{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RANS CFD on 2D CRM Airfoil\n", "\n", "![image_head](../thumbnails/2D_CRM_airfoil.png)\n", "\n", "This notebook shows how to set up and run a 2D airfoil simulation using the Flow360 python API.\n", "\n", "Flow360 is not able to run true 2D simulations, however we can use the quasi-3D approach to simulate 2D flows. This is done by extruding the 2D geometry a small distance in the spanwise direction and applying symmetry boundary conditions on the two side faces.\n", "\n", "Geometry for this tutorial was created using Engineering Sketch Pad (ESP) and is already provided. For more information about the geometry creation process, please refer to the [Geometry creation of 2D CRM airfoil using ESP](../../../knowledge_base/PreProcessing/esp_tutorials/ESPTutorial_2D_multielement/ESPTutorial_2D_multielement.rst) tutorial.\n", "\n", "We will walk through all the steps such as importing Flow360 modules, creating a project and assigning simulation parameters.\n", "\n", "Additionally, we will use the function `fl.AerospaceCondition.from_mach_reynolds()`, which is often used when running workshop cases. It allows us to define operating conditions using a specified mach and reynolds number.\n", "\n", "In order to get a closer look at how we define each parameter, we will first split the simulation parameters into smaller parts, which will be assembled later on.\n", "\n", "As a first step, we will import all dependencies.\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": "8355df66-8b7b-457a-8905-1c6d673cfab7" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-12-12T18:09:46.812777Z", "start_time": "2025-12-12T18:09:43.650388Z" } }, "source": [ "# flow360 imports\n", "import flow360 as fl\n", "from flow360.examples import Tutorial2DCRM" ], "execution_count": 1, "outputs": [], "id": "f1a1793f-0e6c-4836-938a-cf857de32099" }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Project creation\n", "We then create a **Flow360 Project** variable, which will be used to do operations on the geometry as well as running the case. This includes both creating the surface and volume meshes as well as running the flow solution. This project will appear in your Flow360 account on the WEBUI." ], "id": "68120cff-2504-42e7-ba5d-1cdf88571dda" }, { "cell_type": "code", "metadata": { "ExecuteTime": { "end_time": "2025-12-12T18:10:31.859702Z", "start_time": "2025-12-12T18:09:46.925357Z" } }, "source": [ "# Download files associated with the tutorial\n", "Tutorial2DCRM.get_files()\n", "\n", "# Create project variable\n", "project = fl.Project.from_geometry(Tutorial2DCRM.geometry, name=\"RANS CFD on 2D CRM Airfoil\")\n", "\n", "# Create geometry variable\n", "geometry = project.geometry\n", "\n", "# assign the correct face and edge groupings to the geometry\n", "geometry.group_faces_by_tag(\"faceName\")\n", "geometry.group_edges_by_tag(\"edgeName\")" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "
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[14:26:50] INFO: Geometry successfully submitted:                                                                  \n",
       "                   type        = Geometry                                                                          \n",
       "                   name        = Tutorial 2D CRM from Python                                                       \n",
       "                   id          = geo-c48d40ca-fc38-4bf3-90ce-ae23774d9b38                                          \n",
       "                   status      = uploaded                                                                          \n",
       "                   project id  = prj-31637662-3d26-4038-bb41-69641da3a977                                          \n",
       "                                                                                                                   \n",
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       "\u001b[2;36m[14:26:50]\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 2D CRM from Python                                                       \n",
       "\u001b[2;36m           \u001b[0m        id          = geo-\u001b[93mc48d40ca-fc38-4bf3-90ce-ae23774d9b38\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
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       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
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           INFO: Waiting for geometry to be processed.                                                             \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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   "source": [
    "## Defining simulation parameters\n",
    "Once the geometry is processed, we can create separate variables for:\n",
    "- [Entities and volume zones](#Entities-and-volume-zones)\n",
    "- [Meshing parameters](#Meshing-parameters)\n",
    "- [Reference geometry](#Reference-geometry)\n",
    "- [Operating condition](#Operating-condition)\n",
    "- [Time stepping](#Time-stepping)\n",
    "- [Models](#Models)\n",
    "- [Outputs](#Outputs)\n",
    "\n",
    "A helpful statement we will use is `with fl.SI_unit_system:`, which allows us to automatically assign SI units to parameters. \n",
    "\n",
    "We still need to manually define the unit for angle as there is no default angle unit when using `with fl.SI_unit_system:`."
   ],
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   "source": [
    "### Entities and volume zones\n",
    "`fl.Cylinder` entities are used to create mesh refinement regions.\n",
    "\n",
    "A `fl.AutomatedFarfield` volume zone is used to define the circular flow field simulation volume zone farfield and symmetry planes boundary models."
   ],
   "id": "b2418b1b-fa40-4d87-803a-33cd7a0efbe6"
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    }
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   "source": [
    "# Choosing a unit system\n",
    "with fl.SI_unit_system:\n",
    "\n",
    "    # Cylinder entities used for mesh refinements\n",
    "    cylinders = [\n",
    "        fl.Cylinder(\n",
    "            name=f\"cylinder{i+1}\",\n",
    "            axis=[0, 1, 0],\n",
    "            center=[0.7, 0.5, 0],\n",
    "            outer_radius=outer_radius,\n",
    "            height=1.0,\n",
    "        )\n",
    "        for i, outer_radius in enumerate([1.1, 2.2, 3.3, 4.5])\n",
    "    ]\n",
    "    cylinder5 = fl.Cylinder(\n",
    "        name=\"cylinder5\",\n",
    "        axis=[-1, 0, 0],\n",
    "        center=[6.5, 0.5, 0],\n",
    "        outer_radius=6.5,\n",
    "        height=10,\n",
    "    )\n",
    "\n",
    "    # Farfield used for creating a volume zone and assigning appropriate models\n",
    "    farfield = fl.AutomatedFarfield(name=\"farfield\", method=\"quasi-3d\")"
   ],
   "execution_count": 3,
   "outputs": [
    {
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     "data": {
      "text/html": [
       "
[14:27:20] INFO: using: SI unit system for unit inference.                                                         \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[14:27:20]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "fafd03cd-8a5c-49fb-85a9-31c4682b2654"
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   "source": [
    "### Meshing parameters\n",
    "In this section we will assign settings so that our mesh will have sufficient level of refinement for this case.\n",
    "\n",
    "Default settings describe how the mesh will be created globally. Refinements allow for much more precise control of the mesh in regions of interest.\n",
    "\n",
    "Here we will utilize the previously created cylinder entities, farfield, and we will also refer to geometry's faces and edges, which were grouped in the [Project creation](#Project-creation) step.\n",
    "\n",
    "As you can see in the image below, we define multiple refinements regions in order to better capture flow features around the geometry and in the wake region.\n",
    "\n",
    "![image_refinements](figures/2D_CRM_airfoil_refinement_cylinders.png)\n",
    "\n",
    "In the image below we can see the cylindrical flowfield volume zone created using the `fl.AutomatedFarfield` class with the 2D foil at its center. We can also see the effect of the wake refinement region behind the airfoil.\n",
    "\n",
    "\n",
    "![image_mesh_slice](figures/2D_CRM_farfield_slice.png)\n",
    "\n"
   ],
   "id": "c0a2c8c1-5716-493c-b5f3-db48f65bf422"
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   "metadata": {
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     "end_time": "2025-12-12T18:10:31.949347Z",
     "start_time": "2025-12-12T18:10:31.914739Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    meshing_params = fl.SimulationParams(\n",
    "        meshing=fl.MeshingParams(\n",
    "            # Default settings for meshing\n",
    "            defaults=fl.MeshingDefaults(\n",
    "                surface_edge_growth_rate=1.17,\n",
    "                surface_max_edge_length=1.1,\n",
    "                curvature_resolution_angle=12 * fl.u.deg,\n",
    "                boundary_layer_growth_rate=1.17,\n",
    "                boundary_layer_first_layer_thickness=1.8487111e-06,\n",
    "            ),\n",
    "            # Adjust all spacings in refinement regions\n",
    "            refinement_factor=1.35,\n",
    "            # Adjust narrow gap treatment strength for two surfaces in close proximity.\n",
    "            gap_treatment_strength=0.5,\n",
    "            # Crete new volume zones\n",
    "            volume_zones=[farfield],\n",
    "            # Additional fine-tuning for refinements on top of defaults\n",
    "            refinements=[\n",
    "                # cylindrical refinement regions to capture the flow around the foil\n",
    "                fl.UniformRefinement(name=\"refinement1\", spacing=0.1, entities=[cylinders[0]]),\n",
    "                fl.UniformRefinement(name=\"refinement2\", spacing=0.15, entities=[cylinders[1]]),\n",
    "                fl.UniformRefinement(name=\"refinement3\", spacing=0.225, entities=[cylinders[2]]),\n",
    "                fl.UniformRefinement(name=\"refinement4\", spacing=0.275, entities=[cylinders[3]]),\n",
    "                # cylindrical refinement region to capture the wake\n",
    "                fl.UniformRefinement(name=\"refinement5\", spacing=0.325, entities=[cylinder5]),\n",
    "                # Refine surface elements for given faces\n",
    "                fl.SurfaceRefinement(name=\"wing\", max_edge_length=0.74, faces=[geometry[\"wing\"]]),\n",
    "                fl.SurfaceRefinement(\n",
    "                    name=\"flap-slat\",\n",
    "                    max_edge_length=0.55,\n",
    "                    faces=[geometry[\"flap\"], geometry[\"slat\"]],\n",
    "                ),\n",
    "                fl.SurfaceRefinement(\n",
    "                    name=\"trailing\",\n",
    "                    max_edge_length=0.36,\n",
    "                    faces=[\n",
    "                        # '*' allows us to select all faces ending with 'Trailing'\n",
    "                        geometry[\"*Trailing\"],\n",
    "                    ],\n",
    "                ),\n",
    "                # Grows anisotropic layers orthogonal to the specified edges\n",
    "                fl.SurfaceEdgeRefinement(\n",
    "                    name=\"edges\",\n",
    "                    method=fl.HeightBasedRefinement(value=0.0007),\n",
    "                    edges=[\n",
    "                        # Similarly as above, we can select multiple edges using '*' expression\n",
    "                        geometry[\"*trailingEdge\"],\n",
    "                        geometry[\"*leadingEdge\"],\n",
    "                    ],\n",
    "                ),\n",
    "                fl.SurfaceEdgeRefinement(\n",
    "                    name=\"symmetry\",\n",
    "                    method=fl.ProjectAnisoSpacing(),\n",
    "                    edges=[geometry[\"symmetry\"]],\n",
    "                ),\n",
    "            ],\n",
    "        ),\n",
    "    )"
   ],
   "execution_count": 4,
   "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"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "1c9d7fd3-9963-43d7-b67a-40ffe4be1617"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reference geometry\n",
    "Now we will define the `reference_geometry` which is used to report forces such as drag and lift."
   ],
   "id": "105505a6-153b-48e1-8bb5-2f17ace1f4a9"
  },
  {
   "cell_type": "code",
   "metadata": {
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     "end_time": "2025-12-12T18:10:31.992867Z",
     "start_time": "2025-12-12T18:10:31.970705Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    reference_geometry_params = fl.SimulationParams(\n",
    "        # Create geometrical related reference values\n",
    "        reference_geometry=fl.ReferenceGeometry(\n",
    "            moment_center=[0.25, 0, 0], moment_length=[1, 1, 1], area=0.01\n",
    "        )\n",
    "    )"
   ],
   "execution_count": 5,
   "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"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "49bcc4fe-fbee-49e8-a300-4aef42cedbef"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operating condition\n",
    "Next up is the definition of flow conditions with the help of `fl.AerospaceCondition.from_mach_reynolds()` function. It does not require knowledge of fluid properties at the desired conditions and instead allows the usage of mach number in conjunction with reynolds number. This is particularly useful for replicating workshop cases which traditionally give Mach and Reynolds number flow conditions. Viscosity are density are going to be automatically calculated based on the input parameters."
   ],
   "id": "cf0d7a08-2427-4a48-8363-0da388f4e73b"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Note: In this case we are assigning the reynolds number based on the mesh unit length (1 meter). For convenience's sake, the foil is also built with a 1 meter chord. Thus the reynolds number based on chord length will be the same as the one based on mesh unit length."
   ],
   "id": "edf6af8e8ed77a95"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:10:32.165899Z",
     "start_time": "2025-12-12T18:10:32.019347Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    operating_condition_params = fl.SimulationParams(\n",
    "        # Create operating conditions using mach and reynolds number\n",
    "        operating_condition=fl.AerospaceCondition.from_mach_reynolds(\n",
    "            mach=0.2,\n",
    "            reynolds_mesh_unit=5e6,\n",
    "            temperature=272.1,\n",
    "            alpha=16 * fl.u.deg,\n",
    "            beta=0 * fl.u.deg,\n",
    "            project_length_unit=1 * fl.u.m,\n",
    "        ),\n",
    "    )"
   ],
   "execution_count": 6,
   "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"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "           created.                                                                                                \n",
       "
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      ],
      "text/plain": [
       "\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": {}
    }
   ],
   "id": "48bb6d71-298a-442e-80ff-9f6170ac6aab"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time stepping\n",
    "This simulation will be run as a Steady State case so we will use the `fl.Steady()` time stepping class.\n",
    "\n",
    "CFL is the Courant–Friedrichs–Lewy condition which we specify using the `fl.AdaptiveCFL()` class. This allows Flow360 to automatically adjust the CFL number during the simulation in order to achieve faster convergence. For more information about `fl.AdaptiveCFL()` and its usage, please refer to the [CFL settings](../../API_reference/time_stepping/cfl_settings.rst) documentation."
   ],
   "id": "3e60f956-5534-4fbb-8cf8-38e449a59c3e"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:10:32.211685Z",
     "start_time": "2025-12-12T18:10:32.194223Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    time_stepping_params = fl.SimulationParams(\n",
    "        # Time stepping settings for Steady State simulation\n",
    "        time_stepping=fl.Steady(max_steps=3000, CFL=fl.AdaptiveCFL()),\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"
      ]
     },
     "metadata": {}
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   ],
   "id": "00010667-5cc8-4ad0-8715-02e489e3cabb"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Models\n",
    "This parameter is used to define solver settings, numerical models and boundary conditions within our simulation.\n",
    "\n",
    "Please note that the `fl.AutomatedFarfield(name=\"farfield\", method=\"quasi-3d\")` call above automatically created the necessary surfaces for the farfield and symmetry planes, which we can now refer to when creating boundary conditions."
   ],
   "id": "e9d1ecc4-3bfc-4f0b-aeef-0ee14846627e"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:10:32.275230Z",
     "start_time": "2025-12-12T18:10:32.243583Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    models_params = fl.SimulationParams(\n",
    "        # Solver settings, numerical models and boundary conditions\n",
    "        models=[\n",
    "            # Create wall boundary condition\n",
    "            fl.Wall(\n",
    "                surfaces=[\n",
    "                    # Here we use '*' to select all surfaces\n",
    "                    geometry[\"*\"],\n",
    "                ],\n",
    "                name=\"wall\",\n",
    "            ),\n",
    "            # Create freestream boundary condition\n",
    "            fl.Freestream(surfaces=farfield.farfield, name=\"Freestream\"),\n",
    "            # Create slip wall boundary condition\n",
    "            fl.SlipWall(surfaces=farfield.symmetry_planes, name=\"slipwall\"),\n",
    "            # Create fluid volume model\n",
    "            fl.Fluid(\n",
    "                # Set up compressible Navier-Stokes solver\n",
    "                navier_stokes_solver=fl.NavierStokesSolver(\n",
    "                    linear_solver=fl.LinearSolver(max_iterations=35), kappa_MUSCL=0.33\n",
    "                ),\n",
    "                # Set up turbulence solver based on the Spalart-Allmaras model\n",
    "                turbulence_model_solver=fl.SpalartAllmaras(\n",
    "                    linear_solver=fl.LinearSolver(max_iterations=25),\n",
    "                    equation_evaluation_frequency=1,\n",
    "                ),\n",
    "            ),\n",
    "        ],\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"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "ab7f6a83-1144-4e6c-85f1-a4fe4ab2061d"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outputs\n",
    "Here we will specify which outputs we want Flow360 to produce for us."
   ],
   "id": "7a9009cf-79e9-457a-911d-d57c9d0222d5"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:10:32.443194Z",
     "start_time": "2025-12-12T18:10:32.307244Z"
    }
   },
   "source": [
    "# define the output fields we want\n",
    "volume_outputs = [\n",
    "    \"primitiveVars\",\n",
    "    \"vorticity\",\n",
    "    \"residualNavierStokes\",\n",
    "    \"residualTurbulence\",\n",
    "    \"Cp\",\n",
    "    \"Mach\",\n",
    "    \"qcriterion\",\n",
    "    \"mut\",\n",
    "]\n",
    "surface_outputs = [\n",
    "    \"primitiveVars\",\n",
    "    \"Cp\",\n",
    "    \"Cf\",\n",
    "    \"CfVec\",\n",
    "    \"yPlus\",\n",
    "]\n",
    "\n",
    "with fl.SI_unit_system:\n",
    "    outputs_params = fl.SimulationParams(\n",
    "        outputs=[\n",
    "            # Specify the volume field output in tecplot format\n",
    "            fl.VolumeOutput(\n",
    "                output_format=[\"tecplot\"],\n",
    "                name=\"fl.VolumeOutput\",\n",
    "                output_fields=volume_outputs,\n",
    "            ),\n",
    "            # Specify the surface output over the whole geometry in tecplot format\n",
    "            fl.SurfaceOutput(\n",
    "                output_format=[\"tecplot\"],\n",
    "                name=\"fl.SurfaceOutput\",\n",
    "                surfaces=geometry[\"*\"],\n",
    "                output_fields=surface_outputs,\n",
    "            ),\n",
    "            # Create a slice output at Y=0.005m in tecplot format.\n",
    "            # Because the extrusion direction is along the Y axis from y=0m to y=0.01m, this slice is at the mid-plane of the quasi-3D geometry.\n",
    "            fl.SliceOutput(\n",
    "                output_format=[\"tecplot\"],\n",
    "                output_fields=volume_outputs,\n",
    "                slices=[\n",
    "                    # define the fl.Slice from which we will be exporting the data\n",
    "                    fl.Slice(name=\"Y=0.005m slice\", normal=[0, 1, 0], origin=[0, 0.005, 0])\n",
    "                ],\n",
    "            ),\n",
    "        ],\n",
    "    )"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
\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": "5f4475e3-1c53-4771-8d31-2eb0d45e53b2"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combining parameters\n",
    "After having created various instances of the `SimulationParams` class, we can now combine them into one final instance containing all of the parameters defined previously.\n",
    "Flow360 allows us to refer to each parameter specified within `SimulationParams`, for example using `meshing_params.meshing`, we can access meshing parameters within that variable."
   ],
   "id": "03324835-4bcb-4a21-8a4c-477335747ab9"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:10:32.480323Z",
     "start_time": "2025-12-12T18:10:32.474394Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    params = fl.SimulationParams(\n",
    "        meshing=meshing_params.meshing,\n",
    "        reference_geometry=reference_geometry_params.reference_geometry,\n",
    "        operating_condition=operating_condition_params.operating_condition,\n",
    "        time_stepping=time_stepping_params.time_stepping,\n",
    "        models=models_params.models,\n",
    "        outputs=outputs_params.outputs,\n",
    "    )"
   ],
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: using: SI unit system for unit inference.                                                         \n",
       "
\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": "a5c42863-7b89-4888-b829-0ed9632ef6cd"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running the case\n",
    "With all of our parameters defined, we can pass them to our project and run the case."
   ],
   "id": "1eb241f7-a458-4196-a1a9-55f1b5de788f"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:10:34.579940Z",
     "start_time": "2025-12-12T18:10:32.521555Z"
    }
   },
   "source": [
    "case = project.run_case(params=params, name=\"Steady RANS\")"
   ],
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
           INFO: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "           created.                                                                                                \n",
       "
\n"
      ],
      "text/plain": [
       "\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",
       "
\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": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
[14:27:24] INFO: Successfully submitted:                                                                           \n",
       "                   type        = Case                                                                              \n",
       "                   name        = Case of tutorial 2D CRM from Python                                               \n",
       "                   id          = case-4efa0876-8163-4a90-aba4-c86b0709d7a9                                         \n",
       "                   status      = pending                                                                           \n",
       "                   project id  = prj-31637662-3d26-4038-bb41-69641da3a977                                          \n",
       "                                                                                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m[14:27:24]\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        = Case of tutorial 2D CRM from Python                                               \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93m4efa0876-8163-4a90-aba4-c86b0709d7a9\u001b[0m                                         \n",
       "\u001b[2;36m           \u001b[0m        status      = pending                                                                           \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m31637662-3d26-4038-bb41-69641da3a977\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "9e6f9fea-b904-4dfa-85f0-f89a97449efb"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post processing\n",
    "After we have successfully run the simulation, we can post process our results. We will use the `wait()` method to ensure that the case has finished running before we start to download the results and plot the variables of interest's convergence history."
   ],
   "id": "e219c6bf-b105-4fd7-b2cd-afb9e3bed721"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:14.053575Z",
     "start_time": "2025-12-12T18:10:34.617768Z"
    }
   },
   "source": [
    "case.wait()"
   ],
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba3adb9611a34541a152c365ee805b9b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {}
    }
   ],
   "id": "b7921723-0f67-4f06-a90d-ed49c5600e00"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we can download the results.\n",
    "We will be looking at the post processing of these quantities:\n",
    "- total forces\n",
    "- nonlinear residuals\n",
    "- CFL\n",
    "- total CD along the X axis"
   ],
   "id": "4ce6ac7b-6495-4758-8858-f36d0d4291a8"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [],
   "id": "a2d652f63845c83f"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:17.360879Z",
     "start_time": "2025-12-12T18:26:14.160214Z"
    }
   },
   "source": [
    "total_forces = case.results.total_forces\n",
    "total_forces = total_forces.as_dataframe()\n",
    "\n",
    "nonlinear_residuals = case.results.nonlinear_residuals\n",
    "nonlinear_residuals = nonlinear_residuals.as_dataframe()\n",
    "\n",
    "cfl = case.results.cfl\n",
    "cfl = cfl.as_dataframe()\n",
    "\n",
    "x_slicing_force_distribution = case.results.x_slicing_force_distribution\n",
    "x_slicing_force_distribution.wait()\n",
    "x_slicing_force_distribution = x_slicing_force_distribution.as_dataframe()"
   ],
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e33f45c8da44f8ba30f0529ab85c45c",
       "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": [
       "
[14:38:11] INFO: Saved to /tmp/tmpdge4mrr8/3e931599-a2aa-4c3a-ac12-d9045a401709.csv                                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[14:38:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpdge4mrr8/\u001b[0m\u001b[95m3e931599-a2aa-4c3a-ac12-d9045a401709.csv\u001b[0m                                \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12e835fb7faf4dcb8e5504322d313805",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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       "
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      ],
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     },
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       "
[14:38:12] INFO: Saved to /tmp/tmpdge4mrr8/31f72d87-2582-4ef4-bb05-508352253926.csv                                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[14:38:12]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpdge4mrr8/\u001b[0m\u001b[95m31f72d87-2582-4ef4-bb05-508352253926.csv\u001b[0m                                \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a511a87a626d411faaf03b769e57bd71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
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       "
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      ],
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     "metadata": {}
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      "text/html": [
       "
[14:38:13] INFO: Saved to /tmp/tmpdge4mrr8/bf873e8e-e098-4c6f-b9c3-4154de221ff9.csv                                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[14:38:13]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpdge4mrr8/\u001b[0m\u001b[95mbf873e8e-e098-4c6f-b9c3-4154de221ff9.csv\u001b[0m                                \n"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f23f1ce360e4d99b62f42205610d1aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
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     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
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      ],
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     "metadata": {}
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    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "
[14:38:14] INFO: Saved to /tmp/tmpdge4mrr8/1facbeb1-85c9-419a-8d61-d9fba2cac766.csv                                \n",
       "
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      ],
      "text/plain": [
       "\u001b[2;36m[14:38:14]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpdge4mrr8/\u001b[0m\u001b[95m1facbeb1-85c9-419a-8d61-d9fba2cac766.csv\u001b[0m                                \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "c713f8f6-3ec5-40a3-b927-adb1e2202ae0"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to obtain the X and Y variable names for our CFL plots, we will print out the CFL dataframe and look at its column names."
   ],
   "id": "38e8a311"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:17.434266Z",
     "start_time": "2025-12-12T18:26:17.427972Z"
    }
   },
   "source": [
    "print(cfl)"
   ],
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "     physical_step  pseudo_step  0_NavierStokes_cfl  1_SpalartAllmaras_cfl\n",
      "0                0            0            1.000000               1.000000\n",
      "1                0           10            1.103010               1.104622\n",
      "2                0           20            1.218409               1.220190\n",
      "3                0           30            1.345881               1.347849\n",
      "4                0           40            1.486690               1.488864\n",
      "..             ...          ...                 ...                    ...\n",
      "296              0         2960           45.452285              32.553325\n",
      "297              0         2970           45.451042              32.547860\n",
      "298              0         2980           45.450845              32.553560\n",
      "299              0         2990           45.451828              32.532039\n",
      "300              0         2999           45.453959              32.517152\n",
      "\n",
      "[301 rows x 4 columns]\n"
     ],
     "name": "stdout"
    }
   ],
   "id": "9010c839"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will look at how the non-linear residuals converge."
   ],
   "id": "b684107e-c3b7-460c-9cd2-c4f510283e22"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:17.796650Z",
     "start_time": "2025-12-12T18:26:17.482937Z"
    }
   },
   "source": [
    "ax = nonlinear_residuals.plot(\n",
    "    x=\"pseudo_step\",\n",
    "    y=[\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"],\n",
    "    logy=True,\n",
    "    xlabel=\"Pseudo Step\",\n",
    "    ylabel=\"residuals\",\n",
    "    figsize=(10, 5),\n",
    ")\n",
    "ax.grid(True)"
   ],
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "78cf25c4-a9a0-404d-8c80-f8eebd41e548"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we will look at the CFL values for both the Navier-Stokes solver and the turbulence model solver."
   ],
   "id": "78d55e8b-6ab0-42e7-9817-7e063c8f20a4"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:17.941741Z",
     "start_time": "2025-12-12T18:26:17.841614Z"
    }
   },
   "source": [
    "ax = cfl.plot(\n",
    "    x=\"pseudo_step\",\n",
    "    y=[\"0_NavierStokes_cfl\", \"1_SpalartAllmaras_cfl\"],\n",
    "    xlim=(0, 3000),\n",
    "    figsize=(10, 5),\n",
    ")\n",
    "ax.grid(True)"
   ],
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "af56abbb-4059-4276-a69a-8ad84cd4a37a"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another popular form of checking conversion convergence for aerodynamic applications is monitoring how the CL and CD values stabilize during the simulation."
   ],
   "id": "b9dd0787-981c-4ea1-b5da-a8388be2255b"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:18.164954Z",
     "start_time": "2025-12-12T18:26:17.983332Z"
    }
   },
   "source": [
    "ax = total_forces.plot(\n",
    "    x=\"pseudo_step\",\n",
    "    y=\"CL\",\n",
    "    xlabel=\"Pseudo Step\",\n",
    "    ylabel=\"CL\",\n",
    "    figsize=(10, 5),\n",
    "    style=\"orange\",\n",
    ")\n",
    "ax.grid(True)\n",
    "\n",
    "ax = total_forces.plot(\n",
    "    x=\"pseudo_step\",\n",
    "    y=\"CD\",\n",
    "    xlabel=\"Pseudo Step\",\n",
    "    ylabel=\"CD\",\n",
    "    figsize=(10, 5),\n",
    "    style=\"lightgreen\",\n",
    ")\n",
    "ax.grid(True)"
   ],
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "8d4c2a4e-40f0-46b5-84da-f0ad037ef237"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we will look at how the CD varies along the chord of the 2D foil."
   ],
   "id": "15ffff77-724d-49f3-ae9a-ea60faf663b4"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-12T18:26:18.322318Z",
     "start_time": "2025-12-12T18:26:18.213185Z"
    }
   },
   "source": [
    "ax = x_slicing_force_distribution.plot(\n",
    "    x=\"X\", y=\"totalCumulative_CD_Curve\", xlabel=\"X\", ylabel=\"CD\", figsize=(10, 5)\n",
    ")\n",
    "ax.grid(True)"
   ],
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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