{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Dynamic Derivatives Using Sliding Interfaces\n",
    "\n",
    "This notebook demonstrates how to obtain **dynamic stability derivatives** by simulating unsteady flow over a NACA0012 wing that pitches back and forth within a **sliding interface**.\n",
    "\n",
    "### Geometry\n",
    "The wing geometry has:\n",
    "- **Chord** $c$ = 1 m\n",
    "- **Span** $b$ = 2 m  \n",
    "- **Reference area** $A_{ref}$ = 2 m²\n",
    "- **Moment length** = [$b$/2, $c$, $b$/2] = [1, 1, 1] m\n",
    "- **Moment center** = [0, 0, 0] (at quarter-chord, midspan)\n",
    "\n",
    "\n",
    "### Simulation Strategy\n",
    "**Steady Initialization:**\n",
    "- Ramp CFL from 1 to 100 over 1000 steps for robust convergence\n",
    "- Fixed sliding interface (zero angular velocity) to establish baseline flow\n",
    "- Maximum 10000 steps to ensure convergence\n",
    "\n",
    "**Unsteady Oscillation:**\n",
    "- Sliding interface updated to oscillating motion\n",
    "- Sinusoidal pitching with amplitude 2° and frequency 2.5 flowthru/rad\n",
    "- 400 time steps (4 complete cycles)\n",
    "\n",
    "**Note:** The settings in this example are by no means a validation setup; they are crafted to showcase the capabilities of Flow360 and we have intentionally reduced node count and example FC cost. For rigorous validation, modify the settings as needed."
   ],
   "id": "f3e3d083"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Download tutorial files"
   ],
   "id": "2975873a"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import math\n",
    "import flow360 as fl\n",
    "from flow360.examples import TutorialDynamicDerivatives\n",
    "\n",
    "TutorialDynamicDerivatives.get_files()"
   ],
   "execution_count": 1,
   "outputs": [],
   "id": "9e2d9175"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The downloaded files include the geometry (CSM format) for the NACA0012 wing."
   ],
   "id": "f6955b20"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Create project from geometry"
   ],
   "id": "a8f48cc8"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "project = fl.Project.from_geometry(\n",
    "    TutorialDynamicDerivatives.geometry,\n",
    "    name=\"Tutorial Calculating Dynamic Derivatives using Sliding Interfaces from Python\",\n",
    ")\n",
    "geometry = project.geometry\n",
    "geometry.group_faces_by_tag(\"faceName\")"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "display_data",
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:06] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Geometry successfully submitted:                                                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Geometry                                                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Tutorial Calculating Dynamic Derivatives using Sliding Interfaces from Python     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        id          = geo-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">e863d8e0-f3b6-4dd9-ba79-ff8cb38d3272</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = uploaded                                                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">9f2dbc33-dc46-4fa8-a385-82b4f61cc317</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
       "</pre>\n"
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       "\u001b[2;36m[09:57:06]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Geometry successfully submitted:                                                                  \n",
       "\u001b[2;36m           \u001b[0m        type        = Geometry                                                                          \n",
       "\u001b[2;36m           \u001b[0m        name        = Tutorial Calculating Dynamic Derivatives using Sliding Interfaces from Python     \n",
       "\u001b[2;36m           \u001b[0m        id          = geo-\u001b[93me863d8e0-f3b6-4dd9-ba79-ff8cb38d3272\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m9f2dbc33-dc46-4fa8-a385-82b4f61cc317\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Waiting for geometry to be processed.                                                             \n",
       "</pre>\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Waiting for geometry to be processed.                                                             \n"
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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   "id": "94607f9b"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The wing is enclosed in a cylindrical sliding interface as shown below:\n",
    "\n",
    "![Wing in Sliding Interface](figures/wingInSlidingInterface.png)\n",
    "\n",
    "*Figure: Wing enclosed in cylindrical sliding interface. The interface rotates about the y-axis, causing the wing to pitch.*"
   ],
   "id": "9d3c384c"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Define sliding interface and meshing parameters\n",
    "\n",
    "The wing is enclosed in a cylindrical sliding interface:\n",
    "- **Radius** = 1.0 m\n",
    "- **Height** = 2.5 m\n",
    "- **Rotation center** = [0, 0, 0]\n",
    "- **Rotation axis** = y-axis [0, 1, 0]"
   ],
   "id": "291b9eab"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    cylinder = fl.Cylinder(\n",
    "        name=\"cylinder\",\n",
    "        axis=[0, 1, 0],\n",
    "        center=[0, 0, 0],\n",
    "        inner_radius=0,\n",
    "        outer_radius=1.0,\n",
    "        height=2.5,\n",
    "    )\n",
    "    sliding_interface = fl.RotationVolume(\n",
    "        spacing_axial=0.04,\n",
    "        spacing_radial=0.04,\n",
    "        spacing_circumferential=0.04,\n",
    "        entities=cylinder,\n",
    "        enclosed_entities=geometry[\"wing\"],\n",
    "    )\n",
    "    farfield = fl.AutomatedFarfield(name=\"farfield\")"
   ],
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:33] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09:57:33]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "fa7349a6"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Key meshing parameters:\n",
    "- Automated meshing with sliding interface and farfield zones\n",
    "- Refined leading and trailing edges for accuracy\n",
    "- Boundary layer resolved with first layer thickness of 1e-6 m"
   ],
   "id": "768ab93d"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    meshing_params = fl.MeshingParams(\n",
    "        defaults=fl.MeshingDefaults(\n",
    "            surface_max_edge_length=0.03 * fl.u.m,\n",
    "            curvature_resolution_angle=8 * fl.u.deg,\n",
    "            surface_edge_growth_rate=1.15,\n",
    "            boundary_layer_first_layer_thickness=1e-6,\n",
    "            boundary_layer_growth_rate=1.15,\n",
    "        ),\n",
    "        refinement_factor=1.0,\n",
    "        volume_zones=[sliding_interface, farfield],\n",
    "        refinements=[\n",
    "            fl.SurfaceEdgeRefinement(\n",
    "                name=\"leadingEdge\",\n",
    "                method=fl.AngleBasedRefinement(value=1 * fl.u.degree),\n",
    "                edges=geometry[\"leadingEdge\"],\n",
    "            ),\n",
    "            fl.SurfaceEdgeRefinement(\n",
    "                name=\"trailingEdge\",\n",
    "                method=fl.HeightBasedRefinement(value=0.001),\n",
    "                edges=geometry[\"trailingEdge\"],\n",
    "            ),\n",
    "        ],\n",
    "    )"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "da9fadb9"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Run steady case to initialize the flow field\n",
    "\n",
    "First, we run a steady case with a fixed sliding interface (zero angular velocity) to initialize the flow field. This provides a converged solution to start the unsteady simulation."
   ],
   "id": "10bec194"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    params = fl.SimulationParams(\n",
    "        meshing=meshing_params,\n",
    "        reference_geometry=fl.ReferenceGeometry(\n",
    "            moment_center=[0, 0, 0],\n",
    "            moment_length=[1, 1, 1],\n",
    "            area=2,\n",
    "        ),\n",
    "        operating_condition=fl.AerospaceCondition(\n",
    "            velocity_magnitude=50,\n",
    "        ),\n",
    "        time_stepping=fl.Steady(\n",
    "            max_steps=10000,\n",
    "            CFL=fl.RampCFL(initial=1, final=100, ramp_steps=1000),\n",
    "        ),\n",
    "        outputs=[\n",
    "            fl.VolumeOutput(name=\"VolumeOutput\", output_fields=[\"Mach\"]),\n",
    "            fl.SurfaceOutput(\n",
    "                name=\"SurfaceOutput\",\n",
    "                surfaces=geometry[\"*\"],\n",
    "                output_fields=[\"Cp\", \"CfVec\"],\n",
    "            ),\n",
    "        ],\n",
    "        models=[\n",
    "            fl.Rotation(\n",
    "                volumes=cylinder,\n",
    "                spec=fl.AngularVelocity(0 * fl.u.rad / fl.u.s),\n",
    "            ),\n",
    "            fl.Freestream(surfaces=farfield.farfield, name=\"Freestream\"),\n",
    "            fl.Wall(surfaces=geometry[\"wing\"], name=\"NoSlipWall\"),\n",
    "            fl.Fluid(\n",
    "                navier_stokes_solver=fl.NavierStokesSolver(\n",
    "                    absolute_tolerance=1e-9,\n",
    "                    linear_solver=fl.LinearSolver(max_iterations=35),\n",
    "                ),\n",
    "                turbulence_model_solver=fl.SpalartAllmaras(\n",
    "                    absolute_tolerance=1e-8,\n",
    "                    linear_solver=fl.LinearSolver(max_iterations=25),\n",
    "                ),\n",
    "            ),\n",
    "        ],\n",
    "    )\n",
    "\n",
    "project.run_case(params=params, name=\"Tutorial Dynamic Derivatives - Steady Initialization\")\n",
    "\n",
    "steady_case = fl.Case.from_cloud(project.case.id)"
   ],
   "execution_count": 5,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
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     "metadata": {}
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:36] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Successfully submitted:                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Case                                                                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Tutorial Dynamic Derivatives - Steady Initialization                              \n",
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       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = pending                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">9f2dbc33-dc46-4fa8-a385-82b4f61cc317</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
       "</pre>\n"
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       "\u001b[2;36m[09:57:36]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Successfully submitted:                                                                           \n",
       "\u001b[2;36m           \u001b[0m        type        = Case                                                                              \n",
       "\u001b[2;36m           \u001b[0m        name        = Tutorial Dynamic Derivatives - Steady Initialization                              \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93mfc35883b-74e5-4ac9-9697-17e0a3e6a1bc\u001b[0m                                         \n",
       "\u001b[2;36m           \u001b[0m        status      = pending                                                                           \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m9f2dbc33-dc46-4fa8-a385-82b4f61cc317\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
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   "metadata": {},
   "source": [
    "## 5. Understanding the Oscillation Setup\n",
    "\n",
    "The sliding interface oscillates according to the angle expression:\n",
    "\n",
    "$$\\theta = A \\sin(\\omega_\\text{non-dim} \\cdot t_\\text{non-dim})$$\n",
    "\n",
    "Where:\n",
    "- $A$ = 0.0349066 rad (equivalent to 2°) - amplitude of oscillation\n",
    "- $\\omega_\\text{non-dim}$ = nondimensional angular frequency\n",
    "- $t_\\text{non-dim}$ = nondimensional time\n",
    "\n",
    "**Calculating the nondimensional frequency:**\n",
    "\n",
    "We want the wing to complete one oscillation cycle over a specific number of flowthroughs. In this example, we use $n$ = 2.5 flowthru/rad, meaning when $\\omega \\cdot t$ changes by 1 radian, the air flows through 2.5 chord lengths.\n",
    "\n",
    "Given:\n",
    "- Freestream velocity $U_\\infty$ = 50 m/s\n",
    "- Chord $c$ = 1 m\n",
    "- Speed of sound $C_\\infty$ = 340.294 m/s\n",
    "- Grid unit $L_\\text{gridUnit}$ = 1 m\n",
    "\n",
    "Physical angular frequency:\n",
    "$$\\omega_{physical} = \\frac{U_\\infty}{n \\cdot c} = \\frac{50}{2.5 \\times 1} = 20 \\text{ rad/s}$$\n",
    "\n",
    "Nondimensional angular frequency:\n",
    "$$\\omega = \\frac{\\omega_{physical}}{C_\\infty / L_{gridUnit}} = \\frac{20}{340.294} \\approx 0.05877271$$\n",
    "\n",
    "**Time step size:**\n",
    "\n",
    "We split each oscillation period into 100 steps:\n",
    "$$\\Delta t = 0.01 \\cdot \\frac{2\\pi}{\\omega} \\approx 0.01 \\cdot \\frac{2\\pi}{0.05877271}$$"
   ],
   "id": "cc628979"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Run unsteady case with oscillating interface\n",
    "\n",
    "Now we update the simulation parameters for the unsteady case and fork from the steady solution. Only the time stepping and rotation spec need to be modified."
   ],
   "id": "1d87d576"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Update parameters for unsteady case\n",
    "with fl.SI_unit_system:\n",
    "    params.time_stepping = fl.Unsteady(\n",
    "        max_pseudo_steps=80,\n",
    "        steps=400,\n",
    "        step_size=0.01 * 2.0 * math.pi / 20.0 * fl.u.s,\n",
    "        CFL=fl.RampCFL(initial=1, final=1e8, ramp_steps=20),\n",
    "    )\n",
    "    params.models[0].spec = fl.AngleExpression(\"0.0349066 * sin(0.05877271 * t)\")\n",
    "\n",
    "# Run unsteady case forked from steady solution\n",
    "project.run_case(\n",
    "    params=params,\n",
    "    name=\"Tutorial Dynamic Derivatives - Unsteady Oscillation\",\n",
    "    fork_from=steady_case,\n",
    ")\n",
    "unsteady_case = project.case"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:37] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09:57:37]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
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     "metadata": {}
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:38] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Successfully submitted:                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Case                                                                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Tutorial Dynamic Derivatives - Unsteady Oscillation                               \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        id          = case-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">e040a828-b521-432a-8402-5e1c58dcb612</span>                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = pending                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">9f2dbc33-dc46-4fa8-a385-82b4f61cc317</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
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       "\u001b[2;36m[09:57:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Successfully submitted:                                                                           \n",
       "\u001b[2;36m           \u001b[0m        type        = Case                                                                              \n",
       "\u001b[2;36m           \u001b[0m        name        = Tutorial Dynamic Derivatives - Unsteady Oscillation                               \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93me040a828-b521-432a-8402-5e1c58dcb612\u001b[0m                                         \n",
       "\u001b[2;36m           \u001b[0m        status      = pending                                                                           \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m9f2dbc33-dc46-4fa8-a385-82b4f61cc317\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
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   "id": "mns0hslu80b"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Postprocessing - Extract Dynamic Derivatives\n",
    "\n",
    "Once the simulation is complete, we download the results and calculate the dynamic derivatives. The key output is the `total_forces` data which contains force and moment coefficients at each time step."
   ],
   "id": "654e33dd"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import flow360 as fl\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "cmap = plt.colormaps[\"tab10\"]\n",
    "\n",
    "# Load completed example case from the example library (flow360 25.8 or above)\n",
    "project = fl.Project.from_example(by_name=\"Dynamic Derivatives\")\n",
    "case = project.get_case()\n",
    "\n",
    "# Note that in this example we're loading a pre-computed unsteady case from the example library,\n",
    "# so the reader doesn't need to wait for the simulation to finish.\n",
    "# For production usage, the user can use:\n",
    "# case = unsteady_case\n",
    "# or:\n",
    "# case = fl.Case.from_cloud(\"your-case-id\")\n",
    "\n",
    "# Download and load results\n",
    "case.results.total_forces.download(to_file=\"total_forces.csv\")\n",
    "case.results.total_forces.load_from_local(\"total_forces.csv\")\n",
    "total_forces = case.results.total_forces.as_dataframe()"
   ],
   "execution_count": 7,
   "outputs": [
    {
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Found closest match for <span style=\"color: #008000; text-decoration-color: #008000\">'Dynamic Derivatives'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Calculating Dynamic Derivatives using Sliding </span>    \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008000; text-decoration-color: #008000\">Interfaces'</span> <span style=\"font-weight: bold\">(</span>similarity: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">50.67</span> %<span style=\"font-weight: bold\">)</span>                                                                       \n",
       "</pre>\n"
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Found closest match for \u001b[32m'Dynamic Derivatives'\u001b[0m: \u001b[32m'Calculating Dynamic Derivatives using Sliding \u001b[0m    \n",
       "\u001b[2;36m           \u001b[0m\u001b[32mInterfaces'\u001b[0m \u001b[1m(\u001b[0msimilarity: \u001b[1;36m50.67\u001b[0m %\u001b[1m)\u001b[0m                                                                       \n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:39] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Copy operation started for project prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">0ad2a0d2-873f-43cc-b38a-a4e4e7b7268c</span>. Waiting for          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>completion<span style=\"color: #808000; text-decoration-color: #808000\">...</span>                                                                                           \n",
       "</pre>\n"
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       "\u001b[2;36m[09:57:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Copy operation started for project prj-\u001b[93m0ad2a0d2-873f-43cc-b38a-a4e4e7b7268c\u001b[0m. Waiting for          \n",
       "\u001b[2;36m           \u001b[0mcompletion\u001b[33m...\u001b[0m                                                                                           \n"
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       "</pre>\n"
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      "text/plain": [
       "\u001b[2;36m[09:57:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Copy operation completed successfully.                                                            \n"
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       "Output()"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:53] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Saved to total_forces.csv                                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09:57:53]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to total_forces.csv                                                                         \n"
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     "metadata": {}
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   ],
   "id": "8a53583d"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract unique physical steps\n",
    "\n",
    "The `total_forces` data contains multiple rows per physical step (one per pseudo-step). We extract the final converged value at each physical step."
   ],
   "id": "15ee4d05"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "def extract_unique_physical_steps(input_df):\n",
    "    \"\"\"Extract the row at the last pseudo step at the end of each physical step.\"\"\"\n",
    "    physical_steps_raw = input_df[\"physical_step\"]\n",
    "    n_rows = len(physical_steps_raw)\n",
    "    selected_row_index = []\n",
    "    for i in range(n_rows - 1):\n",
    "        if physical_steps_raw.iloc[i + 1] > physical_steps_raw.iloc[i]:\n",
    "            selected_row_index.append(i)\n",
    "    selected_row_index.append(n_rows - 1)\n",
    "    output = pd.DataFrame()\n",
    "    for k, v in input_df.items():\n",
    "        output[k] = [v.iloc[j] for j in selected_row_index]\n",
    "    return output.reset_index(drop=True)\n",
    "\n",
    "\n",
    "total_forces = extract_unique_physical_steps(total_forces)\n",
    "print(f\"Number of physical steps: {len(total_forces)}\")"
   ],
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Number of physical steps: 400\n"
     ],
     "name": "stdout"
    }
   ],
   "id": "90417cd6"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate nondimensional time, angle of attack, and pitch rate\n",
    "\n",
    "We compute:\n",
    "- Nondimensional time from physical steps\n",
    "- Angle of attack $\\alpha = A \\sin(\\omega t)$\n",
    "- Pitch rate $\\dot{\\alpha}$ using central differences, converted to rad/flowthru"
   ],
   "id": "a11af986"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Define simulation parameters\n",
    "length_unit = case.params.private_attribute_asset_cache.project_length_unit  # m\n",
    "U_inf = case.params.operating_condition.velocity_magnitude  # m/s\n",
    "C_inf = 340.29400580821283 * fl.u.m / fl.u.s  # speed of sound\n",
    "n_chord_per_period = 2.5  # flowthru/rad\n",
    "chord = 1 * fl.u.m\n",
    "\n",
    "omega_physical = U_inf / (n_chord_per_period * chord)  # 1/s\n",
    "omega_nondim = omega_physical / (C_inf / length_unit)\n",
    "time_step_size_nondim = 0.01 * 2 * np.pi / omega_nondim  # nondim\n",
    "\n",
    "amplitude_deg = 2 * fl.u.deg\n",
    "amplitude_rad = amplitude_deg.to(fl.u.rad)\n",
    "\n",
    "\n",
    "def compute_time_nondim(df):\n",
    "    \"\"\"Compute nondimensional time from physical step.\"\"\"\n",
    "    n_steps = len(df[\"physical_step\"])\n",
    "    df[\"time_nondim\"] = np.zeros(n_steps)\n",
    "    for i in range(n_steps):\n",
    "        df.loc[i, \"time_nondim\"] = time_step_size_nondim * (df.loc[i, \"physical_step\"] + 1)\n",
    "\n",
    "\n",
    "def compute_alpha_dot(df):\n",
    "    \"\"\"Compute angle of attack and pitch rate.\"\"\"\n",
    "    n_steps = len(df[\"physical_step\"])\n",
    "    df[\"alpha\"] = np.zeros(n_steps)\n",
    "    df[\"alpha_dot\"] = np.zeros(n_steps)\n",
    "    alpha_eq_0_index = []\n",
    "\n",
    "    # Compute alpha\n",
    "    for i in range(n_steps):\n",
    "        t = df.loc[i, \"time_nondim\"]\n",
    "        df.loc[i, \"alpha\"] = amplitude_rad * np.sin(omega_nondim * t)\n",
    "\n",
    "    # Compute alpha_dot using central difference\n",
    "    for i in range(1, n_steps - 1):\n",
    "        df.loc[i, \"alpha_dot\"] = (\n",
    "            (df.loc[i + 1, \"alpha\"] - df.loc[i - 1, \"alpha\"])\n",
    "            / (2 * time_step_size_nondim)\n",
    "            * C_inf\n",
    "            / length_unit\n",
    "            * chord\n",
    "            / U_inf\n",
    "        )\n",
    "        if abs(df.loc[i, \"alpha\"]) < 1e-10:\n",
    "            alpha_eq_0_index.append(i)\n",
    "\n",
    "    return alpha_eq_0_index\n",
    "\n",
    "\n",
    "compute_time_nondim(total_forces)\n",
    "alpha_eq_0_index = compute_alpha_dot(total_forces)\n",
    "print(f\"Found {len(alpha_eq_0_index)} points where alpha = 0\")"
   ],
   "execution_count": 9,
   "outputs": [
    {
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       "Output()"
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {}
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[09:57:54] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[09:57:54]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "text": [
      "Found 7 points where alpha = 0\n"
     ],
     "name": "stdout"
    }
   ],
   "id": "f70ff057"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot time history\n",
    "\n",
    "Plot the time history of the pitch moment coefficient $C_{My}$, angle of attack $\\alpha$, and pitch rate $\\dot{\\alpha}$. The blue and red dots mark the points where $\\alpha = 0$ (extrema of $\\dot{\\alpha}$)."
   ],
   "id": "dd6feef9"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "def plot_time_history(df, alpha_eq_0_idx):\n",
    "    _, axs = plt.subplots(3, figsize=(10, 8))\n",
    "\n",
    "    # Time history of CMy\n",
    "    axs[0].plot(df[\"physical_step\"], df[\"CMy\"], color=cmap.colors[0])\n",
    "    axs[0].set_yticks(np.linspace(-0.02, 0.02, 5))\n",
    "    axs[0].set_ylim([-0.02, 0.02])\n",
    "    axs[0].set_ylabel(\"CMy\")\n",
    "\n",
    "    # Time history of alpha\n",
    "    axs[1].plot(df[\"physical_step\"], df[\"alpha\"], color=cmap.colors[1])\n",
    "    axs[1].set_ylabel(r\"$\\alpha$ (rad)\")\n",
    "\n",
    "    # Time history of alpha_dot\n",
    "    axs[2].plot(df[\"physical_step\"][1:-2], df[\"alpha_dot\"][1:-2], color=cmap.colors[2])\n",
    "    axs[2].set_ylabel(r\"$\\dot{\\alpha}$ (rad/flowthru)\")\n",
    "    axs[2].set_xlabel(\"physical_step\")\n",
    "\n",
    "    for ax in axs:\n",
    "        ax.label_outer()\n",
    "        ax.grid()\n",
    "\n",
    "    # Mark alpha=0 points (blue for min alpha_dot, red for max alpha_dot)\n",
    "    for i in alpha_eq_0_idx:\n",
    "        color = \"b\" if df[\"alpha_dot\"][i] < 0 else \"r\"\n",
    "        axs[0].plot(df[\"physical_step\"][i], df[\"CMy\"][i], \"o\", color=color)\n",
    "        axs[1].plot(df[\"physical_step\"][i], df[\"alpha\"][i], \"o\", color=color)\n",
    "        axs[2].plot(df[\"physical_step\"][i], df[\"alpha_dot\"][i], \"o\", color=color)\n",
    "\n",
    "    axs[0].set_title(\n",
    "        r\"Time History of CMy, $\\alpha$ and $\\dot{\\alpha}$\"\n",
    "        + \"\\n\"\n",
    "        + f\"flowthru/rad={n_chord_per_period:.1f}, amplitude={amplitude_deg:.1f}\"\n",
    "    )\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "plot_time_history(total_forces, alpha_eq_0_index)"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x800 with 3 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "1966b5c6"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract dynamic derivative\n",
    "\n",
    "Plot $C_{My}$ vs $\\dot{\\alpha}$ for the last oscillation period and compute the dynamic derivative $\\frac{dC_{My}}{dq}$ from linear regression at $\\alpha = 0$."
   ],
   "id": "e915efb0"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "def plot_CMy_vs_q(df, alpha_eq_0_idx):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "\n",
    "    # Plot CMy versus alpha_dot for the last period\n",
    "    n_steps = len(df[\"physical_step\"])\n",
    "    begin = n_steps - 201\n",
    "    end = n_steps - 1\n",
    "    alpha_dot = df.loc[begin:end, \"alpha_dot\"]\n",
    "    CMy = df.loc[begin:end, \"CMy\"]\n",
    "    plt.plot(alpha_dot, CMy, \".\")\n",
    "\n",
    "    # Extract alpha=0 points in the last period\n",
    "    x = []\n",
    "    y = []\n",
    "    for i in alpha_eq_0_idx:\n",
    "        if i >= begin and i <= end:\n",
    "            x.append(df.loc[i, \"alpha_dot\"])\n",
    "            y.append(df.loc[i, \"CMy\"])\n",
    "\n",
    "    # Mark the alpha=0 points\n",
    "    plt.plot(x[0], y[0], \"o\", color=\"r\", markersize=10)\n",
    "    plt.plot(x[1], y[1], \"o\", color=\"b\", markersize=10)\n",
    "\n",
    "    # Linear regression through alpha=0 points\n",
    "    coef = np.polyfit(x, y, 1)\n",
    "    func = np.poly1d(coef)\n",
    "    x_line = np.linspace(-0.02, 0.02, 101)\n",
    "    plt.plot(x_line, func(x_line), \"--\", label=f\"slope = {coef[0]:.3f}\", color=\"k\")\n",
    "\n",
    "    # Formatting\n",
    "    plt.xlabel(r\"$\\dot{\\alpha} = q$ (deg/flowthru)\")\n",
    "    plt.ylabel(\"CMy\")\n",
    "    plt.ylim([-0.02, 0.02])\n",
    "    plt.yticks(np.linspace(-0.02, 0.02, 5))\n",
    "    plt.grid()\n",
    "    plt.legend(loc=\"lower left\")\n",
    "    plt.title(\n",
    "        f\"Dynamic Derivative dCMy/dq\\nflowthru/rad={n_chord_per_period:.1f}, amplitude={amplitude_deg:.1f}\"\n",
    "    )\n",
    "\n",
    "    # Set x-axis to deg/flowthru\n",
    "    tick_locations = np.linspace(-1, 1, 5) / 180 * np.pi\n",
    "    ax = plt.gca()\n",
    "    ax.set_xticks(tick_locations, [f\"{loc * 180 / np.pi:.1f}\" for loc in tick_locations])\n",
    "\n",
    "    # Add text annotations\n",
    "    ax.text(\n",
    "        -0.016,\n",
    "        0.01,\n",
    "        r\"$\\alpha = 0$\" + \"\\n\" + r\"min $\\dot{\\alpha}$\",\n",
    "        fontsize=14,\n",
    "        color=\"b\",\n",
    "    )\n",
    "    ax.text(\n",
    "        +0.012,\n",
    "        -0.014,\n",
    "        r\"$\\alpha = 0$\" + \"\\n\" + r\"max $\\dot{\\alpha}$\",\n",
    "        fontsize=14,\n",
    "        color=\"r\",\n",
    "    )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    print(f\"\\nDynamic derivative: dCMy/dq = {coef[0]:.3f}\")\n",
    "\n",
    "\n",
    "plot_CMy_vs_q(total_forces, alpha_eq_0_index)"
   ],
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "text": [
      "\n",
      "Dynamic derivative: dCMy/dq = -0.514\n"
     ],
     "name": "stdout"
    }
   ],
   "id": "d451bb98"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "This notebook demonstrated the complete workflow for calculating dynamic derivatives using Flow360:\n",
    "\n",
    "1. **Download Tutorial Files**: Obtained the NACA0012 wing geometry (CSM format)\n",
    "2. **Create Project**: Set up a Flow360 project from the geometry\n",
    "3. **Define Sliding Interface and Meshing**: Configured the cylindrical sliding interface enclosing the wing, farfield zone, and mesh refinements\n",
    "4. **Run Steady Case**: Initialized the flow field with a fixed sliding interface (zero angular velocity)\n",
    "5. **Understanding Oscillation Setup**: Calculated nondimensional frequency and time step for sinusoidal pitching motion\n",
    "6. **Run Unsteady Case**: Forked from steady solution and ran 400 time steps (4 complete cycles) with oscillating interface\n",
    "7. **Postprocessing**: Extracted dynamic derivatives from force history\n",
    "   - Extracted unique physical steps from force history\n",
    "   - Computed angle of attack $\\alpha$ and pitch rate $\\dot{\\alpha}$\n",
    "   - Extracted dynamic derivative from linear fit at $\\alpha = 0$\n",
    "\n",
    "**Key Result**: The dynamic pitching moment derivative is approximately:\n",
    "$$\\frac{dC_{My}}{d\\dot{\\alpha}} = \\frac{dC_{My}}{dq} \\approx -0.5$$\n",
    "\n",
    "This derivative is critical for:\n",
    "- Flight dynamics modeling\n",
    "- Stability and control analysis\n",
    "- Control system design\n",
    "- Aircraft certification"
   ],
   "id": "276324b9"
  }
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