{
"cells": [
{
"cell_type": "markdown",
"id": "212cac3f",
"metadata": {},
"source": [
"# Hinge Torques Monitoring\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."
]
},
{
"cell_type": "markdown",
"id": "db0eec14",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "ae363631",
"metadata": {},
"source": [
"This tutorial will show how to set up a case where moments on specific surfaces are monitored throughout the simulation. The content will cover:\n",
"- creating a project from a geometry,\n",
"- meshing using the beta mesher and automated farfield,\n",
"- setup of a steady case,\n",
"- setup of `SurfaceIntegralOutput` using `UserVariable` to capture hinge moments on control surfaces,\n",
"- results postprocessing."
]
},
{
"cell_type": "markdown",
"id": "876f7dc7",
"metadata": {},
"source": [
"## 1. Imports and Project Creation\n",
"\n",
"To begin working with the **Flow360** API it is necessary to firstly import the `flow360` python package that will be the interface between you and the software. The files necessary for this example are being downloaded through the `TutorialUDDForcesMoments` example. \n",
"\n",
"All simulations are managed through the `Project` object, which stores all the runs associated with a certain root asset. Root asset can be a geometry, surface mesh or volume mesh. In case of this tutorial the `Project` will be started from the geometry."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "b8542815",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:17:22.011738Z",
"iopub.status.busy": "2026-07-14T10:17:22.011648Z",
"iopub.status.idle": "2026-07-14T10:17:22.017318Z",
"shell.execute_reply": "2026-07-14T10:17:22.016808Z"
}
},
"outputs": [],
"source": [
"import flow360 as fl\n",
"from flow360.examples import TutorialUDDForcesMoments"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c24464c9",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:17:22.018307Z",
"iopub.status.busy": "2026-07-14T10:17:22.018219Z",
"iopub.status.idle": "2026-07-14T10:18:12.976270Z",
"shell.execute_reply": "2026-07-14T10:18:12.975814Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:17:22] INFO: The file (UDD_FM_airplane.csm) is being downloaded, please \n",
" wait. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" WARNING: DeprecationWarning: `length_unit` was not specified and \n",
" defaults to 'm'. Relying on this default is deprecated and will be \n",
" removed in a future release; specify the length unit of your \n",
" geometry/mesh explicitly to avoid silent unit/scale mistakes. \n"
]
},
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:17:39] INFO: Geometry successfully submitted: \n",
" type = Geometry \n",
" name = Hinge Torques Monitoring \n",
" id = geo-3996703c-31fc-42d1-a886-8afca1f752de \n",
" status = uploaded \n",
" project id = prj-cd69ce66-3cf6-450c-b31e-d9c62cb0f2a2 \n",
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: Waiting for geometry to be processed. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[?25h"
]
}
],
"source": [
"TutorialUDDForcesMoments.get_files()\n",
"\n",
"project = fl.Project.from_geometry(\n",
" TutorialUDDForcesMoments.geometry, name=\"Hinge Torques Monitoring\"\n",
")"
]
},
{
"cell_type": "markdown",
"id": "61daeb53",
"metadata": {},
"source": [
"If the `Project` has already been created before it is possible to retrieve it using its unique ID."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6909cc0e",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:12.977727Z",
"iopub.status.busy": "2026-07-14T10:18:12.977637Z",
"iopub.status.idle": "2026-07-14T10:18:12.979301Z",
"shell.execute_reply": "2026-07-14T10:18:12.978917Z"
}
},
"outputs": [],
"source": [
"# project=fl.Project.from_cloud(\"prj-e8aa4e7a-a6b8-47f5-97c4-7e64bbf76aeb\")"
]
},
{
"cell_type": "markdown",
"id": "b630c2fc",
"metadata": {},
"source": [
"`Project` contains all the information about its root asset, in this case - geometry. To be able to correctly reference it it is necessary to group the faces and edges by the attribute that was assigned to distinguish the faces in a CAD file. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ff6f95dd",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:12.980244Z",
"iopub.status.busy": "2026-07-14T10:18:12.980167Z",
"iopub.status.idle": "2026-07-14T10:18:12.987428Z",
"shell.execute_reply": "2026-07-14T10:18:12.987018Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:18:12] INFO: Regrouping face entities under `groupName` tag (previous \n",
" `faceId`). \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Geometry boundaries:\n",
"- fuselage\n",
"- wingRight\n",
"- wingLeft\n",
"- vTailRight\n",
"- vTailLeft\n",
"- rudderLeft\n",
"- rudderRight\n",
"- aileronLeft\n",
"- aileronRight\n"
]
}
],
"source": [
"geometry = project.geometry\n",
"\n",
"geometry.group_edges_by_tag(\"edgeName\")\n",
"geometry.group_faces_by_tag(\"groupName\")\n",
"\n",
"print(\"Geometry boundaries:\")\n",
"for boundary in geometry.entity_info.get_boundaries():\n",
" print(f\"- {boundary.name}\")"
]
},
{
"cell_type": "markdown",
"id": "b1ba1819",
"metadata": {},
"source": [
"## 2. Meshing\n",
"When the projects starts from the geometry it is necessary to set up the automated meshing workflow.\n",
"\n",
"Firstly the AutomatedFarfield object is created to indicate that the geometry only contains the simulated object and the farfield has to be created around it.\n",
"\n",
"Then the defaults for meshing are defined. Those are the \"global\" settings for the mesher."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "13df1c5b",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:12.988440Z",
"iopub.status.busy": "2026-07-14T10:18:12.988356Z",
"iopub.status.idle": "2026-07-14T10:18:12.995172Z",
"shell.execute_reply": "2026-07-14T10:18:12.994723Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: using: SI unit system for unit inference. \n"
]
}
],
"source": [
"farfield = fl.AutomatedFarfield()\n",
"\n",
"with fl.SI_unit_system:\n",
" meshing = fl.MeshingParams(\n",
" defaults=fl.MeshingDefaults(\n",
" surface_max_edge_length=0.5,\n",
" curvature_resolution_angle=10 * fl.u.deg,\n",
" boundary_layer_first_layer_thickness=2e-6,\n",
" boundary_layer_growth_rate=1.2,\n",
" ),\n",
" volume_zones=[farfield],\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "95e7d8b7",
"metadata": {},
"source": [
"To refine specific parts of the mesh the `refinements` field is used, where user can define surface (such as `SurfaceRefinement` and `SurfaceEdgeRefinement`) and volumetric refinements (`UniformRefinement`).\n",
"\n",
"It is worth to notice how the specific parts of the geometry are referenced using the `geometry` object. They can be then referenced using face names directly or through a wildcard entry (`*`). "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "34a45fee",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:12.996408Z",
"iopub.status.busy": "2026-07-14T10:18:12.996324Z",
"iopub.status.idle": "2026-07-14T10:18:13.009153Z",
"shell.execute_reply": "2026-07-14T10:18:13.008590Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: using: SI unit system for unit inference. \n"
]
}
],
"source": [
"with fl.SI_unit_system:\n",
" box1 = fl.Box(name=\"box1\", size=[2, 6, 3], center=[6.5, 9, 0], axis_of_rotation=[0, 1, 0])\n",
" box2 = fl.Box(name=\"box2\", size=[2, 6, 3], center=[6.5, -9, 0], axis_of_rotation=[0, 1, 0])\n",
" box3 = fl.Box(name=\"box3\", size=[4, 8, 3], center=[12, 0, 2], axis_of_rotation=[0, 1, 0])\n",
"\n",
" meshing.refinements.extend(\n",
" [\n",
" fl.SurfaceEdgeRefinement(\n",
" method=fl.AngleBasedRefinement(value=1 * fl.u.deg),\n",
" edges=[geometry[\"leadingEdge\"]],\n",
" ),\n",
" fl.SurfaceEdgeRefinement(\n",
" method=fl.HeightBasedRefinement(value=5e-3),\n",
" edges=[geometry[\"trailingEdge\"]],\n",
" ),\n",
" fl.SurfaceRefinement(max_edge_length=0.5, faces=[geometry[\"wing*\"]]),\n",
" fl.UniformRefinement(name=\"box_refinement1\", entities=[box1, box2, box3], spacing=0.2),\n",
" ]\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "397fe4d3",
"metadata": {},
"source": [
"## 3. Boundary Conditions, Flow physics and numerics settings\n",
"\n",
"To describe the physical behaviour of the flow the Boundary Conditions or surface models are used. In this case the ones needed would be the `Freestream` condition that is assigned to the farfield and the `Wall` condition for the object. The `Fluid` model is a volume model that enables access to the controls of the fluid solver.\n",
"\n",
"The `operating_condition` is responsible for defining the fluid behaviour at the freestream and is used to set the velocity, angle of attack and the thermal state of the fluid in this case.\n",
"\n",
"`reference_geometry` defines values that are used as a reference when calculating various coefficients. \n",
"\n",
"Finally `time_stepping` describes whether the simulation is steady or transient and how many time (or pseudo time) steps should be performed."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "60efc886",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:13.010253Z",
"iopub.status.busy": "2026-07-14T10:18:13.010179Z",
"iopub.status.idle": "2026-07-14T10:18:13.189309Z",
"shell.execute_reply": "2026-07-14T10:18:13.188761Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:18:13] INFO: using: SI unit system for unit inference. \n"
]
}
],
"source": [
"with fl.SI_unit_system:\n",
" reference_geometry = fl.ReferenceGeometry(\n",
" area=60, moment_center=[5.7542, 0, 0], moment_length=[1, 1, 1]\n",
" )\n",
" operating_condition = fl.AerospaceCondition(\n",
" velocity_magnitude=50,\n",
" alpha=10 * fl.u.deg,\n",
" atmosphere=fl.ThermalState(temperature=288.15),\n",
" )\n",
" models = [\n",
" fl.Fluid(\n",
" navier_stokes_solver=fl.NavierStokesSolver(\n",
" absolute_tolerance=1e-9,\n",
" linear_solver=fl.LinearSolver(max_iterations=35),\n",
" ),\n",
" turbulence_model_solver=fl.SpalartAllmaras(\n",
" linear_solver=fl.LinearSolver(max_iterations=25)\n",
" ),\n",
" ),\n",
" fl.Wall(\n",
" surfaces=[geometry[\"*Left\"], geometry[\"*Right\"], geometry[\"fuselage\"]],\n",
" ),\n",
" fl.Freestream(surfaces=[farfield.farfield]),\n",
" ]\n",
" time_stepping = fl.Steady(max_steps=5000)"
]
},
{
"cell_type": "markdown",
"id": "2b9a8efd",
"metadata": {},
"source": [
"## 4. Definition of `UserVariable`s\n",
"\n",
"In the next cell, we define several custom `UserVariable`s to compute hinge torques for the control surfaces (right/left ailerons and right/left rudders). \n",
"Each variable calculates the local contribution to the hinge torque by taking the moment (cross product) of the force per unit area about a specified torque center, and then projecting that moment onto the relevant hinge axis. The resulting user variables represent the hinge torque per unit area at each surface node, \n",
"with units of pressure times length (Pa·m), and can be integrated over the relevant surfaces to obtain the total hinge torque values.\n",
"\n",
">Note: The variables are defined **per unit area** because `SurfaceIntegralOutput` integrates over the surface area, not over individual nodes."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "92e728f8",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:13.190852Z",
"iopub.status.busy": "2026-07-14T10:18:13.190727Z",
"iopub.status.idle": "2026-07-14T10:18:13.234813Z",
"shell.execute_reply": "2026-07-14T10:18:13.234220Z"
}
},
"outputs": [],
"source": [
"right_aileron_torque_center = [5.7542, 7, 0] * fl.u.m\n",
"right_aileron_torque_axis = [0, 1, 0]\n",
"\n",
"right_aileron_hinge_torque_partial_dim = fl.UserVariable(\n",
" name=\"right_aileron_hinge_torque_partial\",\n",
" value=fl.math.dot(\n",
" fl.math.cross(\n",
" fl.math.subtract(fl.solution.coordinate, right_aileron_torque_center),\n",
" fl.solution.node_forces_per_unit_area,\n",
" ),\n",
" right_aileron_torque_axis,\n",
" ),\n",
").in_units(new_unit=fl.u.Pa * fl.u.m)\n",
"\n",
"left_aileron_torque_center = [5.7542, -7, 0] * fl.u.m\n",
"left_aileron_torque_axis = [0, -1, 0]\n",
"\n",
"left_aileron_hinge_torque_partial_dim = fl.UserVariable(\n",
" name=\"left_aileron_hinge_torque_partial\",\n",
" value=fl.math.dot(\n",
" fl.math.cross(\n",
" fl.math.subtract(fl.solution.coordinate, left_aileron_torque_center),\n",
" fl.solution.node_forces_per_unit_area,\n",
" ),\n",
" left_aileron_torque_axis,\n",
" ),\n",
").in_units(new_unit=fl.u.Pa * fl.u.m)\n",
"\n",
"right_rudder_torque_center = [12.01, 0.861, 0.861] * fl.u.m\n",
"right_rudder_torque_axis = [0, fl.math.sqrt(2) / 2, fl.math.sqrt(2) / 2]\n",
"\n",
"right_rudder_hinge_torque_partial_dim = fl.UserVariable(\n",
" name=\"right_rudder_hinge_torque_partial\",\n",
" value=fl.math.dot(\n",
" fl.math.cross(\n",
" fl.math.subtract(fl.solution.coordinate, right_rudder_torque_center),\n",
" fl.solution.node_forces_per_unit_area,\n",
" ),\n",
" right_rudder_torque_axis,\n",
" ),\n",
").in_units(new_unit=fl.u.Pa * fl.u.m)\n",
"\n",
"left_rudder_torque_center = [12.01, -0.861, 0.861] * fl.u.m\n",
"left_rudder_torque_axis = [0, -fl.math.sqrt(2) / 2, fl.math.sqrt(2) / 2]\n",
"\n",
"left_rudder_hinge_torque_partial_dim = fl.UserVariable(\n",
" name=\"left_rudder_hinge_torque_partial\",\n",
" value=fl.math.dot(\n",
" fl.math.cross(\n",
" fl.math.subtract(fl.solution.coordinate, left_rudder_torque_center),\n",
" fl.solution.node_forces_per_unit_area,\n",
" ),\n",
" left_rudder_torque_axis,\n",
" ),\n",
").in_units(new_unit=fl.u.Pa * fl.u.m)"
]
},
{
"cell_type": "markdown",
"id": "e68a0299",
"metadata": {},
"source": [
"Below, each hinge torque user variable (representing the torque per unit area contribution) is integrated over the corresponding control surface using `SurfaceIntegralOutput`. These integrals compute the total hinge torque for each control surface (right/left aileron and rudder) by summing their local torque contributions defined above.\n",
"\n",
"The `output_fields` for each surface integral specify which user variable is integrated, and the `surfaces` parameter specifies which part of the geometry the integration is applied to."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b50144da",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:13.236062Z",
"iopub.status.busy": "2026-07-14T10:18:13.235972Z",
"iopub.status.idle": "2026-07-14T10:18:13.238653Z",
"shell.execute_reply": "2026-07-14T10:18:13.238189Z"
}
},
"outputs": [],
"source": [
"outputs = [\n",
" fl.SurfaceIntegralOutput(\n",
" name=\"right_aileron_hinge_torque\",\n",
" output_fields=[right_aileron_hinge_torque_partial_dim],\n",
" surfaces=geometry[\"aileronRight\"],\n",
" ),\n",
" fl.SurfaceIntegralOutput(\n",
" name=\"left_aileron_hinge_torque\",\n",
" output_fields=[left_aileron_hinge_torque_partial_dim],\n",
" surfaces=geometry[\"aileronLeft\"],\n",
" ),\n",
" fl.SurfaceIntegralOutput(\n",
" name=\"right_rudder_hinge_torque\",\n",
" output_fields=[right_rudder_hinge_torque_partial_dim],\n",
" surfaces=geometry[\"rudderRight\"],\n",
" ),\n",
" fl.SurfaceIntegralOutput(\n",
" name=\"left_rudder_hinge_torque\",\n",
" output_fields=[left_rudder_hinge_torque_partial_dim],\n",
" surfaces=geometry[\"rudderLeft\"],\n",
" ),\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "e2f05891",
"metadata": {},
"source": [
"## 5. `SimulationParams`\n",
"\n",
"Finally all of the previously defined fields are input to the `SimulationParams` object that defines a whole process from meshing to performing the simulation.\n",
"\n",
"After preparing those parameters the simulation can be kicked off using the `run_case` command."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "594ee6a4",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:13.239749Z",
"iopub.status.busy": "2026-07-14T10:18:13.239677Z",
"iopub.status.idle": "2026-07-14T10:18:17.516921Z",
"shell.execute_reply": "2026-07-14T10:18:17.516419Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: using: SI unit system for unit inference. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[?25h"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:18:14] WARNING: The spacing of 0.2 m specified in UniformRefinement will be \n",
" cast to the first lower refinement in the octree series (0.125 m). \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: Units of output `UserVariables`: \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: \n",
" ------------------------------------------------------------------ \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: Variable Name | Unit \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: \n",
" ------------------------------------------------------------------ \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: left_aileron_hinge_torque_partial | Pa*m \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: left_rudder_hinge_torque_partial | Pa*m \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: right_aileron_hinge_torque_partial | Pa*m \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: right_rudder_hinge_torque_partial | Pa*m \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: right_aileron_hinge_torque_partial (Surface integral) | Pa*m**3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: left_aileron_hinge_torque_partial (Surface integral) | Pa*m**3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: right_rudder_hinge_torque_partial (Surface integral) | Pa*m**3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: left_rudder_hinge_torque_partial (Surface integral) | Pa*m**3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: \n",
" ------------------------------------------------------------------ \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" INFO: \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:18:15] INFO: Selecting beta/in-house mesher for possible meshing tasks. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:18:17] INFO: Successfully submitted: \n",
" type = Case \n",
" name = Steady RANS (hinge moments) \n",
" id = case-f2d73218-b5e9-480f-b082-3507ba7974ad \n",
" status = pending \n",
" project id = prj-cd69ce66-3cf6-450c-b31e-d9c62cb0f2a2 \n",
" \n"
]
}
],
"source": [
"with fl.SI_unit_system:\n",
" params = fl.SimulationParams(\n",
" meshing=meshing,\n",
" operating_condition=operating_condition,\n",
" reference_geometry=reference_geometry,\n",
" models=models,\n",
" outputs=outputs,\n",
" )\n",
"\n",
"case = project.run_case(params, name=\"Steady RANS (hinge moments)\", use_beta_mesher=True)"
]
},
{
"cell_type": "markdown",
"id": "3520b972",
"metadata": {},
"source": [
"## 6. Accessing the results\n",
"\n",
"The `case.wait()` command waits for the simulation to be finished before it is possible to access its results. After completing the whole set of results can be accessed using the `results` parameter of case. "
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4493ce63",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:18:17.519011Z",
"iopub.status.busy": "2026-07-14T10:18:17.518898Z",
"iopub.status.idle": "2026-07-14T10:38:39.405127Z",
"shell.execute_reply": "2026-07-14T10:38:39.404526Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[?25l"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[?25h"
]
}
],
"source": [
"case.wait()\n",
"results = case.results"
]
},
{
"cell_type": "markdown",
"id": "86c64861",
"metadata": {},
"source": [
"The cell below shows how to quickly view the convergence of a simulation, but other results are available such as:\n",
"- forces\n",
"- monitors\n",
"- aeroacoustics results\n",
"- user defined dynamics monitoring\n",
"- and others"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "02430383",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:38:39.406668Z",
"iopub.status.busy": "2026-07-14T10:38:39.406536Z",
"iopub.status.idle": "2026-07-14T10:38:41.984385Z",
"shell.execute_reply": "2026-07-14T10:38:41.984004Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:38:41] INFO: Saved to \n",
" /tmp/tmph6i7tr3b/9071ce30-a06d-4afb-a07a-de4274232967.csv \n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"nonlinear_residuals_data = results.nonlinear_residuals.as_dataframe()\n",
"nonlinear_residuals_data.plot(\n",
" \"pseudo_step\",\n",
" [\"0_cont\", \"1_momx\", \"2_momy\", \"3_momz\", \"4_energ\", \"5_nuHat\"],\n",
" logy=True,\n",
" xlim=[10, 2000],\n",
" grid=True,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7c3328e6",
"metadata": {},
"source": [
"To access the hinge torque history that was set with the `SurfaceIntegralOutput`, the `monitors` result type should be accessed."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "10095f31",
"metadata": {
"execution": {
"iopub.execute_input": "2026-07-14T10:38:41.985790Z",
"iopub.status.busy": "2026-07-14T10:38:41.985572Z",
"iopub.status.idle": "2026-07-14T10:38:43.149802Z",
"shell.execute_reply": "2026-07-14T10:38:43.149181Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n"
],
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[12:38:43] INFO: Saved to \n",
" /tmp/tmph6i7tr3b/55d48688-231d-4de1-9805-e2b448308a5d.csv \n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"left_rudder_hinge_torque_history = results.monitors[\"left_rudder_hinge_torque\"].as_dataframe()\n",
"left_rudder_hinge_torque_history.plot(\n",
" \"pseudo_step\", \"left_rudder_hinge_torque_partial_integral\", grid=True\n",
")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.11.15"
}
},
"nbformat": 4,
"nbformat_minor": 5
}