{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MRF Rotor\n", "\n", "This notebook explains the implementation of the Multiple Reference Approximation (MRF) for rotor modelling. The geometry consists of an isolated XV15 rotorcraft.\n", "\n", "\n", "![XV 15 rotor](figures/poster_image.png)\n", "\n", "\n", "## 1. Create a project from geometry:\n", "\n", "* Load python libraries and Flow360 client.\n", "* Get geometry files from example database. The geometry used in the path `XV15_CSM.geometry` is \"XV15_Wing.csm\", if you want to use your own geometry, replace this file path with your local path to your CAD file.\n", "* Load the geometry according to the length unit used in the CAD software in which the model has been created\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. The meshing settings below produce a volume mesh of about 2.4 million nodes (5.5 million cells). For rigorous validation, modify the settings as needed." ], "id": "501534a1" }, { "cell_type": "code", "metadata": {}, "source": [ "import flow360 as fl\n", "from flow360.examples.XV15_csm import XV15_CSM\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "XV15_CSM.get_files()\n", "project = fl.Project.from_geometry(XV15_CSM.geometry, length_unit=\"inch\", name=\"MRF Rotor\")\n", "geometry = project.geometry" ], "execution_count": 1, "outputs": [ { "data": { "text/html": [ "
[09:05:53] INFO: The file (XV15_Wing.csm) is being downloaded, please wait.                                        \n",
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[09:06:12] INFO: Geometry successfully submitted:                                                                  \n",
       "                   type        = Geometry                                                                          \n",
       "                   name        = MRF Rotor                                                                         \n",
       "                   id          = geo-03eef0fa-ef3b-4a8f-b4d2-41b73f276bff                                          \n",
       "                   status      = uploaded                                                                          \n",
       "                   project id  = prj-09d6aa35-01b2-4217-b69e-998e7b9c1507                                          \n",
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       "\u001b[2;36m[09:06:12]\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        = MRF Rotor                                                                         \n",
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       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
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   "metadata": {},
   "source": [
    "## 2. Geometry Primitives Definition:\n",
    "\n",
    "In order to apply the MRF approximation we have to define a cylinder corresponding to the rotating zone encapsulating the cylinder. We also define two cylinders for volumetric refinements: a slightly smaller one inside the rotating zone, which sets the cell size around the blades, and a longer one downstream of the rotor, which refines its wake.\n",
    "\n",
    "The farfield is defined automatically and determines the limits of the simulation domain. This is also included in this code snippet with the class `fl.AutomatedFarfield()`.\n"
   ],
   "id": "4f355c66"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "\n",
    "    farfield = fl.AutomatedFarfield()\n",
    "    R = 3.81\n",
    "    MRF_cylinder = fl.Cylinder(\n",
    "        center=(0, 0, 0),\n",
    "        outer_radius=170 * fl.u.inch,\n",
    "        height=40 * fl.u.inch,\n",
    "        axis=(0, 0, -1),\n",
    "        name=\"RotatingCylinder\",\n",
    "    )\n",
    "\n",
    "    Rotor_refinement_cylinder = fl.Cylinder(\n",
    "        center=(0, 0, 0) * fl.u.inch,\n",
    "        outer_radius=165 * fl.u.inch,\n",
    "        height=36 * fl.u.inch,\n",
    "        axis=(0, 0, -1),\n",
    "        name=\"RotorRefinementCylinder\",\n",
    "    )\n",
    "\n",
    "    Wake_Cylinder = fl.Cylinder(\n",
    "        center=(0, 0, -140) * fl.u.inch,\n",
    "        outer_radius=170 * fl.u.inch,\n",
    "        height=320 * fl.u.inch,\n",
    "        axis=(0, 0, -1),\n",
    "        name=\"WakeCylinder\",\n",
    "    )"
   ],
   "execution_count": 2,
   "outputs": [
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[09:06:49] INFO: using: SI unit system for unit inference.                                                         \n",
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   ],
   "id": "dd94ca0f"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Rotating Zone Definition\n",
    "\n",
    "We use the RotatingCylinder Object to define the mesh volume zone in which the MRF approximation will be applied. We have to define the mesh properties at the interface between the volume zones, which will in turn, determine the overall mesh quality of the volume zone.\n",
    "\n",
    "To control the cell size inside the rotating zone, a uniform volumetric refinement is applied to the slightly smaller `Rotor_refinement_cylinder` in section 4.1."
   ],
   "id": "1e30d43a"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "RotatingZone = fl.RotationVolume(\n",
    "    name=\"RotatingZone\",\n",
    "    spacing_axial=3 * fl.u.inch,\n",
    "    spacing_circumferential=4 * fl.u.inch,\n",
    "    spacing_radial=4 * fl.u.inch,\n",
    "    entities=MRF_cylinder,\n",
    "    enclosed_entities=geometry[\"*\"],\n",
    ")"
   ],
   "execution_count": 3,
   "outputs": [],
   "id": "4976c480"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Case Parameters Definition:\n",
    "\n",
    "### 4.1 Meshing:\n",
    "\n",
    "Having defined the basic information about the rotating zone we can define the different parameters which drive the simulation, starting from the meshing. The required inputs are:\n",
    "\n",
    "1. Meshing Defaults: in this section the basic parameters needed to create the surface and volume meshes are determined. In this case the first layer thickness keeps y+ below 2 on the blades (below 1 over most of the blade surface, with a mean of about 0.5), while the surface edge length, curvature resolution angle and growth rates are chosen to limit the cell count\n",
    "\n",
    "2. Volume Zones: for multizone meshes, all volume zones have to be specified. In this case we have a farfield and the rotating zone\n",
    "\n",
    "3. Refinements: a number of refinements can be included to increase the accuracy of the mesh. Here, one uniform refinement sets the cell size around the blades and a coarser one covers the wake.\n"
   ],
   "id": "808c2c32"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "meshing = fl.MeshingParams(\n",
    "    defaults=fl.MeshingDefaults(\n",
    "        surface_max_edge_length=3 * fl.u.inch,\n",
    "        boundary_layer_first_layer_thickness=1.5e-6 * fl.u.m,\n",
    "        curvature_resolution_angle=20 * fl.u.deg,\n",
    "        boundary_layer_growth_rate=1.3,\n",
    "        surface_edge_growth_rate=1.4,\n",
    "        volume_edge_growth_rate=1.2,\n",
    "    ),\n",
    "    volume_zones=[farfield, RotatingZone],\n",
    "    refinements=[\n",
    "        fl.UniformRefinement(entities=Rotor_refinement_cylinder, spacing=4 * fl.u.inch),\n",
    "        fl.UniformRefinement(entities=Wake_Cylinder, spacing=8 * fl.u.inch),\n",
    "    ],\n",
    ")"
   ],
   "execution_count": 4,
   "outputs": [],
   "id": "d27d9a40"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Final MRF Definition:\n",
    "\n",
    "The MRF zone is established by defining a rotating region in the solver parameters. Using fl.Rotation() inside a fl.Steady configuration allows the solver to apply the MRF approximation."
   ],
   "id": "f168089f"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "omega = 589 * fl.u.rpm\n",
    "\n",
    "MRF = fl.Rotation(name=\"MRF\", volumes=MRF_cylinder, spec=fl.AngularVelocity(omega))\n",
    "\n",
    "time_stepping = fl.Steady(max_steps=1500, CFL=fl.AdaptiveCFL(convergence_limiting_factor=0.75))"
   ],
   "execution_count": 5,
   "outputs": [],
   "id": "8dd73c2b"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Dynamic parameters:\n",
    "\n",
    "Having defined the time-stepping scheme and the MRF rotating zone. We have to specify the turbulence model, and reference geometry. All the parameters are then groped under the following sections. The physical configuration is intended to demonstrate the numerical implementation of a forward-flight case, rather than to represent a realistic operating condition for the rotor."
   ],
   "id": "aa5ef552"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "with fl.SI_unit_system:\n",
    "\n",
    "    params = fl.SimulationParams(\n",
    "        operating_condition=fl.AerospaceCondition.from_mach(\n",
    "            mach=0.12,\n",
    "            alpha=-90 * fl.u.deg,\n",
    "        ),\n",
    "        reference_geometry=fl.ReferenceGeometry(\n",
    "            moment_center=(0, 0, 0), moment_length=(R, R, R), area=np.pi * R**2\n",
    "        ),\n",
    "        time_stepping=time_stepping,\n",
    "        meshing=meshing,\n",
    "        models=[\n",
    "            fl.Fluid(\n",
    "                navier_stokes_solver=fl.NavierStokesSolver(\n",
    "                    absolute_tolerance=1e-10,\n",
    "                    riemann_solver=fl.RoeFlux(low_mach_preconditioner=True),\n",
    "                ),\n",
    "                turbulence_model_solver=fl.SpalartAllmaras(),\n",
    "            ),\n",
    "            fl.Wall(surfaces=geometry[\"*\"]),\n",
    "            fl.Freestream(surfaces=farfield.farfield),\n",
    "            MRF,\n",
    "        ],\n",
    "        outputs=[\n",
    "            fl.SurfaceOutput(\n",
    "                surfaces=geometry[\"*\"],\n",
    "                output_fields=[\n",
    "                    \"primitiveVars\",\n",
    "                    \"Cp\",\n",
    "                    \"Cf\",\n",
    "                    \"CfVec\",\n",
    "                    \"yPlus\",\n",
    "                ],\n",
    "            ),\n",
    "            fl.VolumeOutput(\n",
    "                output_fields=[\n",
    "                    \"Mach\",\n",
    "                    \"primitiveVars\",\n",
    "                    \"qcriterion\",\n",
    "                    \"Cp\",\n",
    "                ]\n",
    "            ),\n",
    "        ],\n",
    "    )"
   ],
   "execution_count": 6,
   "outputs": [
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           INFO: using: SI unit system for unit inference.                                                         \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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   "id": "cf894a2e"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Running the case\n",
    "Here we run the case using the beta mesher."
   ],
   "id": "bf7ce010"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "case = project.run_case(params=params, name=\"Steady MRF\", use_beta_mesher=True)"
   ],
   "execution_count": 7,
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[09:06:51] INFO: Selecting beta/in-house mesher for possible meshing tasks.                                        \n",
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[09:06:53] INFO: Successfully submitted:                                                                           \n",
       "                   type        = Case                                                                              \n",
       "                   name        = Steady MRF                                                                        \n",
       "                   id          = case-4be5bf8d-e96b-4282-b3c6-1ce97bc55f3e                                         \n",
       "                   status      = pending                                                                           \n",
       "                   project id  = prj-09d6aa35-01b2-4217-b69e-998e7b9c1507                                          \n",
       "                                                                                                                   \n",
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       "\u001b[2;36m[09:06:53]\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        = Steady MRF                                                                        \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93m4be5bf8d-e96b-4282-b3c6-1ce97bc55f3e\u001b[0m                                         \n",
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   ],
   "id": "441dc499"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Numerical and Force Convergence\n",
    "\n",
    "Having ran the case we can plot the thrust and non-linear residuals. Note that for the thrust we will need to use dimensional quantities from the case."
   ],
   "id": "8a9811d6"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "case.wait()\n",
    "\n",
    "# Reference quantities\n",
    "density = case.params.operating_condition.thermal_state.density\n",
    "U_ref = (\n",
    "    case.params.operating_condition.reference_velocity_magnitude\n",
    "    or case.params.operating_condition.velocity_magnitude\n",
    ")\n",
    "A_ref = case.params.reference_geometry.area\n",
    "\n",
    "# Get results\n",
    "results = case.results\n",
    "\n",
    "# Plot thrust\n",
    "total_forces = case.results.total_forces.as_dataframe()\n",
    "total_forces[\"thrust\"] = total_forces[\"CFz\"] * 0.5 * density * U_ref**2 * A_ref\n",
    "total_forces.plot(\"pseudo_step\", [\"thrust\"])\n",
    "\n",
    "# Plot non-linear residuals\n",
    "non_linear = case.results.nonlinear_residuals.as_dataframe()\n",
    "non_linear.plot(\n",
    "    \"pseudo_step\",\n",
    "    [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"],\n",
    "    logy=True,\n",
    ")\n",
    "plt.show()"
   ],
   "execution_count": 8,
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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "id": "91eeba96"
  }
 ],
 "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
}