 0, the quaternion derivatives should be zero,
     meaning the attitude does not evolve.
@@ -190,13 +204,7 @@ def test_weathercock_zero_gives_fixed_attitude(example_plain_env, point_mass_roc
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-        weathercock_coeff=0.0,
-    )
+    flight = flight_weathercock_zero
     # Create a state vector with non-zero velocity (to have freestream)
     # [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
     u = [0, 0, 100, 10, 5, 50, 1, 0, 0, 0, 0, 0, 0]
@@ -207,7 +215,7 @@ def test_weathercock_zero_gives_fixed_attitude(example_plain_env, point_mass_roc
     assert all(abs(ed) < 1e-10 for ed in e_dot), "Quaternion derivatives should be zero"
 
 
-def test_weathercock_nonzero_evolves_attitude(example_plain_env, point_mass_rocket):
+def test_weathercock_nonzero_evolves_attitude(flight_3dof):
     """Tests that non-zero weathercock_coeff causes attitude evolution.
     When the body axis is misaligned with the relative wind and weathercock_coeff
     is positive, the quaternion derivatives should be non-zero.
@@ -219,13 +227,7 @@ def test_weathercock_nonzero_evolves_attitude(example_plain_env, point_mass_rock
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-        weathercock_coeff=1.0,
-    )
+    flight = flight_3dof
     # Create a state with misaligned body axis
     # Body pointing straight up (e0=1, e1=e2=e3=0) but velocity is horizontal
     # [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
@@ -238,7 +240,7 @@ def test_weathercock_nonzero_evolves_attitude(example_plain_env, point_mass_rock
     assert e_dot_magnitude > 1e-6, "Quaternion derivatives should be non-zero"
 
 
-def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
+def test_weathercock_aligned_no_evolution(flight_3dof):
     """Tests that when body axis is aligned with relative wind, no rotation occurs.
     When the rocket's body z-axis is already aligned with the negative of the
     freestream velocity, the quaternion derivatives should be approximately zero.
@@ -250,13 +252,7 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-        weathercock_coeff=1.0,
-    )
+    flight = flight_3dof
     # Body pointing in +x direction (into the wind for vx=50)
     # Quaternion for 90 degree rotation about y-axis uses half-angle:
     # e0=cos(90°/2)=cos(45°), e2=sin(90°/2)=sin(45°)
@@ -273,22 +269,14 @@ def test_weathercock_aligned_no_evolution(example_plain_env, point_mass_rocket):
     )
 
 
-def test_weathercock_anti_aligned_uses_perp_axis_and_evolves(
-    example_plain_env, point_mass_rocket
-):
+def test_weathercock_anti_aligned_uses_perp_axis_and_evolves(flight_3dof):
     """Tests the anti-aligned case where body z-axis is opposite freestream.
 
     This should exercise the branch that selects a perpendicular axis (y-axis)
     when the cross with x-axis is nearly zero, producing a non-zero quaternion
     derivative.
     """
-    flight = Flight(
-        rocket=point_mass_rocket,
-        environment=example_plain_env,
-        rail_length=1,
-        simulation_mode="3 DOF",
-        weathercock_coeff=1.0,
-    )
+    flight = flight_3dof
 
     sqrt2_2 = np.sqrt(2) / 2
     # Build quaternion that makes body z-axis = [-1, 0, 0]

From c6b8efec01fb0250021c368ce0d1f1531588216c Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Mon, 1 Dec 2025 00:47:41 +0530
Subject: [PATCH 11/24] MNT: shifting test_flight_dof.py to integration tests.

- MNT: fixed default weathercock_coeff value in flight.py to match 3 dof behaviour by default

- MNT: corrected fixtures and docstrings in test_flight_3dof.py
---
 rocketpy/simulation/flight.py                 |  7 ++--
 .../simulation/test_flight_3dof.py            | 38 ++++++++++++++-----
 2 files changed, 32 insertions(+), 13 deletions(-)
 rename tests/{unit => integration}/simulation/test_flight_3dof.py (91%)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 1c52feadf..9ed8642a4 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -492,7 +492,7 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
         equations_of_motion="standard",
         ode_solver="LSODA",
         simulation_mode="6 DOF",
-        weathercock_coeff=None,
+        weathercock_coeff=0.0,
     ):
         """Run a trajectory simulation.
 
@@ -581,8 +581,9 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
             aligns with the relative wind direction in 3-DOF simulations, in rad/s.
             A higher value means faster alignment (quasi-static weathercocking).
             This parameter is only used when simulation_mode is '3 DOF'.
-            Default is 1.0, which provides moderate alignment. Set to 0 to
-            disable weathercocking (fixed attitude).
+            Default is 0.0 to mimic a pure 3-DOF simulation without any
+            weathercocking (fixed attitude). Set to a positive value to enable
+            quasi-static weathercocking behaviour.
 
 
         Returns
diff --git a/tests/unit/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
similarity index 91%
rename from tests/unit/simulation/test_flight_3dof.py
rename to tests/integration/simulation/test_flight_3dof.py
index 3dc3e55be..72d60bea1 100644
--- a/tests/unit/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -70,7 +70,7 @@ def flight_weathercock_zero(example_plain_env, point_mass_rocket):
 
 @pytest.fixture
 def flight_3dof(example_plain_env, point_mass_rocket):
-    """Creates a standard 3-DOF Flight fixture with default weathercock_coeff=1.0.
+    """Creates a standard 3-DOF Flight fixture with default weathercock_coeff=0.0.
 
     Returns
     -------
@@ -85,6 +85,24 @@ def flight_3dof(example_plain_env, point_mass_rocket):
     )
 
 
+@pytest.fixture
+def flight_weathercock_pos(example_plain_env, point_mass_rocket):
+    """Creates a Flight fixture with weathercock_coeff set to 1.0.
+
+    Returns
+    -------
+    rocketpy.simulation.flight.Flight
+        A Flight object configured for 3-DOF with weathercocking enabled.
+    """
+    return Flight(
+        rocket=point_mass_rocket,
+        environment=example_plain_env,
+        rail_length=1,
+        simulation_mode="3 DOF",
+        weathercock_coeff=1.0,
+    )
+
+
 def test_simulation_mode_sets_3dof_with_point_mass_rocket(flight_3dof):
     """Tests that simulation mode is correctly set to 3 DOF for PointMassRocket.
 
@@ -180,7 +198,7 @@ def test_weathercock_coeff_stored(example_plain_env, point_mass_rocket):
 
 
 def test_weathercock_coeff_default(flight_3dof):
-    """Tests that the default weathercock_coeff is 1.0.
+    """Tests that the default weathercock_coeff is 0.0 (no weathercocking).
 
     Parameters
     ----------
@@ -189,7 +207,7 @@ def test_weathercock_coeff_default(flight_3dof):
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    assert flight_3dof.weathercock_coeff == 1.0
+    assert flight_3dof.weathercock_coeff == 0.0
 
 
 def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero):
@@ -201,7 +219,7 @@ def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero):
     ----------
     example_plain_env : rocketpy.Environment
         A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
+    point_mass_rocket : rocketpy.PointMassMotor
         A point mass rocket fixture for 3-DOF simulation.
     """
     flight = flight_weathercock_zero
@@ -215,7 +233,7 @@ def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero):
     assert all(abs(ed) < 1e-10 for ed in e_dot), "Quaternion derivatives should be zero"
 
 
-def test_weathercock_nonzero_evolves_attitude(flight_3dof):
+def test_weathercock_nonzero_evolves_attitude(flight_weathercock_pos):
     """Tests that non-zero weathercock_coeff causes attitude evolution.
     When the body axis is misaligned with the relative wind and weathercock_coeff
     is positive, the quaternion derivatives should be non-zero.
@@ -227,7 +245,7 @@ def test_weathercock_nonzero_evolves_attitude(flight_3dof):
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = flight_3dof
+    flight = flight_weathercock_pos
     # Create a state with misaligned body axis
     # Body pointing straight up (e0=1, e1=e2=e3=0) but velocity is horizontal
     # [x, y, z, vx, vy, vz, e0, e1, e2, e3, w1, w2, w3]
@@ -240,7 +258,7 @@ def test_weathercock_nonzero_evolves_attitude(flight_3dof):
     assert e_dot_magnitude > 1e-6, "Quaternion derivatives should be non-zero"
 
 
-def test_weathercock_aligned_no_evolution(flight_3dof):
+def test_weathercock_aligned_no_evolution(flight_weathercock_pos):
     """Tests that when body axis is aligned with relative wind, no rotation occurs.
     When the rocket's body z-axis is already aligned with the negative of the
     freestream velocity, the quaternion derivatives should be approximately zero.
@@ -252,7 +270,7 @@ def test_weathercock_aligned_no_evolution(flight_3dof):
     point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
-    flight = flight_3dof
+    flight = flight_weathercock_pos
     # Body pointing in +x direction (into the wind for vx=50)
     # Quaternion for 90 degree rotation about y-axis uses half-angle:
     # e0=cos(90°/2)=cos(45°), e2=sin(90°/2)=sin(45°)
@@ -269,14 +287,14 @@ def test_weathercock_aligned_no_evolution(flight_3dof):
     )
 
 
-def test_weathercock_anti_aligned_uses_perp_axis_and_evolves(flight_3dof):
+def test_weathercock_anti_aligned_uses_perp_axis_and_evolves(flight_weathercock_pos):
     """Tests the anti-aligned case where body z-axis is opposite freestream.
 
     This should exercise the branch that selects a perpendicular axis (y-axis)
     when the cross with x-axis is nearly zero, producing a non-zero quaternion
     derivative.
     """
-    flight = flight_3dof
+    flight = flight_weathercock_pos
 
     sqrt2_2 = np.sqrt(2) / 2
     # Build quaternion that makes body z-axis = [-1, 0, 0]

From 9afa353a2ab85ffc25c1005deeaca0b27425f137 Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Mon, 1 Dec 2025 14:52:11 +0530
Subject: [PATCH 12/24] MNT: docsrting update in test_flight_3dof.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 tests/integration/simulation/test_flight_3dof.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
index 72d60bea1..455fa339e 100644
--- a/tests/integration/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -219,7 +219,7 @@ def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero):
     ----------
     example_plain_env : rocketpy.Environment
         A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassMotor
+    point_mass_rocket : rocketpy.PointMassRocket
         A point mass rocket fixture for 3-DOF simulation.
     """
     flight = flight_weathercock_zero

From a179962f464e4e2dd5c63e67dbb7dda74632981b Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Mon, 1 Dec 2025 14:53:24 +0530
Subject: [PATCH 13/24] MNT: Update of docstring in test_flight_3dof.py

- MNT: correction of docstring now that fixtures are used.

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 tests/integration/simulation/test_flight_3dof.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
index 455fa339e..c17d95f07 100644
--- a/tests/integration/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -265,10 +265,8 @@ def test_weathercock_aligned_no_evolution(flight_weathercock_pos):
 
     Parameters
     ----------
-    example_plain_env : rocketpy.Environment
-        A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
-        A point mass rocket fixture for 3-DOF simulation.
+    flight_weathercock_pos : rocketpy.Flight
+        A Flight fixture configured for weathercocking tests with a nonzero initial angle.
     """
     flight = flight_weathercock_pos
     # Body pointing in +x direction (into the wind for vx=50)

From dc18ed841f37bbea585e8ebb8ae3172f868cbd1d Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Mon, 1 Dec 2025 14:54:13 +0530
Subject: [PATCH 14/24] DOC: Update of three_dof_simulation.rst

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 docs/user/three_dof_simulation.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/user/three_dof_simulation.rst b/docs/user/three_dof_simulation.rst
index c90fa5298..3c5f87434 100644
--- a/docs/user/three_dof_simulation.rst
+++ b/docs/user/three_dof_simulation.rst
@@ -427,7 +427,7 @@ The ``weathercock_coeff`` parameter controls the rate at which the rocket
 aligns with the relative wind:
 
 - ``weathercock_coeff=0``: No weathercocking (original fixed-attitude behavior)
-- ``weathercock_coeff=1.0``: Default moderate alignment rate
+- ``weathercock_coeff=1.0``: Moderate alignment rate
 - ``weathercock_coeff>1.0``: Faster alignment (more stable rocket)
 
 Effect of Weathercocking Coefficient

From d5b9f371aa413038110f6236d83c886e749094bb Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Mon, 1 Dec 2025 14:54:37 +0530
Subject: [PATCH 15/24] Update tests/integration/simulation/test_flight_3dof.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 tests/integration/simulation/test_flight_3dof.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
index c17d95f07..f5ef038a3 100644
--- a/tests/integration/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -217,10 +217,9 @@ def test_weathercock_zero_gives_fixed_attitude(flight_weathercock_zero):
 
     Parameters
     ----------
-    example_plain_env : rocketpy.Environment
-        A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
-        A point mass rocket fixture for 3-DOF simulation.
+    flight_weathercock_zero : rocketpy.simulation.Flight
+        A Flight fixture with weathercock_coeff set to 0. Used to verify that
+        the attitude (quaternion) does not evolve when weathercocking is disabled.
     """
     flight = flight_weathercock_zero
     # Create a state vector with non-zero velocity (to have freestream)

From 3d1a6ed074fd6a63e1f7c2ee4e9155451172c83a Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Wed, 3 Dec 2025 16:54:54 +0530
Subject: [PATCH 16/24] Docstring Update
 tests/integration/simulation/test_flight_3dof.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 tests/integration/simulation/test_flight_3dof.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
index f5ef038a3..5a5e47cc8 100644
--- a/tests/integration/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -239,10 +239,8 @@ def test_weathercock_nonzero_evolves_attitude(flight_weathercock_pos):
 
     Parameters
     ----------
-    example_plain_env : rocketpy.Environment
-        A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
-        A point mass rocket fixture for 3-DOF simulation.
+    flight_weathercock_pos : rocketpy.simulation.Flight
+        A Flight fixture with a positive weathercock coefficient for 3-DOF simulation.
     """
     flight = flight_weathercock_pos
     # Create a state with misaligned body axis

From b1e6212d142223ba8719a5771f9922be933ec0a9 Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Wed, 3 Dec 2025 16:55:11 +0530
Subject: [PATCH 17/24] docstring Update
 tests/integration/simulation/test_flight_3dof.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 tests/integration/simulation/test_flight_3dof.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
index 5a5e47cc8..92ff7dc4f 100644
--- a/tests/integration/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -202,10 +202,8 @@ def test_weathercock_coeff_default(flight_3dof):
 
     Parameters
     ----------
-    example_plain_env : rocketpy.Environment
-        A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
-        A point mass rocket fixture for 3-DOF simulation.
+    flight_3dof : rocketpy.Flight
+        A Flight object for a 3-DOF simulation, provided by the flight_3dof fixture.
     """
     assert flight_3dof.weathercock_coeff == 0.0
 

From 8b35366584eb95cab76bb9953e0a275e9aa4df9c Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Wed, 3 Dec 2025 16:55:23 +0530
Subject: [PATCH 18/24] docstring Update docs/user/three_dof_simulation.rst

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 docs/user/three_dof_simulation.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/user/three_dof_simulation.rst b/docs/user/three_dof_simulation.rst
index 3c5f87434..b66a35f92 100644
--- a/docs/user/three_dof_simulation.rst
+++ b/docs/user/three_dof_simulation.rst
@@ -448,7 +448,7 @@ relative wind. This affects the lateral motion and impact point:
      - Original 3-DOF behavior
    * - 1.0
      - Moderate
-     - Default, general purpose
+     - General purpose
    * - 2.0-5.0
      - Fast
      - Highly stable rockets

From f3f3b0acb19a8245ef720443626ef1c21e4f4f17 Mon Sep 17 00:00:00 2001
From: Ishan <99824864+aZira371@users.noreply.github.com>
Date: Thu, 4 Dec 2025 02:09:54 +0530
Subject: [PATCH 19/24] Docstring Update rocketpy/simulation/flight.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 rocketpy/simulation/flight.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 9ed8642a4..e1b770fd1 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -577,13 +577,13 @@ def __init__(  # pylint: disable=too-many-arguments,too-many-statements
             For more information on the integration methods, see the scipy
             documentation [1]_.
         weathercock_coeff : float, optional
-            Coefficient that controls the rate at which the rocket's body axis
-            aligns with the relative wind direction in 3-DOF simulations, in rad/s.
-            A higher value means faster alignment (quasi-static weathercocking).
-            This parameter is only used when simulation_mode is '3 DOF'.
-            Default is 0.0 to mimic a pure 3-DOF simulation without any
-            weathercocking (fixed attitude). Set to a positive value to enable
-            quasi-static weathercocking behaviour.
+            Proportionality coefficient (rate coefficient) for the alignment rate of the rocket's body axis
+            with the relative wind direction in 3-DOF simulations, in rad/s. The actual angular velocity
+            applied to align the rocket is calculated as ``weathercock_coeff * sin(angle)``, where ``angle``
+            is the angle between the rocket's axis and the wind direction. A higher value means faster alignment
+            (quasi-static weathercocking). This parameter is only used when simulation_mode is '3 DOF'.
+            Default is 0.0 to mimic a pure 3-DOF simulation without any weathercocking (fixed attitude).
+            Set to a positive value to enable quasi-static weathercocking behaviour.
 
 
         Returns

From 90acba7373f24d5ac156c97ea3b8db280f777403 Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Thu, 4 Dec 2025 02:14:58 +0530
Subject: [PATCH 20/24] MNT: Docstring updates to various files related to 3
 dof

---
 docs/user/index.rst                              |  2 +-
 docs/user/three_dof_simulation.rst               |  2 +-
 tests/integration/simulation/test_flight_3dof.py | 12 ++----------
 3 files changed, 4 insertions(+), 12 deletions(-)

diff --git a/docs/user/index.rst b/docs/user/index.rst
index ee5cb3a67..5afaee3a6 100644
--- a/docs/user/index.rst
+++ b/docs/user/index.rst
@@ -7,7 +7,7 @@ RocketPy's User Guide
 
    Installation and Requirements <installation.rst>
    First Simulation <first_simulation.rst>
-   3 DOF Simulations and comparison <three_dof_simulation.rst>
+   3 DOF Simulations and Comparison <three_dof_simulation.rst>
 
 .. toctree::
    :maxdepth: 1
diff --git a/docs/user/three_dof_simulation.rst b/docs/user/three_dof_simulation.rst
index b66a35f92..dd318af0e 100644
--- a/docs/user/three_dof_simulation.rst
+++ b/docs/user/three_dof_simulation.rst
@@ -418,7 +418,7 @@ in the :class:`rocketpy.Flight` class:
         inclination=85,
         heading=45,
         simulation_mode="3 DOF",
-        weathercock_coeff=1.0,  # Default value
+        weathercock_coeff=1.0,  # Example with weathercocking enabled
     )
 
     print(f"Apogee: {flight.apogee - env.elevation:.2f} m")
diff --git a/tests/integration/simulation/test_flight_3dof.py b/tests/integration/simulation/test_flight_3dof.py
index 92ff7dc4f..ef4c35c14 100644
--- a/tests/integration/simulation/test_flight_3dof.py
+++ b/tests/integration/simulation/test_flight_3dof.py
@@ -108,10 +108,8 @@ def test_simulation_mode_sets_3dof_with_point_mass_rocket(flight_3dof):
 
     Parameters
     ----------
-    example_plain_env : rocketpy.Environment
-        A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
-        A point mass rocket fixture for 3-DOF simulation.
+    flight_3dof : rocketpy.simulation.flight.Flight
+        A Flight fixture configured for 3-DOF simulation with a PointMassRocket.
     """
     assert flight_3dof.simulation_mode == "3 DOF"
 
@@ -145,12 +143,6 @@ def test_u_dot_generalized_3dof_returns_valid_result(flight_3dof):
     Verifies that the u_dot_generalized_3dof method returns a list or numpy
     array representing the state derivative vector.
 
-    Parameters
-    ----------
-    example_plain_env : rocketpy.Environment
-        A basic environment fixture for flight simulation.
-    point_mass_rocket : rocketpy.PointMassRocket
-        A point mass rocket fixture for 3-DOF simulation.
     """
     flight = flight_3dof
     u = [0] * 13  # Generalized state vector size

From b309d5e929927f3fb238d9b8786da581356224bd Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Thu, 4 Dec 2025 02:19:24 +0530
Subject: [PATCH 21/24] MNT: unit vector edge case check in flight.py

- MNT: perp_axis singularity value error implemented to handle edge case for perp_axis being parallel to body axis in weathercocking model of 3 dof.
---
 rocketpy/simulation/flight.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index e1b770fd1..81d2c48d6 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -1976,6 +1976,12 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                         # If parallel, use y axis
                         y_axis = Vector([0.0, 1.0, 0.0])
                         perp_axis = body_z_inertial ^ y_axis
+                        if abs(perp_axis) < 1e-6:
+                            # If still parallel, raise an error or choose a default axis
+                            raise ValueError(
+                                "Cannot determine a valid rotation axis: "
+                                "body_z_inertial is parallel to both x and y axes."
+                            )
                     rotation_axis = perp_axis.unit_vector
                     # 180 degree rotation: sin(angle) = 1
                     omega_mag = self.weathercock_coeff * 1.0

From b1eb6cb8acb382a4af82bb307987c9e1a9f737f3 Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Thu, 4 Dec 2025 02:33:36 +0530
Subject: [PATCH 22/24] DOC: Add note about motor file paths in 3-DOF
 comparison section (#902)

* Initial plan

* DOC: Add note about motor file paths in 3-DOF comparison section

Co-authored-by: aZira371 <99824864+aZira371@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: aZira371 <99824864+aZira371@users.noreply.github.com>
---
 docs/user/three_dof_simulation.rst | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/docs/user/three_dof_simulation.rst b/docs/user/three_dof_simulation.rst
index dd318af0e..3ac88dca0 100644
--- a/docs/user/three_dof_simulation.rst
+++ b/docs/user/three_dof_simulation.rst
@@ -464,6 +464,15 @@ with 3-DOF simulations using ``PointMassRocket`` and different weathercocking
 coefficients. This demonstrates the trade-off between computational speed and
 accuracy.
 
+.. note::
+
+    The thrust curve files used in this example (e.g., ``AeroTech_K828FJ.eng``)
+    are included in the RocketPy repository under the ``data/motors/`` directory.
+    If you are running this code outside of the repository, you can download the
+    motor files from `RocketPy's data/motors folder on GitHub
+    <https://github.com/RocketPy-Team/RocketPy/tree/master/data/motors>`_ or use
+    your own thrust curve files.
+
 **Setup the simulations:**
 
 .. jupyter-execute::

From 70c6b5f9c1105e3bed10e53b5a431102b94c7564 Mon Sep 17 00:00:00 2001
From: Copilot <198982749+Copilot@users.noreply.github.com>
Date: Thu, 4 Dec 2025 03:34:44 +0530
Subject: [PATCH 23/24] MNT: eliminate quaternion derivative code duplication
 in u_dot_generalized_3dof (#903)

* Initial plan

* Refactor: eliminate quaternion derivative code duplication in u_dot_generalized_3dof

Co-authored-by: aZira371 <99824864+aZira371@users.noreply.github.com>

* Improve comment clarity in weathercocking aligned case

Co-authored-by: aZira371 <99824864+aZira371@users.noreply.github.com>

---------

Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: aZira371 <99824864+aZira371@users.noreply.github.com>
---
 rocketpy/simulation/flight.py | 75 +++++++++++++----------------------
 1 file changed, 27 insertions(+), 48 deletions(-)

diff --git a/rocketpy/simulation/flight.py b/rocketpy/simulation/flight.py
index 81d2c48d6..0cf1e23ab 100644
--- a/rocketpy/simulation/flight.py
+++ b/rocketpy/simulation/flight.py
@@ -1929,55 +1929,33 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
             rotation_axis = body_z_inertial ^ desired_direction
             rotation_axis_mag = abs(rotation_axis)
 
+            # Determine omega_body based on alignment state
+            omega_body = None
+
             if rotation_axis_mag > 1e-8:
-                # Normalize rotation axis
+                # Normal case: compute angular velocity from misalignment
                 rotation_axis = rotation_axis / rotation_axis_mag
 
                 # The magnitude of the cross product of two unit vectors equals
                 # the sine of the angle between them
-                sin_angle = rotation_axis_mag
-
-                # Clamp sin_angle to valid range
-                sin_angle = min(1.0, max(-1.0, sin_angle))
+                sin_angle = min(1.0, max(-1.0, rotation_axis_mag))
 
                 # Angular velocity magnitude proportional to misalignment angle
                 omega_mag = self.weathercock_coeff * sin_angle
 
-                # Angular velocity in inertial frame
-                omega_inertial = rotation_axis * omega_mag
-
-                # Transform angular velocity to body frame
-                omega_body = Kt @ omega_inertial
-
-                # Quaternion derivative from angular velocity in body frame
-                omega1_wc, omega2_wc, omega3_wc = (
-                    omega_body.x,
-                    omega_body.y,
-                    omega_body.z,
-                )
-                e0_dot = 0.5 * (-omega1_wc * e1 - omega2_wc * e2 - omega3_wc * e3)
-                e1_dot = 0.5 * (omega1_wc * e0 + omega3_wc * e2 - omega2_wc * e3)
-                e2_dot = 0.5 * (omega2_wc * e0 - omega3_wc * e1 + omega1_wc * e3)
-                e3_dot = 0.5 * (omega3_wc * e0 + omega2_wc * e1 - omega1_wc * e2)
-                e_dot = [e0_dot, e1_dot, e2_dot, e3_dot]
-                w_dot = [0, 0, 0]  # No angular acceleration in 3DOF model
+                # Angular velocity in inertial frame, then transform to body frame
+                omega_body = Kt @ (rotation_axis * omega_mag)
             else:
                 # Check if aligned or anti-aligned using dot product
-                dot = body_z_inertial @ desired_direction  # Vector dot product
-                if dot > 0.999:  # Aligned
-                    e_dot = [0, 0, 0, 0]
-                    w_dot = [0, 0, 0]
-                elif dot < -0.999:  # Anti-aligned
+                dot = body_z_inertial @ desired_direction
+                if dot < -0.999:  # Anti-aligned
                     # Choose an arbitrary perpendicular axis
-                    # Try [1,0,0] unless body_z_inertial is parallel to it
                     x_axis = Vector([1.0, 0.0, 0.0])
                     perp_axis = body_z_inertial ^ x_axis
                     if abs(perp_axis) < 1e-6:
-                        # If parallel, use y axis
                         y_axis = Vector([0.0, 1.0, 0.0])
                         perp_axis = body_z_inertial ^ y_axis
                         if abs(perp_axis) < 1e-6:
-                            # If still parallel, raise an error or choose a default axis
                             raise ValueError(
                                 "Cannot determine a valid rotation axis: "
                                 "body_z_inertial is parallel to both x and y axes."
@@ -1985,23 +1963,24 @@ def u_dot_generalized_3dof(self, t, u, post_processing=False):
                     rotation_axis = perp_axis.unit_vector
                     # 180 degree rotation: sin(angle) = 1
                     omega_mag = self.weathercock_coeff * 1.0
-                    omega_inertial = rotation_axis * omega_mag
-                    omega_body = Kt @ omega_inertial
-                    omega1_wc, omega2_wc, omega3_wc = (
-                        omega_body.x,
-                        omega_body.y,
-                        omega_body.z,
-                    )
-                    e0_dot = 0.5 * (-omega1_wc * e1 - omega2_wc * e2 - omega3_wc * e3)
-                    e1_dot = 0.5 * (omega1_wc * e0 + omega3_wc * e2 - omega2_wc * e3)
-                    e2_dot = 0.5 * (omega2_wc * e0 - omega3_wc * e1 + omega1_wc * e3)
-                    e3_dot = 0.5 * (omega3_wc * e0 + omega2_wc * e1 - omega1_wc * e2)
-                    e_dot = [e0_dot, e1_dot, e2_dot, e3_dot]
-                    w_dot = [0, 0, 0]
-                else:
-                    # Vectors are nearly aligned, treat as aligned
-                    e_dot = [0, 0, 0, 0]
-                    w_dot = [0, 0, 0]
+                    omega_body = Kt @ (rotation_axis * omega_mag)
+                # else: aligned (dot > 0.999) - no rotation needed, omega_body stays None
+
+            # Compute quaternion derivatives from omega_body
+            if omega_body is not None:
+                omega1_wc, omega2_wc, omega3_wc = (
+                    omega_body.x,
+                    omega_body.y,
+                    omega_body.z,
+                )
+                e0_dot = 0.5 * (-omega1_wc * e1 - omega2_wc * e2 - omega3_wc * e3)
+                e1_dot = 0.5 * (omega1_wc * e0 + omega3_wc * e2 - omega2_wc * e3)
+                e2_dot = 0.5 * (omega2_wc * e0 - omega3_wc * e1 + omega1_wc * e3)
+                e3_dot = 0.5 * (omega3_wc * e0 + omega2_wc * e1 - omega1_wc * e2)
+                e_dot = [e0_dot, e1_dot, e2_dot, e3_dot]
+            else:
+                e_dot = [0, 0, 0, 0]
+            w_dot = [0, 0, 0]  # No angular acceleration in 3DOF model
         else:
             # No weathercocking or negligible freestream speed
             e_dot = [0, 0, 0, 0]

From 3693e5e89a84b3e684138cf7d630b23f50b57867 Mon Sep 17 00:00:00 2001
From: Ishan <dubeyishan371@gmail.com>
Date: Thu, 4 Dec 2025 03:48:04 +0530
Subject: [PATCH 24/24] DOC: CHANGELOG.md update to reflect implementation of 3
 dof lateral motion improvement

---
 CHANGELOG.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 73596780f..75e20416e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -32,6 +32,7 @@ Attention: The newest changes should be on top -->
 
 ### Added
 
+- ENH: 3-dof lateral motion improvement [#883](https://github.com/RocketPy-Team/RocketPy/pull/883)
 - ENH: Add multi-dimensional drag coefficient support (Cd as function of M, Re, α) [#875](https://github.com/RocketPy-Team/RocketPy/pull/875)
 - ENH: Add save functionality to `_MonteCarloPlots.all` method [#848](https://github.com/RocketPy-Team/RocketPy/pull/848)
 - ENH: add animations for motor propellant mass and tank fluid volumes [#894](https://github.com/RocketPy-Team/RocketPy/pull/894)