{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Flat Plate with Structural Aerodynamic Load\n", "\n", "In this example, we present an illustrative case where a flat plate rotates while being subjected to a rotational spring moment and damper as well as aerodynamic loads. The structural dynamics are modeled by a torsional spring–mass–damper ODE driven by the aerodynamic pitching moment $\\tau_y(t)$:\n", "\n", "$$\n", "I\\,\\ddot{\\theta}(t) + 2\\,\\zeta\\,\\omega_N\\,I\\,\\dot{\\theta}(t) + K\\bigl(\\theta(t)-\\theta_0\\bigr) = \\tau_y(t).\n", "$$\n", "\n", "As you can see below, the flat-plate mesh consists of two zones separated by a circular sliding interface. The inner zone will rotate while the outer zone remains stationary. This is the same mesh topology as what is used for rotating propeller simulations.\n", "\n", "![UDD mesh flat plate](figures/flat_plate_UDD_tutorial_mesh.png)\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": "9d777507" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Imports and Create Project\n", "\n", "In this section we:\n", "- **Import Flow360 and NumPy**: `flow360` provides the high-level Python API for setting up and running the CFD case, while `numpy` is used for basic math and unit handling.\n", "- **Load the flat-plate mesh**: the CGNS file `rotatingPlate.cgns` is uploaded to the Flow360 platform and becomes the basis of the `Project`.\n", "- **Create a** `Project` **and** `VolumeMesh` **handle**: `Project.from_volume_mesh(mesh)` creates (or reuses) a project on the server, and `vm = project.volume_mesh` gives convenient access to mesh blocks and boundary entities used later for BCs and UDD setup." ], "id": "7bb41c90" }, { "cell_type": "code", "metadata": {}, "source": [ "import flow360 as fl\n", "from flow360.examples import TutorialUDDStructural\n", "import numpy as np\n", "\n", "TutorialUDDStructural.get_files()\n", "project = fl.Project.from_volume_mesh(\n", " TutorialUDDStructural.mesh_filename, name=\"Flat Plate with Structural Aerodynamic Load\"\n", ")\n", "vm = project.volume_mesh" ], "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "012097bbf3864afba7de8957db58e4e0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/html": [ "
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[18:39:40] INFO: VolumeMesh successfully submitted:                                                                \n",
       "                   type        = Volume Mesh                                                                       \n",
       "                   name        = UDD structural tutorial from Python                                               \n",
       "                   id          = vm-b11bca1f-a264-487c-8f1a-eeb168096f2c                                           \n",
       "                   status      = uploaded                                                                          \n",
       "                   project id  = prj-50ef37d8-4b77-480e-92c1-98a68f613176                                          \n",
       "                                                                                                                   \n",
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       "\u001b[2;36m[18:39:40]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: VolumeMesh successfully submitted:                                                                \n",
       "\u001b[2;36m           \u001b[0m        type        = Volume Mesh                                                                       \n",
       "\u001b[2;36m           \u001b[0m        name        = UDD structural tutorial from Python                                               \n",
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       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Operating Conditions\n",
    "\n",
    "Since the mesh flat plate length is different from experiment, the operating conditions are computed based on a reference Mach number and reference Reynolds number.\n",
    "\n",
    "More precisely, the code below:\n",
    "- **Defines experimental reference values**: freestream velocity, reference length, density, temperature, and kinematic viscosity are specified in SI units using Flow360’s unit helpers.\n",
    "- **Computes Mach and Reynolds numbers**: `Mach` is derived from `u_inf` and the speed of sound; `Reynolds` is based on the experiment reference length and kinematic viscosity.\n",
    "- **Maps to mesh units**: `Re_mesh_unit` rescales the Reynolds number to the mesh length scale so the simulation matches the intended experimental conditions.\n",
    "- **Creates an** `AerospaceCondition`: `operating_condition = fl.AerospaceCondition.from_mach_reynolds(...)` packages these quantities into a reusable object for the solver."
   ],
   "id": "0afc6c7f"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Experiment reference values in SI unit system\n",
    "u_inf = 15 * fl.u.m / fl.u.s\n",
    "ref_length = (4 * fl.u.inch).to(\"m\")\n",
    "ref_density = 1.226 * fl.u.kg / fl.u.m**3\n",
    "ref_T = 288.15 * fl.u.K\n",
    "kinematic_viscosity = 14.61e-6 * fl.u.m**2 / fl.u.s\n",
    "\n",
    "R = 287.058 * fl.u.J / (fl.u.kg * fl.u.K)\n",
    "speed_of_sound = np.sqrt(1.4 * R * ref_T).to(\"m/s\")\n",
    "Mach = u_inf / speed_of_sound\n",
    "Reynolds = u_inf * ref_length / kinematic_viscosity\n",
    "\n",
    "# Define operating condition based on reference values\n",
    "mesh_unit = project.length_unit\n",
    "Re_mesh_unit = Reynolds * mesh_unit.to_value() / ref_length\n",
    "operating_condition = fl.AerospaceCondition.from_mach_reynolds(\n",
    "    mach=Mach,\n",
    "    reynolds_mesh_unit=Re_mesh_unit,\n",
    "    project_length_unit=project.length_unit,\n",
    ")"
   ],
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[18:39:58] INFO: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "           created.                                                                                                \n",
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       "\u001b[2;36m[18:39:58]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "\u001b[2;36m           \u001b[0mcreated.                                                                                                \n"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Boundary Conditions and Rotating Interfaces\n",
    "\n",
    "The following cell defines how the flow interacts with the flat plate and how the inner plate region is allowed to rotate:\n",
    "- **Freestream and walls**: `Freestream` is applied on the far-field block, `SlipWall` on non-plate walls, and `Wall` (no-slip) on the plate itself.\n",
    "- **Rotating volume**: `volume_rotation = vm[\"plateBlock\"]` selects the inner block that will move as a rigid body around the specified axis.\n",
    "- **Rotation model**: `fl.Rotation(..., spec=fl.FromUserDefinedDynamics())` tells Flow360 that the rotational motion will be driven by a User Defined Dynamic (UDD) law defined later, rather than by a prescribed constant rate. The UDD will specify $\\dot{\\omega}$, $\\omega$, and $\\theta$ for each physical step."
   ],
   "id": "5fbe737b"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "# Boundary Conditions\n",
    "freestream = fl.Freestream(entities=vm[\"farFieldBlock/farField\"])\n",
    "slip_wall = fl.SlipWall(entities=vm[\"*/slipWall\"])\n",
    "no_slip_wall = fl.Wall(entities=vm[\"plateBlock/noSlipWall\"])\n",
    "BCs = [freestream, slip_wall, no_slip_wall]\n",
    "\n",
    "# Rotating zones\n",
    "volume_rotation = vm[\"plateBlock\"]\n",
    "volume_rotation.center = (0, 0, 0) * fl.u.m\n",
    "volume_rotation.axis = [0, 1, 0]\n",
    "rotation_model = fl.Rotation(entities=[volume_rotation], spec=fl.FromUserDefinedDynamics())"
   ],
   "execution_count": 3,
   "outputs": [],
   "id": "ff41c667"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Couple Structural Model\n",
    "\n",
    "The dynamics of the plate will be influenced by the spring and damper according to this law:\n",
    "\n",
    "$$\n",
    "\\dot{\\omega}(t) = \\frac{\\tau_y(t) - K\\,(\\theta(t) - \\theta_0) - 2\\,\\zeta\\,\\omega_N\\,I\\,\\omega(t)}{I}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\omega(t) - \\omega_0 = \\int_0^t \\dot{\\omega}(t') \\, dt'\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\theta(t) - \\theta_0 = \\int_0^t \\omega(t') \\, dt'\n",
    "$$\n",
    "\n",
    "| Symbol | Description |\n",
    "|--------|-------------|\n",
    "| $\\theta$ | Rotation angle of the plate in radians. |\n",
    "| $\\omega$ | Rotation velocity of the plate in radians. |\n",
    "| $\\dot{\\omega}$ | Rotation acceleration of the plate in radians. |\n",
    "| $\\tau_y$ | Aerodynamic moment exerted on the plate. |\n",
    "| $K$ | Stiffness of the spring attached to the plate at the structural support. |\n",
    "| $\\zeta$ | Structural damping ratio. |\n",
    "| $\\omega_N$ | Structural natural angular frequency. |\n",
    "| $I$ | Structural moment of inertia. |\n",
    "\n",
    "In the next few cells we will:\n",
    "- **Specify numerical values** for the structural parameters ($I, \\zeta, K, \\omega_N, \\theta_0$) and initial conditions for $\\theta$ and $\\dot{\\omega}$.\n",
    "- **Set the time step and number of steps** so that the structural oscillations are resolved in time.\n",
    "- **Encode this ODE system as a User Defined Dynamic** in Flow360, which takes the aerodynamic moment $\\tau_y$ as input and outputs the plate rotation state used by the mesh motion model.\n"
   ],
   "id": "6b134c47"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Define Model Constants\n",
    "\n",
    "Here we choose the numerical values that define the torsional spring–mass–damper model of the plate:\n",
    "- $I$: structural moment of inertia of the rotating plate about its hinge axis.\n",
    "- $\\zeta$: non-dimensional structural damping ratio; smaller values lead to slowly decaying oscillations.\n",
    "- $\\theta_0$: equilibrium pitch angle about which the spring tries to restore the plate.\n",
    "- `iniPert`: initial perturbation added on top of $\\theta_0$, which directly sets the initial oscillation amplitude.\n",
    "- $K$ and $\\omega_N$: linear torsional stiffness and corresponding natural frequency, related approximately by $\\omega_N = \\sqrt{K/I}$.\n",
    "- `initTheta` and `initOmegaDot`: initial angle and angular acceleration used to initialize the UDD state vector for the first physical time step."
   ],
   "id": "830d7c20"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "deg2rad = np.pi / 180\n",
    "\n",
    "I = 0.443768309310345  # Structural moment of inertia\n",
    "zeta = 0.005  # Structural damping ratio\n",
    "theta0 = 5 * deg2rad  # Initial equilibrium angle\n",
    "iniPert = 45 * deg2rad  # Initial perturbation\n",
    "K = 0.005  # Stiffness of the spring\n",
    "omegaN = np.sqrt(K / I)  # Structural natural angular frequency\n",
    "initTheta = theta0 + iniPert\n",
    "initOmegaDot = (0 - K * (initTheta - theta0)) / I"
   ],
   "execution_count": 4,
   "outputs": [],
   "id": "552b6072"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Define Time Settings\n",
    "\n",
    "The next cell configures the unsteady time marching for the coupled aeroelastic simulation:\n",
    "- `deltaNonDimensional`: a non-dimensional time step size, scaled by the acoustic time based on `ref_length / speed_of_sound`.\n",
    "- `delta_SI`: the corresponding physical time step in seconds, passed to the solver as `step_size`.\n",
    "- `n_steps`: total number of physical steps to march the coupled flow–structure system.\n",
    "- `fl.Unsteady(...)`: creates a time stepping object that controls the outer physical time loop and the number of inner pseudo-steps used to converge each time level."
   ],
   "id": "2d21ad6f"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "deltaNonDimensional = 0.5\n",
    "delt_SI = deltaNonDimensional * (ref_length / speed_of_sound)\n",
    "n_steps = 3000\n",
    "time_stepping = fl.Unsteady(\n",
    "    max_pseudo_steps=100,\n",
    "    step_size=delt_SI,\n",
    "    steps=n_steps,\n",
    ")"
   ],
   "execution_count": 5,
   "outputs": [],
   "id": "5f65216a"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 UDD for structural solver\n",
    "\n",
    "This User Defined Dynamic (UDD) encodes the structural ODE and couples it to the CFD solution:\n",
    "- **Inputs**: `momentY` is the aerodynamic pitching moment from the wall patch on the plate.\n",
    "- **Constants**: the structural parameters (`I`, `zeta`, `K`, `omegaN`, `theta0`) are passed in once and reused each step.\n",
    "- **Output Vars**: the state vector `state[0:3]` stores $\\dot{\\omega}$, $\\omega$, and $\\theta$; these are exposed as `omegaDot`, `omega`, and `theta` outputs. It is important to use this names since the values of $\\dot{\\omega}$, $\\omega$, and $\\theta$ will be updated for `state[0:3]` for each physical step, this will drive the rotation of the sliding interface.\n",
    "- **Initial values**: `state_vars_initial_value` initializes the ODE with `initOmegaDot` and `initTheta` from Section 3.1.\n",
    "- **Update law**: for each physical step (`pseudoStep == 0`), the first expression integrates $\\dot{\\omega}$, the second integrates $\\omega$, and the third integrates $\\theta$ using an explicit Euler update with `timeStepSize`.\n",
    "- **Coupling to motion**: `output_target=vm[\"plateBlock\"]` sends the updated `theta` back to the rotating volume, which the rotation model uses to move the mesh."
   ],
   "id": "75b3694d"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "dynamic = fl.UserDefinedDynamic(\n",
    "    name=\"dynamicTheta\",\n",
    "    input_vars=[\"momentY\"],\n",
    "    constants={\n",
    "        \"I\": I,\n",
    "        \"zeta\": zeta,\n",
    "        \"K\": K,\n",
    "        \"omegaN\": omegaN,\n",
    "        \"theta0\": theta0,\n",
    "    },\n",
    "    output_vars={\n",
    "        \"omegaDot\": \"state[0];\",\n",
    "        \"omega\": \"state[1];\",\n",
    "        \"theta\": \"state[2];\",\n",
    "    },\n",
    "    state_vars_initial_value=[str(initOmegaDot), \"0.0\", str(initTheta)],\n",
    "    update_law=[\n",
    "        \"if (pseudoStep == 0) (momentY - K * ( state[2] - theta0 ) - 2 * zeta * omegaN * I *state[1] ) / I; else state[0];\",\n",
    "        \"if (pseudoStep == 0) state[1] + state[0] * timeStepSize; else state[1];\",\n",
    "        \"if (pseudoStep == 0) state[2] + state[1] * timeStepSize; else state[2];\",\n",
    "    ],\n",
    "    input_boundary_patches=vm[\"plateBlock/noSlipWall\"],\n",
    "    output_target=vm[\"plateBlock\"],\n",
    ")"
   ],
   "execution_count": 6,
   "outputs": [],
   "id": "392053c3"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Outputs\n",
    "\n",
    "We will put some slice outputs to have a time visualisation of the flowfield.\n",
    "\n",
    "The code below configures what Flow360 writes during the run:\n",
    "- `SliceOutput`: extracts a 2D slice at $y=0$ (normal to the $y$ axis) at selected time steps, outputting primitive variables and Mach number in Tecplot format.\n",
    "- `VolumeOutput`: writes 3D volume fields (primitive variables, $Q$-criterion, and Mach number) for full-flow visualization.\n",
    "- **Output frequency**: `frequency` and `frequency_offset` control how often and when in the run the slice output is written, so that the transient behavior of the oscillating plate can be visualized efficiently."
   ],
   "id": "eb8242b0"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    outputs = [\n",
    "        fl.SliceOutput(\n",
    "            slices=[fl.Slice(name=\"y-slice\", origin=(0, 0, 0), normal=(0, 1, 0))],\n",
    "            output_format=[\"tecplot\"],\n",
    "            output_fields=[\"primitiveVars\", \"Mach\"],\n",
    "            frequency=10,\n",
    "            frequency_offset=200,\n",
    "        ),\n",
    "        fl.VolumeOutput(\n",
    "            output_format=[\"tecplot\"],\n",
    "            output_fields=[\"primitiveVars\", \"qcriterion\", \"Mach\"],\n",
    "        ),\n",
    "    ]"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
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      ],
      "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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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Simulation Parameters\n",
    "\n",
    "The previous defined settings are used here to define the `SimulationParams`.\n",
    "\n",
    "Specifically, this section:\n",
    "- **Defines a reference geometry**: sets the aerodynamic reference moment center, length, and area used to non-dimensionalize forces and moments.\n",
    "- **Attaches the operating condition and time stepping**: `operating_condition` and `time_stepping` from earlier sections are plugged into the top-level parameters.\n",
    "- **Configures fluid and turbulence solvers**: creates a `NavierStokesSolver` and `SpalartAllmaras` turbulence model with tolerances, linear solver controls, and accuracy/order settings tailored to this case.\n",
    "- **Adds models and BCs**: combines all boundary conditions, the rotation model, and the User Defined Dynamic into the list of active models.\n",
    "- **Registers outputs**: passes the `outputs` list so that slice and volume data are produced according to the configuration in Section 4."
   ],
   "id": "af22f4c9"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "    param = fl.SimulationParams(\n",
    "        reference_geometry=fl.ReferenceGeometry(\n",
    "            moment_center=(0, 0, 0),\n",
    "            moment_length=(1, 1, 1) * mesh_unit,\n",
    "            area=0.5325 * mesh_unit * mesh_unit,\n",
    "        ),\n",
    "        operating_condition=operating_condition,\n",
    "        time_stepping=time_stepping,\n",
    "        models=[\n",
    "            fl.Fluid(\n",
    "                navier_stokes_solver=fl.NavierStokesSolver(\n",
    "                    absolute_tolerance=1e-9,\n",
    "                    relative_tolerance=1e-3,\n",
    "                    linear_solver=fl.LinearSolver(max_iterations=35),\n",
    "                    kappa_MUSCL=0.33,\n",
    "                    order_of_accuracy=2,\n",
    "                    update_jacobian_frequency=1,\n",
    "                    equation_evaluation_frequency=1,\n",
    "                    low_mach_preconditioner=True,\n",
    "                ),\n",
    "                turbulence_model_solver=fl.SpalartAllmaras(\n",
    "                    absolute_tolerance=1e-8,\n",
    "                    relative_tolerance=1e-2,\n",
    "                    linear_solver=fl.LinearSolver(max_iterations=35),\n",
    "                    order_of_accuracy=2,\n",
    "                    rotation_correction=True,\n",
    "                    update_jacobian_frequency=1,\n",
    "                    reconstruction_gradient_limiter=0.5,\n",
    "                    equation_evaluation_frequency=1,\n",
    "                ),\n",
    "            ),\n",
    "            *BCs,\n",
    "            rotation_model,\n",
    "        ],\n",
    "        outputs=outputs,\n",
    "        user_defined_dynamics=[dynamic],\n",
    "    )"
   ],
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
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      ],
      "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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   "source": [
    "## 6. Run Case\n",
    "\n",
    "Here we finally submit the configured simulation to Flow360:\n",
    "- `project.run_case(param)`: packages the mesh, models, operating condition, and solver settings into a `Case` and sends it to the server.\n",
    "- `case`: can be used to monitor status, wait for completion, and fetch results (as done in the post-processing section)."
   ],
   "id": "6c9de9ae"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "case = project.run_case(params=param, name=\"Aeroelastic (structural UDD)\")"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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       "
           INFO: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "           created.                                                                                                \n",
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       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "\u001b[2;36m           \u001b[0mcreated.                                                                                                \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
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      ],
      "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": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
[18:40:00] INFO: Successfully submitted:                                                                           \n",
       "                   type        = Case                                                                              \n",
       "                   name        = UDD structural tutorial from Python                                               \n",
       "                   id          = case-cb351231-8428-4b41-8e00-a00cb4c9c61c                                         \n",
       "                   status      = pending                                                                           \n",
       "                   project id  = prj-50ef37d8-4b77-480e-92c1-98a68f613176                                          \n",
       "                                                                                                                   \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[18:40:00]\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        = UDD structural tutorial from Python                                               \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93mcb351231-8428-4b41-8e00-a00cb4c9c61c\u001b[0m                                         \n",
       "\u001b[2;36m           \u001b[0m        status      = pending                                                                           \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m50ef37d8-4b77-480e-92c1-98a68f613176\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "c37adc29"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Post Processing\n",
    "\n",
    "Once the case has finished, we read back and visualize key integrated quantities from the server-side results:\n",
    "- **Wait for completion**: `case.wait()` blocks until the CFD run finishes.\n",
    "- **Total forces**: `results.total_forces.as_dataframe()` returns time histories of lift and drag coefficients (`CL`, `CD`), which are plotted versus `physical_step` to show the plate’s oscillatory response.\n",
    "- **Nonlinear residuals**: `results.nonlinear_residuals` exposes solver convergence histories (continuity, momentum, energy, and turbulence variable), plotted on a log scale to verify converge. The data is filtered to show only the last pseudo step using `filter_physical_steps_only`."
   ],
   "id": "173dc65e"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "case.wait()\n",
    "\n",
    "results = case.results\n",
    "\n",
    "total_forces = results.total_forces.as_dataframe()\n",
    "total_forces_2000 = total_forces[total_forces[\"physical_step\"] >= 2000]\n",
    "total_forces_2000.plot(\"physical_step\", [\"CL\", \"CD\"])\n",
    "\n",
    "non_linear = results.nonlinear_residuals\n",
    "non_linear.filter_physical_steps_only()\n",
    "non_linear = non_linear.as_dataframe()\n",
    "non_linear_2000 = non_linear[non_linear[\"physical_step\"] >= 2000]\n",
    "non_linear_2000.plot(\n",
    "    \"physical_step\",\n",
    "    [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"],\n",
    "    logy=True,\n",
    ")\n",
    "\n",
    "plt.show()"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da4e9e58638f48ed84b0a93609e33f95",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93f009b466b5482baf53bb2c71f43172",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
[18:58:49] INFO: Saved to /tmp/tmpbfsct6q_/8f1619f2-d54b-48c7-b87b-c4b1cd54fa42.csv                                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[18:58:49]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpbfsct6q_/\u001b[0m\u001b[95m8f1619f2-d54b-48c7-b87b-c4b1cd54fa42.csv\u001b[0m                                \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c9d61aa342ca48759edaa6073cdc571c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
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      ],
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
[18:58:51] INFO: Saved to /tmp/tmpbfsct6q_/2715dda5-2f01-4200-a74c-b33fbb253b90.csv                                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[18:58:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpbfsct6q_/\u001b[0m\u001b[95m2715dda5-2f01-4200-a74c-b33fbb253b90.csv\u001b[0m                                \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "afe8aa99"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![v field](figures/flat_plate_v.gif)"
   ],
   "id": "b2be9573"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "flow360-dev",
   "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}