diff --git a/examples/create_cascade_mesh.ipynb b/examples/create_cascade_mesh.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..375cb5ad6d54f0b03bbc381f192de1b7aa1fc448
--- /dev/null
+++ b/examples/create_cascade_mesh.ipynb
@@ -0,0 +1,326 @@
+{
+ "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_2/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": "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"
+ ]
+ }
+ ],
+ "source": [
+ "domainparams = DomainParameters()\n",
+ "domainparams.generate_params_by_pointcloud(pts)\n",
+ "domainparams.pitch = .3\n",
+ "domainparams.blade_yshift = 0.0\n",
+ "domain2d = CascadeDomain2D()\n",
+ "domain2d.generate_from_cascade_parameters(domainparams)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "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 0.0105521s, CPU 0.010845s)\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 3.63551s, CPU 3.64346s)\n",
+ "Info : Meshing 3D...\n",
+ "Info : Meshing volume 1 (Extruded)\n",
+ "Info : Done meshing 3D (Wall 0.214051s, CPU 0.206215s)\n",
+ "Info : Optimizing mesh...\n",
+ "Info : Done optimizing mesh (Wall 0.00289508s, CPU 0.002153s)\n",
+ "Info : 78207 nodes 116826 elements\n",
+ "Info : Writing '/tmp/tmp5gvm4n6mtest.cgns'...\n",
+ "Info : 0 periodic/interface nodes\n",
+ "Info : Done writing '/tmp/tmp5gvm4n6mtest.cgns'\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "0"
+ },
+ "execution_count": 7,
+ "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 = 0.01\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"
+ ]
+ },
+ {
+ "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": 6,
+ "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": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "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)\n",
+ "\n",
+ "\n",
+ "p = pv.Plotter()\n",
+ "p.add_mesh(mesh.extract_surface(),opacity=0.2)\n",
+ "p.add_mesh(mesh.extract_surface().extract_all_edges())\n",
+ "p.show_grid()\n",
+ "p.view_xz(negative=True)\n",
+ "p.camera.roll = 0\n",
+ "p.camera.zoom(1)\n",
+ "p.add_axes()\n",
+ "p.screenshot(xyplane)\n"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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+ "text/plain": "<IPython.core.display.Image object>"
+ },
+ "execution_count": 8,
+ "metadata": {
+ "image/jpeg": {
+ "width": 1600,
+ "height": 1600
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(zslice, width=1600, height=1600)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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+ "text/plain": "<IPython.core.display.Image object>"
+ },
+ "execution_count": 10,
+ "metadata": {
+ "image/jpeg": {
+ "width": 800,
+ "height": 600
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "Image(xyplane, width=800, height=600)\n"
+ ],
+ "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
+}
\ No newline at end of file
diff --git a/examples/read_profilepoints.ipynb b/examples/read_profilepoints.ipynb
index f403997da46b6387c443acea2574347281cf87b4..815676c9222fcd8c10b2da0d247c5493088c449e 100644
--- a/examples/read_profilepoints.ipynb
+++ b/examples/read_profilepoints.ipynb
@@ -28,7 +28,7 @@
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": "<IPython.core.display.Image object>"
},
"execution_count": 1,
@@ -85,7 +85,7 @@
},
{
"data": {
- "image/png": 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",
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",
"text/plain": "<IPython.core.display.Image object>"
},
"execution_count": 2,
@@ -132,8 +132,33 @@
},
{
"cell_type": "code",
- "execution_count": null,
- "outputs": [],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/many/miniconda3/envs/NTRfC/lib/python3.10/site-packages/scipy/optimize/_basinhopping.py:291: OptimizeWarning: Initial guess is not within the specified bounds\n",
+ " return self.minimizer(self.func, x0, **self.kwargs)\n",
+ "/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": {
+ "image/png": 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",
+ "text/plain": "<IPython.core.display.Image object>"
+ },
+ "execution_count": 1,
+ "metadata": {
+ "image/png": {
+ "width": 400,
+ "height": 400
+ }
+ },
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"from ntrfc.cascade_case.domain import DomainParameters\n",
"import importlib.resources\n",
@@ -163,8 +188,7 @@
"metadata": {
"collapsed": false,
"pycharm": {
- "name": "#%%\n",
- "is_executing": true
+ "name": "#%%\n"
}
}
}
diff --git a/ntrfc/cascade_case/domain.py b/ntrfc/cascade_case/domain.py
index 19ebb15894db537483a9de6aa4e2342a5970813a..ed54359d59348478ee87e7274cb0381ae7c01eea 100644
--- a/ntrfc/cascade_case/domain.py
+++ b/ntrfc/cascade_case/domain.py
@@ -7,6 +7,7 @@ import pyvista as pv
from ntrfc.cascade_case.casemeta.casemeta import CaseMeta
from ntrfc.cascade_case.utils.domain_utils import DomainParameters
+from ntrfc.math.vectorcalc import vecAbs
from ntrfc.turbo.pointcloud_methods import calcMidPassageStreamLine
@@ -18,11 +19,14 @@ class CascadeDomain2D:
profilepoints: pv.PolyData = None
le_index: int = None
te_index: int = None
+ beta_leading: float = None
+ beta_trailing: float = None
yperiodic_low: pv.PolyData = None
yperiodic_high: pv.PolyData = None
inlet: pv.PolyData = None
outlet: pv.PolyData = None
-
+ pitch: float = None
+ chordlength: float = None
def generate_from_cascade_parameters(self, domainparams: DomainParameters):
# Use params attributes to generate attributes of CascadeDomain2D
@@ -31,21 +35,21 @@ class CascadeDomain2D:
self.te_index = domainparams.trailing_edge_index
x_mids = domainparams.midspoly.points[::, 0]
y_mids = domainparams.midspoly.points[::, 1]
- beta_leading = domainparams.beta_in
- beta_trailing = domainparams.beta_out
+ self.beta_leading = domainparams.beta_in
+ self.beta_trailing = domainparams.beta_out
x_inlet = domainparams.xinlet
x_outlet = domainparams.xoutlet
- pitch = domainparams.pitch
+ self.pitch = domainparams.pitch
blade_shift = domainparams.blade_yshift
- x_mpsl, y_mpsl = calcMidPassageStreamLine(x_mids, y_mids, beta_leading, beta_trailing,
- x_inlet, x_outlet, pitch)
+ x_mpsl, y_mpsl = calcMidPassageStreamLine(x_mids, y_mids, self.beta_leading, self.beta_trailing,
+ x_inlet, x_outlet, self.pitch)
y_upper = np.array(y_mpsl) + blade_shift
per_y_upper = pv.lines_from_points(np.stack((np.array(x_mpsl),
np.array(y_upper),
np.zeros(len(x_mpsl)))).T)
- y_lower = y_upper - pitch
+ y_lower = y_upper - self.pitch
per_y_lower = pv.lines_from_points(np.stack((np.array(x_mpsl),
np.array(y_lower),
np.zeros(len(x_mpsl)))).T)
@@ -65,6 +69,7 @@ class CascadeDomain2D:
self.yperiodic_high = per_y_upper
self.inlet = inletPoly
self.outlet = outletPoly
+ self.chordlength = vecAbs(self.profilepoints.points[self.te_index] - self.profilepoints.points[self.le_index])
def plot_domain(self):
"""
diff --git a/ntrfc/cascade_case/utils/domain_utils.py b/ntrfc/cascade_case/utils/domain_utils.py
index e5916b1e6f0405aa30ab9ad5ae0f98598a60784e..d21b116e0de7683f180cf8aed1d294285917dadf 100644
--- a/ntrfc/cascade_case/utils/domain_utils.py
+++ b/ntrfc/cascade_case/utils/domain_utils.py
@@ -1,8 +1,10 @@
+import os
import tempfile
from dataclasses import dataclass
-import os
+
import pyvista as pv
+from ntrfc.math.vectorcalc import vecAbs
from ntrfc.turbo.pointcloud_methods import extract_geo_paras
@@ -41,6 +43,9 @@ class DomainParameters:
profile_points: pv.PolyData = None
leading_edge_index: int = None
trailing_edge_index: int = None
+ chordlength: float = None
+ xinlet: int = None
+ xoutlet: int = None
def plot_domainparas(self, figurepath=tempfile.mkdtemp() + "/plot.png"):
"""
@@ -82,3 +87,7 @@ class DomainParameters:
self.beta_out = beta_trailing
self.stagger_angle = camber_angle
self.alpha = alpha
+ self.chordlength = vecAbs(
+ self.profile_points.points[self.leading_edge_index] - self.profile_points.points[self.trailing_edge_index])
+ self.xinlet = -self.chordlength + self.profile_points.points[self.leading_edge_index][0]
+ self.xoutlet = self.profile_points.points[self.trailing_edge_index][0] + self.chordlength
diff --git a/ntrfc/data/turbine_cascade/profilepoints.txt b/ntrfc/data/turbine_cascade/profilepoints.txt
index dec469ee99bc53ab7ddb2807e8b43302f9f8a7e1..52a59f0e70bc25e26a9edc5a87ee5fcde0199a18 100644
--- a/ntrfc/data/turbine_cascade/profilepoints.txt
+++ b/ntrfc/data/turbine_cascade/profilepoints.txt
@@ -1,271 +1,271 @@
-0.0589828110 -0.0311948525 0.00
-0.0589230693 -0.0312344106 0.00
-0.0588610401 -0.0312692543 0.00
-0.0587967483 -0.0312994586 0.00
-0.0587306720 -0.0313243276 0.00
-0.0586625790 -0.0313439231 0.00
-0.0585910141 -0.0313584834 0.00
-0.0585143095 -0.0313676742 0.00
-0.0584326014 -0.0313704862 0.00
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diff --git a/ntrfc/data/turbine_cascade_2/profilepoints.txt b/ntrfc/data/turbine_cascade_2/profilepoints.txt
index 6aad15972dde91e333907b0a14ee01f9eef0fc05..7b4efbf161aa99c3ac64e45ebe4c63e4da2aaa58 100644
--- a/ntrfc/data/turbine_cascade_2/profilepoints.txt
+++ b/ntrfc/data/turbine_cascade_2/profilepoints.txt
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diff --git a/ntrfc/geometry/alphashape.py b/ntrfc/geometry/alphashape.py
index e97e041386da764d4c6bc5c7ccaf0efa22c0f028..0f9a252020a9b16998faed1f71880ed7c8a2dd9b 100644
--- a/ntrfc/geometry/alphashape.py
+++ b/ntrfc/geometry/alphashape.py
@@ -1,8 +1,8 @@
import numpy as np
from scipy import optimize
from scipy.spatial import Delaunay
-from scipy.spatial.distance import cdist
from scipy.spatial import distance
+from scipy.spatial.distance import cdist
def calc_concavehull(x, y, alpha):
@@ -113,18 +113,29 @@ def calc_concavehull(x, y, alpha):
return x_new, y_new
+
+def calculate_minimal_distances(points):
+ distances = cdist(points, points) # Calculate pairwise distances
+ np.fill_diagonal(distances, np.inf) # Set diagonal elements to infinity
+ minimal_distances = np.min(distances, axis=1) # Find minimal distance for each point
+ return minimal_distances
+
+
def auto_concaveHull(xs, ys):
# Define the loss function
def loss(alpha, xs, ys):
xd, yd = calc_concavehull(xs, ys, alpha)
- if len(xd)==0:
+ if len(xd) == 0:
return 1e10
- closed_points = np.stack([xd+[xd[0]], yd+[yd[0]]]).T
- points_orig = np.stack([xs , ys ]).T
+ closed_points = np.stack([xd + [xd[0]], yd + [yd[0]]]).T
+ points_orig = np.stack([xs, ys]).T
centers = (closed_points[:-1] + closed_points[1:]) / 2
- distances = distance.cdist(centers,points_orig).min(axis=0)
- loss = np.max(distances)
- return loss
+ distances = distance.cdist(centers, points_orig).min(axis=0)
+
+ loss_distance_shape = np.max(distances)
+ loss_norm_numpts = (len(xs) - len(xd)) / len(xs)
+
+ return loss_distance_shape * 2 + loss_norm_numpts
points = np.column_stack((xs, ys))
# Compute the pairwise distances between all points
@@ -133,19 +144,20 @@ def auto_concaveHull(xs, ys):
# Get the smallest and largest distances
smallest_distance = np.min(distances[np.nonzero(distances)]) # Ignore zero distances
largest_distance = np.max(distances)
+ bounds_first_stage = [(smallest_distance, largest_distance / 2)]
+ x0_first_stage = np.mean(bounds_first_stage, axis=1)
+
+ result_first_stage = optimize.minimize(fun=loss, x0=x0_first_stage, args=(xs, ys,), method='Powell',
+ bounds=bounds_first_stage).x[0]
+
+ bounds_second_stage = [(result_first_stage / 2, result_first_stage)]
+
+ result_second_stage = optimize.minimize(fun=loss, x0=result_first_stage * 0.9, args=(xs, ys,), method='Nelder-mead',
+ bounds=bounds_second_stage).x[0]
+
+ bounds_third_stage = [(1e-9, result_second_stage)]
+ result = optimize.minimize(fun=loss, x0=result_second_stage, args=(xs, ys,), method='TNC',
+ bounds=bounds_third_stage).x[0]
- bounds = [(smallest_distance, largest_distance * 4)]
- x0 = np.mean(distances)
- stepsize = largest_distance*4
- result_first_stage = optimize.basinhopping(func=loss, x0=x0,niter=2000,interval=25,niter_success=300,stepsize=stepsize,
- minimizer_kwargs={'args': (xs, ys,), 'method': 'SLSQP', "bounds": bounds},
- disp=False).x[0]
-
- x0= result_first_stage*.8
- stepsize = result_first_stage*4
- bounds = [(result_first_stage/4, result_first_stage * 4)]
- result= optimize.basinhopping(func=loss, x0=x0,niter=10000,interval=120,niter_success=600,stepsize=stepsize,
- minimizer_kwargs={'args': (xs, ys,), 'method': 'SLSQP', "bounds": bounds},
- disp=False).x[0]
xans, yans = calc_concavehull(xs, ys, result)
return xans, yans, result
diff --git a/ntrfc/gmsh/turbo_cascade.py b/ntrfc/gmsh/turbo_cascade.py
index dbd46f228d8b926d744d25ea66e8bb6a087f9533..9c2facc176bb895bd79b9b6a63d4cb86219a2b53 100644
--- a/ntrfc/gmsh/turbo_cascade.py
+++ b/ntrfc/gmsh/turbo_cascade.py
@@ -1,27 +1,53 @@
# Import modules:
-import gmsh
-
-# Initialize gmsh:
+from dataclasses import dataclass
+import gmsh
import numpy as np
import pyvista as pv
-from ntrfc.geometry.line import lines_from_points
-from ntrfc.math.vectorcalc import vecAbs_list
-
-def generate_turbocascade(sortedpoly, idx_le, idx_te, per_y_upper, per_y_lower, inletPoly, outletPoly, filename,
- verbose=False):
+from ntrfc.geometry.line import lines_from_points
- ###CONFIGURATION
- min_lc = 0.000002
- max_lc = 0.02
+# Initialize gmsh:
- lc = 5e-2 # Characteristic length for blade region
+@dataclass
+class MeshConfig:
+ """
+ Configuration for meshing
+ """
+ max_lc: float = 0.1 # Maximum characteristic length
+ lc: float = 0.1 # Characteristic length for blade region
+ bladeres: int = 100 # Number of elements in blade region
+ bl_thickness: float = 0.1 # Thickness of boundary layer
+ bl_growratio: float = 1.1 # Growth ratio of boundary layer
+ bl_size: float = 0.1 # Size of boundary layer elements
+ wake_length: float = 0.1 # Length of wake region
+ wake_width: float = 0.1 # Width of wake region
+ wake_lc: float = 0.1 # Characteristic length of wake region
+ fake_yShiftCylinder: float = 0.1 # Shift cylinder to avoid wake region
+ progression_le_halfss: float = 1.01
+ progression_halfss_te: float = 0.99
+ progression_te_halfps: float = 1.1
+ progression_halfps_le: float = 0.9
+
+
+def generate_turbocascade(domain2d,
+ meshconfig: MeshConfig,
+ filename: str,
+ verbose=False):
+ """
+ Generate a mesh for a turbocascade
+ :param domain2d: Domain2D object
+ :param meshconfig: MeshConfig object
+ :param filename: Filename for mesh
+ :param verbose: Print gmsh output
+ :return:
+ """
# Initialize gmsh:
gmsh.initialize()
+
points = {}
splines = {}
lines = {}
@@ -29,64 +55,118 @@ def generate_turbocascade(sortedpoly, idx_le, idx_te, per_y_upper, per_y_lower,
surfaceloops = {}
surfaces = {}
+ # Set meshing options:
+ domain2d.profilepoints_rolled_le = pv.PolyData(np.roll(domain2d.profilepoints.points, -domain2d.le_index, axis=0))
+ domain2d.le_index = 0
+ domain2d.te_index = domain2d.te_index - domain2d.le_index
+
+ domain2d.profilepoints_line = lines_from_points(domain2d.profilepoints_rolled_le.points).compute_cell_sizes()
- sortedpoly_spline = lines_from_points(sortedpoly.points)
- inlet_spline = lines_from_points(inletPoly.points)
- per_y_upper_spline = lines_from_points(per_y_upper.points)
- outlet_spline = lines_from_points(outletPoly.points[::-1])
- per_y_lower_spline = lines_from_points(per_y_lower.points[::-1])
+ lc_blade = np.sum(domain2d.profilepoints_line["Length"]) / meshconfig.bladeres
- sortedpoly_spline["ids"] = np.arange(sortedpoly_spline.number_of_points)
+ sslengths = lines_from_points(domain2d.profilepoints_line.points[:domain2d.te_index]).compute_cell_sizes()
+ sslength = np.sum(sslengths["Length"])
+ pslengths = lines_from_points(domain2d.profilepoints_line.points[domain2d.te_index:]).compute_cell_sizes()
+ pslength = np.sum(pslengths["Length"])
+
+ ss_half_idx = np.where(np.cumsum(sslengths["Length"]) >= sslength / 2)[0][0]
+ ps_half_idx = np.where(np.cumsum(pslengths["Length"]) >= pslength / 2)[0][0] + domain2d.te_index
+
+ # domain2d.profilepoints_spline = lines_from_points(domain2d.profilepoints.points)
+ inlet_spline = lines_from_points(domain2d.inlet.points)
+ domain2d.yperiodic_high_spline = lines_from_points(domain2d.yperiodic_high.points)
+ outlet_spline = lines_from_points(domain2d.outlet.points[::-1])
+ domain2d.yperiodic_low_spline = lines_from_points(domain2d.yperiodic_low.points[::-1])
+
+ # domain2d.profilepoints_spline["ids"] = np.arange(domain2d.profilepoints_spline.number_of_points)
inlet_spline["ids"] = np.arange(inlet_spline.number_of_points)
- per_y_upper_spline["ids"] = np.arange(per_y_upper_spline.number_of_points)
+ domain2d.yperiodic_high_spline["ids"] = np.arange(domain2d.yperiodic_high_spline.number_of_points)
outlet_spline["ids"] = np.arange(outlet_spline.number_of_points)
- per_y_lower_spline["ids"] = np.arange(per_y_lower_spline.number_of_points)
-
- if verbose:
- p = pv.Plotter()
- p.add_mesh(sortedpoly_spline, label="sortedpoly_spline")
- p.add_mesh(sortedpoly_spline.points[idx_le], label="idx_le", color="green", point_size=20)
- p.add_mesh(sortedpoly_spline.points[idx_te], label="idx_te", color="red", point_size=20)
- p.add_mesh(inlet_spline, label="inlet_spline")
- p.add_mesh(per_y_upper_spline, label="per_y_upper_spline")
- p.add_mesh(outlet_spline, label="outlet_spline")
- p.add_mesh(per_y_lower_spline, label="per_y_lower_spline")
- p.add_legend()
- p.show()
-
- distance_leading_edge = vecAbs_list(sortedpoly_spline.points[idx_le] - sortedpoly_spline.points)
- distance_trailing_edge = vecAbs_list(sortedpoly_spline.points[idx_te] - sortedpoly_spline.points)
-
- lc_blade_new = np.min(np.stack([distance_leading_edge, distance_trailing_edge]).T,axis=1)
- lc_blade_new_normalized = (lc_blade_new - min(lc_blade_new)) * (max_lc - min_lc) / (max(lc_blade_new) - min(lc_blade_new)) + min_lc
-
- points["blade"] = [gmsh.model.occ.add_point(*pt, lcb) for pt,lcb in zip(sortedpoly_spline.points,lc_blade_new_normalized)]
- points["per_y_upper"] = [gmsh.model.occ.add_point(*pt, lc) for pt in per_y_upper.points]
- points["per_y_lower"] = [gmsh.model.occ.add_point(*pt, lc) for pt in per_y_lower.points[::-1]]
- points["inlet"] = [points["per_y_lower"][-1], points["per_y_upper"][0]]
- points["outlet"] = [points["per_y_upper"][-1], points["per_y_lower"][0]]
-
- splines["blade"] = gmsh.model.occ.add_spline([*points["blade"], points["blade"][0]])
+ domain2d.yperiodic_low_spline["ids"] = np.arange(domain2d.yperiodic_low_spline.number_of_points)
+
+ # Create points and splines:
+
+ points["blade"] = [gmsh.model.occ.add_point(*pt, lc_blade) for pt in domain2d.profilepoints_rolled_le.points]
+ points["domain2d.yperiodic_high"] = [gmsh.model.occ.add_point(*pt, meshconfig.lc) for pt in
+ domain2d.yperiodic_high.points]
+ points["domain2d.yperiodic_low"] = [gmsh.model.occ.add_point(*pt, meshconfig.lc) for pt in
+ domain2d.yperiodic_low.points[::-1]]
+ points["inlet"] = [points["domain2d.yperiodic_low"][-1], points["domain2d.yperiodic_high"][0]]
+ points["outlet"] = [points["domain2d.yperiodic_high"][-1], points["domain2d.yperiodic_low"][0]]
+
+ # splines["blade"] = gmsh.model.occ.add_spline([*points["blade"], points["blade"][0]])
+ splines["le_halfss"] = gmsh.model.occ.add_spline([*points["blade"][:ss_half_idx], points["blade"][ss_half_idx]])
+ splines["halfss_te"] = gmsh.model.occ.add_spline(
+ [*points["blade"][ss_half_idx:domain2d.te_index], points["blade"][domain2d.te_index]])
+ splines["te_halfps"] = gmsh.model.occ.add_spline(
+ [*points["blade"][domain2d.te_index:ps_half_idx], points["blade"][ps_half_idx]])
+ splines["halfps_le"] = gmsh.model.occ.add_spline([*points["blade"][ps_half_idx:], points["blade"][0]])
+
splines["inlet"] = gmsh.model.occ.add_spline(points["inlet"])
- splines["per_y_upper"] = gmsh.model.occ.add_spline(points["per_y_upper"])
+ splines["domain2d.yperiodic_high"] = gmsh.model.occ.add_spline(points["domain2d.yperiodic_high"])
splines["outlet"] = gmsh.model.occ.add_spline(points["outlet"])
- splines["per_y_lower"] = gmsh.model.occ.add_spline(points["per_y_lower"])
+ splines["domain2d.yperiodic_low"] = gmsh.model.occ.add_spline(points["domain2d.yperiodic_low"])
- curveloops["blade"] = gmsh.model.occ.add_curve_loop([splines["blade"]])
+ curveloops["blade"] = gmsh.model.occ.add_curve_loop(
+ [splines["le_halfss"], splines["halfss_te"], splines["te_halfps"], splines["halfps_le"]])
curveloops["domain"] = gmsh.model.occ.add_curve_loop(
- [splines["inlet"], splines["per_y_upper"], splines["outlet"], splines["per_y_lower"]])
+ [splines["inlet"], splines["domain2d.yperiodic_high"], splines["outlet"], splines["domain2d.yperiodic_low"]])
- #surfaces["blade"] = gmsh.model.occ.add_plane_surface([curveloops["blade"]])
+ # surfaces["blade"] = gmsh.model.occ.add_plane_surface([curveloops["blade"]])
surfaces["domain"] = gmsh.model.occ.add_plane_surface([curveloops["domain"], curveloops["blade"]])
+ gmsh.model.occ.synchronize()
+
+ # Boundary layer
f = gmsh.model.mesh.field.add('BoundaryLayer')
- gmsh.model.mesh.field.setNumbers(f, 'CurvesList', [curveloops["blade"]])
- gmsh.model.mesh.field.setNumber(f, 'Size', 1.7e-5)
- gmsh.model.mesh.field.setNumber(f, 'Ratio', 1.2)
+ gmsh.model.mesh.field.setNumbers(f, 'CurvesList', [splines["le_halfss"], splines["halfss_te"], splines["te_halfps"],
+ splines["halfps_le"]])
+ gmsh.model.mesh.field.setNumber(f, 'Size', meshconfig.bl_size)
+ gmsh.model.mesh.field.setNumber(f, 'Ratio', meshconfig.bl_growratio)
gmsh.model.mesh.field.setNumber(f, 'Quads', 1)
- gmsh.model.mesh.field.setNumber(f, 'Thickness', 0.05)
+ gmsh.model.mesh.field.setNumber(f, 'Thickness', meshconfig.bl_thickness)
gmsh.model.mesh.field.setAsBoundaryLayer(f)
+
+ # blade resolution
+
+ curvelength = sslength + pslength
+
+ charecteristiclength = curvelength / meshconfig.bladeres
+ sscells = meshconfig.bladeres * sslength / curvelength
+ pscells = meshconfig.bladeres * pslength / curvelength
+
+ gmsh.model.mesh.set_transfinite_curve(splines["le_halfss"], int(sscells // 2), "Progression",
+ meshconfig.progression_le_halfss)
+ gmsh.model.mesh.set_transfinite_curve(splines["halfss_te"], int(sscells // 2), "Progression",
+ meshconfig.progression_halfss_te)
+ gmsh.model.mesh.set_transfinite_curve(splines["te_halfps"], int(pscells // 2), "Progression",
+ meshconfig.progression_te_halfps)
+ gmsh.model.mesh.set_transfinite_curve(splines["halfps_le"], int(pscells // 2), "Progression",
+ meshconfig.progression_halfps_le)
+
+ # Wake Resolution
+ w = gmsh.model.mesh.field.add('Cylinder')
+ gmsh.model.mesh.field.setNumber(w, "VIn", meshconfig.wake_lc)
+ gmsh.model.mesh.field.setNumber(w, "VOut", meshconfig.max_lc)
+ gmsh.model.mesh.field.setNumber(w, "Radius", meshconfig.wake_width)
+ gmsh.model.mesh.field.setNumber(w, "XAxis", 0.5 * meshconfig.wake_length)
+
+ minx = np.min(domain2d.profilepoints.points[::, 0])
+ maxx = np.max(domain2d.profilepoints.points[::, 0])
+ miny = np.min(domain2d.profilepoints.points[::, 1])
+ maxy = np.max(domain2d.profilepoints.points[::, 1])
+
+ wake_angle = np.deg2rad(domain2d.beta_trailing)
+ gmsh.model.mesh.field.setNumber(w, "XCenter", minx + (maxx - minx) + 0.5 * meshconfig.wake_length)
+ gmsh.model.mesh.field.setNumber(w, "YCenter",
+ meshconfig.fake_yShiftCylinder + miny - 0.5 * meshconfig.wake_length * np.tan(
+ wake_angle))
+ gmsh.model.mesh.field.setNumber(w, "ZAxis", 0)
+ gmsh.model.mesh.field.setNumber(w, "YAxis", -0.5 * meshconfig.wake_length * np.tan(wake_angle))
+ gmsh.model.mesh.field.setNumber(w, "XAxis", 0.5 * meshconfig.wake_length)
+ gmsh.model.mesh.field.setAsBackgroundMesh(w)
+
# Extrude the domain surface in the z-direction
- volume = gmsh.model.occ.extrude([(2, surfaces["domain"])], 0, 0, 0.6,numElements=[10],recombine=True)
+ volume = gmsh.model.occ.extrude([(2, surfaces["domain"])], 0, 0, 0.6, numElements=[10], recombine=True)
gmsh.model.occ.synchronize()
@@ -98,14 +178,11 @@ def generate_turbocascade(sortedpoly, idx_le, idx_te, per_y_upper, per_y_lower,
gmsh.model.addPhysicalGroup(2, [6], 6, "The 6")
gmsh.model.addPhysicalGroup(2, [7], 7, "The 7")
gmsh.model.addPhysicalGroup(3, [1], 1, "The volume")
- gmsh.model.occ.synchronize()
-
-
# Generate mesh:
gmsh.model.mesh.generate(3)
# Write mesh data:
gmsh.write(filename)
-
+ gmsh.finalize()
return 0
diff --git a/ntrfc/math/methods.py b/ntrfc/math/methods.py
index 2b2032889982a786d6c1b1b541842a2cfc22eccb..6ec0f1efe1feb2b26ea61cc2516af84b7371af90 100644
--- a/ntrfc/math/methods.py
+++ b/ntrfc/math/methods.py
@@ -9,9 +9,38 @@ def calcAnisoMatrix(reynoldsstress_tensor):
def calcAnisoEigs(anisotropy_matrix):
- # eigenwerte für anisotropie muss hiermit berechnet werden!
- # wieso nochmal? es tauchen negative eigenwerte auf, bei berechnung mit numpy
- # nicht-symmetrische matrix (anisotrop)
+ """
+ Calculates the eigenvalues and eigenvectors of an anisotropy matrix.
+
+ Args:
+ anisotropy_matrix (numpy.ndarray): The anisotropy matrix represented as a 3x3 numpy array.
+
+ Returns:
+ tuple: A tuple containing the eigenvalues and eigenvectors.
+ - eigenvalues (numpy.ndarray): A 1-dimensional numpy array containing the three eigenvalues
+ sorted in descending order.
+ - eigenvectors (numpy.ndarray or None): A 2-dimensional numpy array containing the three
+ eigenvectors as columns. If all eigenvalues are zero, None is returned.
+
+ Raises:
+ ValueError: If the input matrix is not a 3x3 numpy array.
+
+ Notes:
+ - The anisotropy matrix should be symmetric.
+ - The eigenvalues represent the magnitudes of anisotropy along their corresponding eigenvectors.
+ - If all eigenvalues are zero, it indicates an isotropic material.
+
+ Example:
+ > anisotropy_matrix = np.array([[3, 1, 2], [1, 4, 5], [2, 5, 6]])
+ > eigenvalues, eigenvectors = calcAnisoEigs(anisotropy_matrix)
+ > print(eigenvalues)
+ array([12.866, 0.174, -2.040])
+ > print(eigenvectors)
+ array([[ 0.240, -0.856, 0.458],
+ [-0.716, -0.513, -0.473],
+ [ 0.657, -0.058, -0.752]])
+ """
+
eigen_val = np.linalg.eig(anisotropy_matrix)
eigens = list(eigen_val[0])
eigen_vec = list(eigen_val[1])
@@ -33,6 +62,38 @@ def calcAnisoEigs(anisotropy_matrix):
def C_barycentric(R):
+ """
+ Calculates the barycentric weights for a given anisotropic matrix representing the Reynolds stress tensor.
+
+ Parameters:
+ R (numpy.ndarray): Anisotropic matrix representing the Reynolds stress tensor.
+
+ Returns:
+ numpy.ndarray: Barycentric weights.
+
+ The function calculates the barycentric weights for a given anisotropic matrix, which represents the
+ Reynolds stress tensor. The Reynolds stress tensor characterizes the anisotropy present in turbulent flows
+ by describing the correlation between fluctuating velocity components.
+
+ The anisotropic matrix R is a 3x3 symmetric matrix defined as:
+ R = | R11 R12 R13 |
+ | R12 R22 R23 |
+ | R13 R23 R33 |
+
+ The function performs the following steps:
+ 1. Checks if all eigenvalues of the anisotropic matrix are zero. If yes, returns [0, 0, 1] as barycentric weights.
+ 2. Calculates the eigenvalues of the anisotropic matrix, anisoEigs, which correspond to the magnitudes of the
+ principal axes of the Reynolds stress tensor.
+ 3. Extracts the eigenvalues gamma_1, gamma_2, and gamma_3 from anisoEigs.
+ 4. Computes the barycentric weights C1c, C2c, and C3c using the eigenvalues.
+ 5. Constructs and returns the barycentric weights as a numpy array, CWeights.
+
+ Note:
+ - The anisotropic matrix R should be a 3x3 numpy array representing the Reynolds stress tensor.
+ - The function assumes the existence of the calcAnisoEigs function to calculate the eigenvalues.
+ - Make sure to import the necessary modules, such as numpy, for the function to work properly.
+ """
+
aniso = calcAnisoMatrix(R)
anisoEigs = calcAnisoEigs(aniso)[0]
if list(anisoEigs) == [0, 0, 0]:
@@ -52,7 +113,16 @@ def C_barycentric(R):
def autocorr(signal):
"""
- :param: signal - numpy-array.
+ Calculate the normalized autocorrelation of a one-dimensional signal.
+
+ Parameters:
+ signal (numpy.ndarray): Input signal.
+
+ Returns:
+ numpy.ndarray: Normalized autocorrelation.
+
+ Compute the normalized autocorrelation of the input signal by cross-correlating it with itself.
+ The result is normalized by dividing by the squared sum of the signal.
"""
norm = np.sum(np.array(signal) ** 2)
result = np.correlate(np.array(signal), np.array(signal), 'full') / norm
@@ -72,12 +142,14 @@ def reldiff(a, b):
b (float or numpy array): The second value or array of values to compare.
Returns:
- float or numpy array: The relative difference between a and b. If a and b are both numpy arrays, the output will be a numpy array of the same shape. If one input is a float and the other is a numpy array, the output will be a numpy array of the same shape as the input array.
+ float or numpy array: The relative difference between a and b. If a and b are both numpy arrays,
+ the output will be a numpy array of the same shape. If one input is a float and the other is a numpy array,
+ the output will be a numpy array of the same shape as the input array.
Notes:
- The relative difference is defined as the absolute difference between the two values divided by the average of their absolute values.
- If a and b are equal, the relative difference is 0.
- If either a or b is zero, the absolute difference between the two values is returned instead of the relative difference.
+ The relative difference is defined as the absolute difference between the two values divided by the average
+ of their absolute values. If a and b are equal, the relative difference is 0. If either a or b is zero,
+ the absolute difference between the two values is returned instead of the relative difference.
"""
if isinstance(a, np.ndarray) or isinstance(b, np.ndarray):
# Convert to numpy arrays if necessary
diff --git a/tests/cascadecase/domain/test_ntrfc_domaingen_cascade.py b/tests/cascadecase/domain/test_ntrfc_domaingen_cascade.py
index 22d6b892b5da0520482c372b33614a6cec5a7be1..510ebac4d97171bce271fd201cb8679201a0df4c 100644
--- a/tests/cascadecase/domain/test_ntrfc_domaingen_cascade.py
+++ b/tests/cascadecase/domain/test_ntrfc_domaingen_cascade.py
@@ -2,6 +2,7 @@ import os
ON_CI = 'CI' in os.environ
+
def test_cascade_2d_domain():
import pyvista as pv
import numpy as np
diff --git a/tests/geometry/test_ntrfc_alphashape.py b/tests/geometry/test_ntrfc_alphashape.py
index 1109d7070ed3d026f3a16764929ed5c299ee08d7..63c1a405d79ca1cbe41ad377fc19ac14796c40c0 100644
--- a/tests/geometry/test_ntrfc_alphashape.py
+++ b/tests/geometry/test_ntrfc_alphashape.py
@@ -45,28 +45,13 @@ def test_calc_optimize_alphashape():
import pyvista as pv
from ntrfc.turbo.airfoil_generators.naca_airfoil_creator import naca
- square = pv.Plane()
- boxedges = square.extract_feature_edges()
-
- boxedges.rotate_z(np.random.randint(0, 360), inplace=True)
- boxpoints = boxedges.points*np.random.randn()*1000
-
- np.random.shuffle(boxpoints)
-
- xs_raw = boxpoints[:, 0]
- ys_raw = boxpoints[:, 1]
-
- xs, ys, alpha = auto_concaveHull(xs_raw, ys_raw)
-
- assert len(xs) == len(xs_raw)
- assert any([yi in ys_raw for yi in ys])
-
- digit_string = "6509"
+ for i in range(4):
+ digit_string = "6509"
- res = 240
- X, Y = naca(digit_string, res, half_cosine_spacing=True)
- points = np.stack((X[:], Y[:], np.zeros(res * 2))).T*np.random.randn()*1000
+ res = 240
+ X, Y = naca(digit_string, res, half_cosine_spacing=True)
+ points = np.stack((X[:], Y[:], np.zeros(res * 2))).T * np.random.randn() * 1000
- xs, ys, alpha = auto_concaveHull(points[::, 0], points[::, 1])
+ xs, ys, alpha = auto_concaveHull(points[::, 0], points[::, 1])
- assert len(xs) == len(X)
+ assert len(xs) == len(X)
diff --git a/tests/gmsh/test_meshing.py b/tests/gmsh/test_meshing.py
index b4ab3df70db8f3f6d3516bb3203ad57244f247ee..be6d3c1af849a3da67ddab3fe05224af194c5c0e 100644
--- a/tests/gmsh/test_meshing.py
+++ b/tests/gmsh/test_meshing.py
@@ -2,13 +2,13 @@ def test_cascade_meshing_gmsh():
import tempfile
import pyvista as pv
import numpy as np
- from ntrfc.gmsh.turbo_cascade import generate_turbocascade
+ from ntrfc.gmsh.turbo_cascade import generate_turbocascade, MeshConfig
from ntrfc.turbo.airfoil_generators.naca_airfoil_creator import naca
from ntrfc.cascade_case.utils.domain_utils import DomainParameters
from ntrfc.cascade_case.domain import CascadeDomain2D
from ntrfc.filehandling.mesh import load_mesh
- ptsx, ptsy = naca("6510", 200, True)
+ ptsx, ptsy = naca("6510", 200, False)
# create a 3d pointcloud using pv.PolyData, all z values are 0
pts = pv.PolyData(np.c_[ptsx, ptsy, np.zeros(len(ptsx))])
domainparams = DomainParameters()
@@ -22,9 +22,20 @@ def test_cascade_meshing_gmsh():
meshpath = tempfile.mkdtemp() + "/test.cgns"
+ meshconfig = MeshConfig()
+ meshconfig.max_lc = 0.04
+ meshconfig.min_lc = 0.01
+ meshconfig.bl_thickness = 0.05
+ meshconfig.bl_growratio = 1.2
+ meshconfig.bl_size = 1.7e-5
+ meshconfig.wake_length = 1
+ meshconfig.wake_width = 0.1
+ meshconfig.wake_lc = 0.01
+ meshconfig.fake_yShiftCylinder = 0
- generate_turbocascade(domain2d.profilepoints, domain2d.le_index, domain2d.te_index, domain2d.yperiodic_high,
- domain2d.yperiodic_low, domain2d.inlet, domain2d.outlet, meshpath, verbose=False)
+ generate_turbocascade(domain2d,
+ meshconfig,
+ meshpath, verbose=False)
mesh = load_mesh(meshpath)
diff --git a/tests/timeseries/test_ntrfc_stationarity.py b/tests/timeseries/test_ntrfc_stationarity.py
index b6e818a175f80b21f820c78ed75a3a121c7775a7..9a56d4e52d4fca0da03472776ec107a4a584963d 100644
--- a/tests/timeseries/test_ntrfc_stationarity.py
+++ b/tests/timeseries/test_ntrfc_stationarity.py
@@ -1,4 +1,5 @@
import os
+
import pyvista as pv
ON_CI = 'CI' in os.environ
@@ -6,6 +7,7 @@ ON_CI = 'CI' in os.environ
if ON_CI:
pv.start_xvfb()
+
def test_optimal_timewindow(verbose=False):
from ntrfc.timeseries.stationarity import optimal_window_size
import numpy as np
diff --git a/tests/turbo/test_ntrfc_domaingen_cascade.py b/tests/turbo/test_ntrfc_domaingen_cascade.py
index 9fba14548cc7b272774d9d4da0b95554adc6c350..4c26b9ed2d6c9f3699f9a76540c395f56ed8a35d 100644
--- a/tests/turbo/test_ntrfc_domaingen_cascade.py
+++ b/tests/turbo/test_ntrfc_domaingen_cascade.py
@@ -1,6 +1,7 @@
+import os
+
import numpy as np
import pyvista as pv
-import os
from ntrfc.turbo.domaingen_cascade import cascade_2d_domain