{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Periodic BCs\n",
    "\n",
    "![image](../thumbnails/periodic_turbo.png)\n",
    "\n",
    "This notebook sets up a rotationally periodic steady-state simulation of the [TU Berlin TurboLab Stator](https://www.caeses.com/wp-content/uploads/2016/07/TU_Berlin_Stator.pdf \"TU berlin stator\") using the Flow360 Python API. It focuses on applying **Periodic Boundary Conditions** using rotational periodicity solver settings.\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": "d0170a6464c6b9b7"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Imports and Project Setup\n",
    "\n",
    "We import the necessary Flow360 libraries and load the project geometry from the pre-existing volume mesh file provided by the `TutorialPeriodicBC` example module. The project is created directly from a pre generated volume mesh. The grid unit length is in millimeters (mm)."
   ],
   "id": "441d4c4787460af8"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.629637Z",
     "start_time": "2025-12-02T19:08:06.075221Z"
    },
    "tags": [
     "setup"
    ]
   },
   "source": [
    "import flow360 as fl\n",
    "from flow360.examples import TutorialPeriodicBC\n",
    "\n",
    "# Fetch the necessary volume mesh file\n",
    "TutorialPeriodicBC.get_files()\n",
    "\n",
    "# Create the project from the pre-existing volume mesh\n",
    "project = fl.Project.from_volume_mesh(\n",
    "    TutorialPeriodicBC.mesh_filename,\n",
    "    length_unit=\"mm\",\n",
    "    name=\"Periodic BCs\",\n",
    ")\n",
    "\n",
    "# Get the volume mesh object to define surfaces for models\n",
    "volume_mesh = project.volume_mesh"
   ],
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "display_data",
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      },
      "text/plain": [
       "Output()"
      ]
     },
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    },
    {
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:27:44] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: VolumeMesh successfully submitted:                                                                \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Volume Mesh                                                                       \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Tutorial Periodic Boundary Condition from Python                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        id          = vm-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">e82bc122-d82d-449a-9bab-23b3ec2fcff4</span>                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = uploaded                                                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">6d073a5d-7cca-4517-b25d-59ca8ae66623</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:27:44]\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        = Tutorial Periodic Boundary Condition from Python                                  \n",
       "\u001b[2;36m           \u001b[0m        id          = vm-\u001b[93me82bc122-d82d-449a-9bab-23b3ec2fcff4\u001b[0m                                           \n",
       "\u001b[2;36m           \u001b[0m        status      = uploaded                                                                          \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m6d073a5d-7cca-4517-b25d-59ca8ae66623\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {}
    }
   ],
   "id": "e2067e437e9cfc6b"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## 2. Define the **operating_condition** simulation parameter\n",
    "\n",
    "We define the operating condition based on Mach number and Reynolds number. All definitions are in the standard SI unit system with the Reynolds reference length being the grid unit (mm)."
   ],
   "id": "c01bf3a9f2e46b04"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.647809300Z",
     "start_time": "2025-12-01T20:32:04.348368Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "\n",
    "    # Calculate full thermodynamic state from Mach and Reynolds numbers\n",
    "    operating_condition = fl.AerospaceCondition.from_mach_reynolds(\n",
    "        mach=0.13989,\n",
    "        reynolds_mesh_unit=3200,\n",
    "        project_length_unit=1 * fl.u.mm,\n",
    "        temperature=298.25 * fl.u.K,\n",
    "    )"
   ],
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:28:03] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:28:03]\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": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>created.                                                                                                \n",
       "</pre>\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": {}
    }
   ],
   "id": "3e810b48ae1cde8b"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## 3. Define solver settings through the **Fluid** model\n",
    "\n",
    "We define detailed settings for the steady-state Navier-Stokes and Spalart-Allmaras turbulence solvers, including absolute tolerances, linear solver limits, and 2nd order accuracy."
   ],
   "id": "23cb09f1121f39d6"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.650418800Z",
     "start_time": "2025-12-01T20:32:05.100977Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    # Configure the Navier-Stokes solver for steady flow\n",
    "    navier_stokes_solver = fl.NavierStokesSolver(\n",
    "        absolute_tolerance=1e-11,\n",
    "        linear_solver=fl.LinearSolver(max_iterations=20),\n",
    "        order_of_accuracy=2,\n",
    "        kappa_MUSCL=0.33,\n",
    "        update_jacobian_frequency=1,\n",
    "        equation_evaluation_frequency=1,\n",
    "        numerical_dissipation_factor=1,\n",
    "    )\n",
    "\n",
    "    # Configure the Spalart-Allmaras turbulence model solver\n",
    "    turbulence_model_solver = fl.SpalartAllmaras(\n",
    "        absolute_tolerance=1e-10,\n",
    "        linear_solver=fl.LinearSolver(max_iterations=20),\n",
    "        update_jacobian_frequency=1,\n",
    "        equation_evaluation_frequency=1,\n",
    "        order_of_accuracy=2,\n",
    "    )\n",
    "\n",
    "    # Combine into a Fluid model\n",
    "    fluid_model = fl.Fluid(\n",
    "        navier_stokes_solver=navier_stokes_solver,\n",
    "        turbulence_model_solver=turbulence_model_solver,\n",
    "    )"
   ],
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "1abd4164eb036841"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## 4. Define Boundary Condition Models\n",
    "\n",
    "We assign boundary conditions to the surfaces defined in the volume mesh, including a crucial **Periodic** condition with rotational specification for the passage walls."
   ],
   "id": "60b802e4bea15774"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.662836800Z",
     "start_time": "2025-12-01T20:32:05.690549Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    models_list = [\n",
    "        fluid_model,\n",
    "        # Wall surfaces (vane, blade fillet, shroud, hub)\n",
    "        fl.Wall(\n",
    "            surfaces=[\n",
    "                volume_mesh[\"fluid/vane_*\"],\n",
    "                volume_mesh[\"fluid/bladeFillet_*\"],\n",
    "                volume_mesh[\"fluid/shroud\"],\n",
    "                volume_mesh[\"fluid/hub\"],\n",
    "            ]\n",
    "        ),\n",
    "        # Freestream inlet condition\n",
    "        fl.Freestream(\n",
    "            surfaces=[\n",
    "                volume_mesh[\"fluid/inlet\"],\n",
    "            ]\n",
    "        ),\n",
    "        # Outflow condition with specified static pressure\n",
    "        fl.Outflow(\n",
    "            surfaces=[\n",
    "                volume_mesh[\"fluid/outlet\"],\n",
    "            ],\n",
    "            spec=fl.Pressure(\n",
    "                1.0032 * operating_condition.thermal_state.pressure\n",
    "            ),  # Slightly above ambient\n",
    "        ),\n",
    "        # Slip walls for computational domain boundaries (top/bottom)\n",
    "        fl.SlipWall(\n",
    "            surfaces=[\n",
    "                volume_mesh[\"fluid/bottomFront\"],\n",
    "                volume_mesh[\"fluid/topFront\"],\n",
    "            ]\n",
    "        ),\n",
    "    ]"
   ],
   "execution_count": 4,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "4b10c3d42b605379"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define the **Periodic** Boundary Condition\n",
    "\n",
    "The periodic boundary condition connects two sets of surfaces in the mesh that are opposite each other. We need to specify the two sets of faces and that the periodicity is rotational around the X-axis.\n",
    "\n",
    "For more information on the **periodic** boundary conditions, refer to the [Surface Models](../../API_reference/surface_models/surface_model_index.rst) documentation."
   ],
   "id": "5decfe83c2cc08a4"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.664843Z",
     "start_time": "2025-12-01T20:32:06.385876Z"
    }
   },
   "source": [
    "# Here we define the Periodic BC to connect the two periodic surfaces in the mesh.\n",
    "models_list.append(\n",
    "    # Rotational Periodic Boundary Condition\n",
    "    fl.Periodic(\n",
    "        surface_pairs=[\n",
    "            (volume_mesh[\"fluid/periodic-1\"], volume_mesh[\"fluid/periodic-2\"])\n",
    "        ],  # Connects the two radially oppose mesh surfaces\n",
    "        spec=fl.Rotational(axis_of_rotation=(1, 0, 0)),  # Specify rotation around X-axis\n",
    "    ),\n",
    ")"
   ],
   "execution_count": 5,
   "outputs": [],
   "id": "43f6462a46fe245"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## 5. Define slice locations and outputs\n",
    "\n",
    "We specify the desired slice locations and the output fields for volume data (**tecplot** format), surface data, and slice data. This ensures we capture all relevant flow physics for post-processing."
   ],
   "id": "696a58fdc30fbff1"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.664843Z",
     "start_time": "2025-12-01T20:32:07.120258Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    # Define slice planes for visualization and data extraction\n",
    "    slice_inlet = fl.Slice(\n",
    "        name=\"Inlet\",\n",
    "        normal=[1, 0, 0],\n",
    "        origin=[-179, 0, 0] * fl.u.m,\n",
    "    )\n",
    "    slice_outlet = fl.Slice(\n",
    "        name=\"Outlet\",\n",
    "        normal=[1, 0, 0],\n",
    "        origin=[539, 0, 0] * fl.u.m,\n",
    "    )\n",
    "    slice_trailing_edge = fl.Slice(\n",
    "        name=\"TrailingEdge\",\n",
    "        normal=[1, 0, 0],\n",
    "        origin=[183, 0, 0] * fl.u.m,\n",
    "    )\n",
    "    slice_wake = fl.Slice(\n",
    "        name=\"Wake\",\n",
    "        normal=[1, 0, 0],\n",
    "        origin=[294.65, 0, 0] * fl.u.m,\n",
    "    )\n",
    "    outputs_list = [\n",
    "        # Full Volume Output for 3D visualization (Tecplot recommended)\n",
    "        fl.VolumeOutput(\n",
    "            output_format=[\"tecplot\"],\n",
    "            output_fields=[\n",
    "                \"primitiveVars\",\n",
    "                \"vorticity\",\n",
    "                \"Cp\",\n",
    "                \"Mach\",\n",
    "                \"qcriterion\",\n",
    "                \"mut\",\n",
    "                \"nuHat\",\n",
    "                \"mutRatio\",\n",
    "                \"gradW\",\n",
    "                \"T\",\n",
    "                \"residualNavierStokes\",\n",
    "            ],\n",
    "        ),\n",
    "        # Surface Output for wall quantities\n",
    "        fl.SurfaceOutput(\n",
    "            surfaces=volume_mesh[\"*\"],\n",
    "            output_format=[\"paraview\", \"tecplot\"],\n",
    "            output_fields=[\n",
    "                \"primitiveVars\",\n",
    "                \"Cp\",\n",
    "                \"Cf\",\n",
    "                \"CfVec\",\n",
    "                \"yPlus\",\n",
    "                \"nodeForcesPerUnitArea\",\n",
    "            ],\n",
    "        ),\n",
    "        # Slice Output for 2D flow field investigation\n",
    "        fl.SliceOutput(\n",
    "            slices=[slice_inlet, slice_outlet, slice_trailing_edge, slice_wake],\n",
    "            output_format=[\"paraview\", \"tecplot\"],\n",
    "            output_fields=[\"Cp\", \"primitiveVars\", \"T\", \"Mach\", \"gradW\"],\n",
    "        ),\n",
    "    ]"
   ],
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "9e3756a56973aff6"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## 6. Construct Final Simulation Parameters\n",
    "\n",
    "We combine all the previously defined components—geometry, operating condition, time stepping (Steady), models, and outputs—into the final `SimulationParams` object."
   ],
   "id": "cad424a45459b8c6"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.679473900Z",
     "start_time": "2025-12-01T20:32:07.777696Z"
    }
   },
   "source": [
    "with fl.SI_unit_system:\n",
    "    params = fl.SimulationParams(\n",
    "        reference_geometry=fl.ReferenceGeometry(\n",
    "            moment_center=[0, 0, 0], moment_length=[1, 1, 1], area=209701.3096271187\n",
    "        ),\n",
    "        operating_condition=operating_condition,\n",
    "        time_stepping=fl.Steady(max_steps=5000, CFL=fl.AdaptiveCFL()),\n",
    "        models=models_list,\n",
    "        outputs=outputs_list,\n",
    "    )"
   ],
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "6e77415c884da206"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "## 7. Run the Case 🏃\n",
    "\n",
    "Finally, we submit the simulation using the configured parameters to Flow360."
   ],
   "id": "1d4d97f1f61b7aab"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.680485600Z",
     "start_time": "2025-12-01T20:32:08.404990Z"
    }
   },
   "source": [
    "case = project.run_case(params=params, name=\"Steady periodic channel\")"
   ],
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Density and viscosity were calculated based on input data, ThermalState will be automatically     \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>created.                                                                                                \n",
       "</pre>\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": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: using: SI unit system for unit inference.                                                         \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: using: SI unit system for unit inference.                                                         \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:28:05] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Successfully submitted:                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        type        = Case                                                                              \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        name        = Tutorial Periodic Boundary Condition from Python                                  \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        id          = case-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">994ad1dc-1fc0-4e42-9d25-2e609778f2e8</span>                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        status      = pending                                                                           \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>        project id  = prj-<span style=\"color: #ffff00; text-decoration-color: #ffff00\">6d073a5d-7cca-4517-b25d-59ca8ae66623</span>                                          \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">           </span>                                                                                                        \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:28:05]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Successfully submitted:                                                                           \n",
       "\u001b[2;36m           \u001b[0m        type        = Case                                                                              \n",
       "\u001b[2;36m           \u001b[0m        name        = Tutorial Periodic Boundary Condition from Python                                  \n",
       "\u001b[2;36m           \u001b[0m        id          = case-\u001b[93m994ad1dc-1fc0-4e42-9d25-2e609778f2e8\u001b[0m                                         \n",
       "\u001b[2;36m           \u001b[0m        status      = pending                                                                           \n",
       "\u001b[2;36m           \u001b[0m        project id  = prj-\u001b[93m6d073a5d-7cca-4517-b25d-59ca8ae66623\u001b[0m                                          \n",
       "\u001b[2;36m           \u001b[0m                                                                                                        \n"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "f4c71aeed26f9975"
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While we wait for the case to finish, we invite you to monitor the case in the Flow360 GUI. Once finished, you can easily post-process the results after completion."
   ],
   "id": "7b98b350968da4cd"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-02T19:08:23.683015900Z",
     "start_time": "2025-12-02T19:07:54.238551Z"
    }
   },
   "source": [
    "# wait until the case finishes execution\n",
    "case.wait()\n",
    "\n",
    "results = case.results\n",
    "\n",
    "# nonlinear residuals contain convergence information\n",
    "nonlinear_residuals = results.nonlinear_residuals.as_dataframe()\n",
    "ax = nonlinear_residuals.plot(\n",
    "    x=\"pseudo_step\",\n",
    "    y=[\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"],\n",
    "    xlim=(0, None),\n",
    "    xlabel=\"Pseudo Step\",\n",
    "    secondary_y=[\"5_nuHat\"],\n",
    "    figsize=(10, 7),\n",
    "    logy=True,\n",
    "    title=\"Nonlinear residuals\",\n",
    ")\n",
    "ax.grid(True)"
   ],
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30ff32dcdd6347bb8669e4cb830d961d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6259fa41d094c329b05c02f1e309550",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[12:33:31] </span><span style=\"color: #008080; text-decoration-color: #008080\">INFO</span>: Saved to <span style=\"color: #800080; text-decoration-color: #800080\">/tmp/tmpk0dinw1f/</span><span style=\"color: #ff00ff; text-decoration-color: #ff00ff\">d01c7941-039c-4eda-bd68-fef55912ced2.csv</span>                                \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m[12:33:31]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mINFO\u001b[0m: Saved to \u001b[35m/tmp/tmpk0dinw1f/\u001b[0m\u001b[95md01c7941-039c-4eda-bd68-fef55912ced2.csv\u001b[0m                                \n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "id": "7627a1051746e48f"
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [],
   "execution_count": null,
   "outputs": [],
   "id": "8412faf8031b7520"
  }
 ],
 "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
}
