create_cascade_mesh.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "lets generate a mesh from a set of points. note that we can rotate the pv.PolyData object using rotate_z"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "import tempfile\n",
    "import pyvista as pv\n",
    "import numpy as np\n",
    "import os\n",
    "from IPython.display import Image\n",
    "import importlib\n",
    "\n",
    "from ntrfc.gmsh.turbo_cascade import generate_turbocascade , MeshConfig\n",
    "from ntrfc.cascade_case.utils.domain_utils import DomainParameters\n",
    "from ntrfc.cascade_case.domain import CascadeDomain2D\n",
    "from ntrfc.filehandling.mesh import load_mesh\n",
    "\n",
    "# we need a display some situations like a cicd run\n",
    "if os.getenv('DISPLAY') is None:\n",
    "    pv.start_xvfb()  # Start X virtual framebuffer (Xvfb)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "profilepoints_file = importlib.resources.files(\"ntrfc\") / \"data/turbine_cascade/profilepoints.txt\"\n",
    "\n",
    "\n",
    "points = np.loadtxt(profilepoints_file)\n",
    "pts = pv.PolyData(points)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio of cleaned sites: 0.8073422026607983\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/many/miniconda3/envs/NTRfC/lib/python3.10/site-packages/pyvista/core/filters/poly_data.py:2848: PyVistaFutureWarning: The default value of the ``capping`` keyword argument will change in a future version to ``True`` to match the behavior of VTK. We recommend passing the keyword explicitly to prevent future surprises.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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AEAAAAAAAAAAAAFARAoQAAAAAAAAAAACAihAgBAAAAAAAAAAAAFSEACEAAAAAAAAAAACgIgQIAQAAAAAAAAAAABUhQAgAAAAAAAAAAACoCAFCAAAAAAAAAAAAQEUIEAIAAAAAAAAAAAAqQoAQAAAAAAAAAAAAUBEChAAAAAAAAAAAAICKECAEAAAAAAAAAAAAVIQAIQAAAAAAAAAAAKAi/x/Lehgwz652mwAAAABJRU5ErkJggg==",
      "text/plain": "<IPython.core.display.Image object>"
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "width": 1600,
       "height": 1600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "domainparams = DomainParameters()\n",
    "domainparams.generate_params_by_pointcloud(pts)\n",
    "domainparams.pitch = .13\n",
    "domainparams.blade_yshift = 0.0\n",
    "domain2d = CascadeDomain2D()\n",
    "\n",
    "\n",
    "domain2d.generate_from_cascade_parameters(domainparams)\n",
    "path = domain2d.plot_domain()\n",
    "Image(path, width=1600, height=1600)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Info    : Meshing 1D...\n",
      "Info    : [  0%] Meshing curve 1 (BSpline)\n",
      "Info    : [ 10%] Meshing curve 2 (BSpline)\n",
      "Info    : [ 10%] Meshing curve 3 (BSpline)\n",
      "Info    : [ 20%] Meshing curve 4 (BSpline)\n",
      "Info    : [ 20%] Meshing curve 5 (BSpline)\n",
      "Info    : [ 30%] Meshing curve 6 (BSpline)\n",
      "Info    : [ 30%] Meshing curve 7 (BSpline)\n",
      "Info    : [ 30%] Meshing curve 8 (BSpline)\n",
      "Info    : [ 40%] Meshing curve 9 (Extruded)\n",
      "Info    : [ 40%] Meshing curve 10 (Extruded)\n",
      "Info    : [ 50%] Meshing curve 11 (Extruded)\n",
      "Info    : [ 50%] Meshing curve 12 (Extruded)\n",
      "Info    : [ 50%] Meshing curve 13 (Extruded)\n",
      "Info    : [ 60%] Meshing curve 14 (Extruded)\n",
      "Info    : [ 60%] Meshing curve 15 (Extruded)\n",
      "Info    : [ 70%] Meshing curve 16 (Extruded)\n",
      "Info    : [ 70%] Meshing curve 17 (Extruded)\n",
      "Info    : [ 80%] Meshing curve 18 (Extruded)\n",
      "Info    : [ 80%] Meshing curve 19 (Extruded)\n",
      "Info    : [ 80%] Meshing curve 20 (Extruded)\n",
      "Info    : [ 90%] Meshing curve 21 (Extruded)\n",
      "Info    : [ 90%] Meshing curve 22 (Extruded)\n",
      "Info    : [100%] Meshing curve 23 (Extruded)\n",
      "Info    : [100%] Meshing curve 24 (Extruded)\n",
      "Info    : Done meshing 1D (Wall 0.0167006s, CPU 0.017442s)\n",
      "Info    : Meshing 2D...\n",
      "Info    : [  0%] Meshing surface 1 (Plane, Frontal-Delaunay)\n",
      "Info    : [ 10%] Meshing surface 2 (Extruded)\n",
      "Info    : [ 20%] Meshing surface 3 (Extruded)\n",
      "Info    : [ 30%] Meshing surface 4 (Extruded)\n",
      "Info    : [ 40%] Meshing surface 5 (Extruded)\n",
      "Info    : [ 50%] Meshing surface 6 (Extruded)\n",
      "Info    : [ 60%] Meshing surface 7 (Extruded)\n",
      "Info    : [ 70%] Meshing surface 8 (Extruded)\n",
      "Info    : [ 80%] Meshing surface 9 (Extruded)\n",
      "Info    : [ 90%] Meshing surface 10 (Extruded)\n",
      "Info    : Done meshing 2D (Wall 3.60878s, CPU 3.6116s)\n",
      "Info    : Meshing 3D...\n",
      "Info    : Meshing volume 1 (Extruded)\n",
      "Info    : Done meshing 3D (Wall 0.164554s, CPU 0.154041s)\n",
      "Info    : Optimizing mesh...\n",
      "Info    : Done optimizing mesh (Wall 0.00123052s, CPU 0.00102s)\n",
      "Info    : 68611 nodes 96102 elements\n",
      "Info    : Writing '/tmp/tmp8o9map3c/test.cgns'...\n",
      "Info    : 0 periodic/interface nodes\n",
      "Info    : Done writing '/tmp/tmp8o9map3c/test.cgns'\n"
     ]
    },
    {
     "data": {
      "text/plain": "0"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meshconfig = MeshConfig()\n",
    "meshconfig.max_lc = 0.01\n",
    "meshconfig.min_lc =0.005\n",
    "meshconfig.bl_thickness = 0.005\n",
    "meshconfig.bl_growratio = 1.2\n",
    "meshconfig.bl_size = 1.7e-5\n",
    "meshconfig.wake_length = domain2d.chordlength*.6\n",
    "meshconfig.wake_width = domain2d.chordlength*.1\n",
    "meshconfig.wake_lc = 0.005\n",
    "meshconfig.fake_yShiftCylinder = 0\n",
    "meshconfig.bladeres = 200\n",
    "meshconfig.progression_le_halfss = 1.05\n",
    "meshconfig.progression_halfss_te = 0.95\n",
    "meshconfig.progression_te_halfps = 1.05\n",
    "meshconfig.progression_halfps_le = 0.95\n",
    "\n",
    "meshpath =  tempfile.mkdtemp() + \"/test.cgns\"\n",
    "\n",
    "generate_turbocascade(domain2d,meshconfig, meshpath, verbose=False)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "lets plot the outcome of the mesh generation algorithm.\n",
    "as you can see, this method is still experimental although it is implemented in the main branch.\n",
    "i made the decision because the method is worhtwhile developing further and maintainence as well as advertising it is easier if it is in the main branch."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "pyvista_ndarray([[[76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  ...,\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76]],\n\n                 [[76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  ...,\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76]],\n\n                 [[76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  ...,\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76]],\n\n                 ...,\n\n                 [[76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  ...,\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76]],\n\n                 [[76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  ...,\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76]],\n\n                 [[76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  ...,\n                  [76, 76, 76],\n                  [76, 76, 76],\n                  [76, 76, 76]]], dtype=uint8)"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "zslice = tempfile.mkdtemp() + \"/zslice.jpg\"\n",
    "xyplane = tempfile.mkdtemp() + \"/xyplane.jpg\"\n",
    "\n",
    "mesh = load_mesh(meshpath)\n",
    "\n",
    "slice = mesh.slice(normal=\"z\", origin=(0, 0, 0.02))\n",
    "edges = slice.extract_all_edges()\n",
    "\n",
    "p = pv.Plotter()\n",
    "p.add_mesh(edges)\n",
    "p.show_grid()\n",
    "p.view_yx(negative=True)\n",
    "p.camera.roll = 0\n",
    "p.camera.zoom(1)\n",
    "p.add_axes()\n",
    "p.screenshot(zslice)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": "<IPython.core.display.Image object>"
     },
     "execution_count": 6,
     "metadata": {
      "image/jpeg": {
       "width": 1600,
       "height": 1600
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(zslice, width=1600, height=1600)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "'/tmp/tmp8o9map3c/test.cgns'"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meshpath\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}