 ## Linear elasticity: disc with hole convergence study   # Convergence Analysis: Disc with a hole

For many typical engineering problems that need to be solved using numerical techniques, convergence plays a decisive role. It is important to obtain an accurate numerical solution, i.e., to know the mesh refinement level beyond which the results of the numerical analysis do not change significantly, anymore.

In this study the convergence in stress and displacement values for the disc with hole problem are analysed and compared to each other. This study is based on the accomanying Jupyter notebook “Linear_Disc_with_hole.ipynb”, which explains the main features of the analytical and numerical solution of the disc with hole problem. For a better access to the solutions used in this document, it is recommended to read through the previously mentioned Jupyter notebook first.

For the evaluation of the stress and displacement convergence, we consider seven steps of mesh refinement, described by an index parameter. The following table lists each refinement index with the corresponding edge length, which is considered representative of the cell size.

\begin{aligned} &\begin{array}{cccc} \text {Refinement Index} & \text {Cell Size [cm]} \\ \hline 8 & 1.429 \\ 16 & 0.667 \\ 24 & 0.435 \\ 40 & 0.256 \\ 60 & 0.169 \\ 80 & 0.127 \\ 240 & 0.042 \\ \end{array} \end{aligned}

Click to toggle input cell
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm

# Some plot settings
plt.style.use("seaborn-v0_8-deep")
plt.rcParams["lines.linewidth"] = 2.0
plt.rcParams["lines.color"] = "black"
plt.rcParams["legend.frameon"] = True
plt.rcParams["font.family"] = "serif"
plt.rcParams["legend.fontsize"] = 14
plt.rcParams["font.size"] = 14
plt.rcParams["axes.spines.right"] = False
plt.rcParams["axes.spines.top"] = False
plt.rcParams["axes.spines.left"] = True
plt.rcParams["axes.spines.bottom"] = True
plt.rcParams["axes.axisbelow"] = True
plt.rcParams["figure.figsize"] = (8, 6)
Click to toggle input cell
import os

# All files produced by this notebook will be put there.
# ATTENTION: We assume that this notebook is executed in the directory where
#            it is stored. Otherwise this notebook might not work!
out_dir = os.environ.get("OGS_TESTRUNNER_OUT_DIR", "out")
os.makedirs(out_dir, exist_ok=True)
Click to toggle input cell
# This list contains all parameters for the
# different meshes we create
STUDY_indices = [8, 16, 24, 40, 60, 80, 240]

# With this parameter the length of one axis of the square plate is defined
STUDY_mesh_size = 20
Click to toggle input cell
def read_last_timestep_mesh(study_idx):
# ATTENTION: The finest resolution (240) is rather expensive to simulate.
#            Therefore it is tracked in git and might be in a different
#            directory.
d = "out" if study_idx == 240 else out_dir

reader.set_active_time_point(-1)  # go to last timestep

return mesh

def slice_along_line(mesh, start_point, end_point):
line = pv.Line(start_point, end_point, resolution=2)
return mesh.slice_along_line(line)

def get_sigma_polar_components(mesh):
sig = mesh.point_data["sigma"]

xs = mesh.points[:, 0]
ys = mesh.points[:, 1]
sigs_polar = vec4_to_mat3x3polar(sig, xs, ys)

sig_rr = sigs_polar[:, 0, 0]
sig_tt = sigs_polar[:, 1, 1]
sig_rt = sigs_polar[:, 0, 1]

return sig_rr, sig_tt, sig_rt

def get_sort_indices_and_distances_by_distance_from_origin_2D(mesh):
xs = mesh.points[:, 0]
ys = mesh.points[:, 1]
dist_from_origin = np.hypot(xs, ys)
indices_sorted = np.argsort(dist_from_origin)
dist_sorted = dist_from_origin[indices_sorted]

return indices_sorted, dist_sorted

def compute_abs_and_rel_stress_error_rr(sigmas_rr_num, rs, theta_degree):
num_points = sigmas_rr_num.shape
f_abs_rr = np.zeros(num_points)
f_rel_rr = np.zeros(num_points)

for pt_idx in range(num_points):
r = rs[pt_idx]

sigma_rr_ana = kirsch_sig_rr(10, r, theta_degree, 2)

sigma_rr_num = sigmas_rr_num[pt_idx] * 1000

f_abs_rr[pt_idx] = sigma_rr_num - sigma_rr_ana

if sigma_rr_ana == 0:
f_rel_rr[pt_idx] = f_abs_rr[pt_idx] / 1e-2
else:
f_rel_rr[pt_idx] = f_abs_rr[pt_idx] / sigma_rr_ana

return f_abs_rr, f_rel_rr

def compute_abs_and_rel_stress_error_rt(sigmas_rt_num, rs, theta_degree):
num_points = sigmas_rt_num.shape
f_abs_rt = np.zeros(num_points)
f_rel_rt = np.zeros(num_points)

for pt_idx in range(num_points):
r = rs[pt_idx]

sigma_rt_ana = kirsch_sig_rt(10, r, theta_degree, 2)
sigma_rt_num = sigmas_rt_num[pt_idx] * 1000

f_abs_rt[pt_idx] = sigma_rt_num - sigma_rt_ana

if sigma_rt_ana == 0:
f_rel_rt[pt_idx] = f_abs_rt[pt_idx] / 1e-2
else:
f_rel_rt[pt_idx] = f_abs_rt[pt_idx] / sigma_rt_ana

return f_abs_rt, f_rel_rt

def compute_abs_and_rel_stress_error_tt(sigmas_tt_num, rs, theta_degree):
num_points = sigmas_tt_num.shape
f_abs_tt = np.zeros(num_points)
f_rel_tt = np.zeros(num_points)

for pt_idx in range(num_points):
r = rs[pt_idx]

sigma_tt_ana = kirsch_sig_tt(10, r, theta_degree, 2)
sigma_tt_num = sigmas_tt_num[pt_idx] * 1000

f_abs_tt[pt_idx] = sigma_tt_num - sigma_tt_ana

if sigma_tt_ana == 0:
f_rel_tt[pt_idx] = f_abs_tt[pt_idx] / 1e-2
else:
f_rel_tt[pt_idx] = f_abs_tt[pt_idx] / sigma_tt_ana

return f_abs_tt, f_rel_tt

def compute_cell_size(idx, mesh):
pt1 = (19.999, 0, 0)
pt2 = (19.999, 20, 0)
line_mesh = slice_along_line(mesh, pt1, pt2)
number = (
line_mesh.points.shape - 1
)  # number of cells along the right edge of the plate
size = STUDY_mesh_size / number  # height of plate divided by number of cells
return size

def resample_mesh_to_240_resolution(idx):
return mesh_fine.sample(mesh_coarse)

## Create Gmsh meshes

To generate the meshes and to enable the individual parameters to be adjusted from within this notebook the script “mesh_quarter_of_rectangle_with_hole.py” is used. For the considered problem the parameter $$a$$ and $$b$$ which define the size of the rectangular quarter of the plate are set to a value of $$20\, \text{cm}$$. The radius of the central hole is controlled by the value of $$r$$, which is set at $$r = 2\, \text{cm}$$.

The arguments Nx, Ny, NR and Nr of the script describe the refinement of the mesh and are determined according to each refinement index.

Parameter $$R$$ describes the area for a further refinement in the vicinity of the hole. This additional refinement is needed for better capturing the stress and strain gradients near the hole. Because the fine resolution of the mesh is only needed in the area around the hole, the size of $$R$$ is half the size of the plate.

Click to toggle input cell
import mesh_quarter_of_rectangle_with_hole
Click to toggle input cell
for idx in STUDY_indices:
"""
a and b seem to be the sizes of the rectangular plate,
the other parameters control the mesh resolution
please check that, and maybe document in mesh_quarter_of_rectangle_with_hole.py
"""
output_file = f"{out_dir}/disc_with_hole_idx_is_{idx}.msh"
mesh_quarter_of_rectangle_with_hole.run(
output_file,
a=STUDY_mesh_size,
b=STUDY_mesh_size,
r=2,
R=0.5 * STUDY_mesh_size,
Nx=idx,
Ny=idx,
NR=idx,
Nr=idx,
P=1,
)
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Info    : Done writing '/var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_24.msh'
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Info    : Done writing '/var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_80.msh'
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Info    : Done writing '/var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_240.msh'


## Transform to VTU meshes suitable for OGS

Click to toggle input cell
for idx in STUDY_indices:
input_file = f"{out_dir}/disc_with_hole_idx_is_{idx}.msh"
! msh2vtu -r --ogs -o {out_dir}/disc_with_hole_idx_is_{idx} {input_file}
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_8

281 points in 3 dimensions; cells: 70 line, 245 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
281 points in 3 dimensions; cells: 245 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
70 points in 3 dimensions; cells: 70 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
15 points in 3 dimensions; cells: 14 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
15 points in 3 dimensions; cells: 14 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
15 points in 3 dimensions; cells: 14 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
15 points in 3 dimensions; cells: 14 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
15 points in 3 dimensions; cells: 14 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
281 points in 3 dimensions; cells: 245 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_16

1201 points in 3 dimensions; cells: 150 line, 1125 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
1201 points in 3 dimensions; cells: 1125 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
150 points in 3 dimensions; cells: 150 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
31 points in 3 dimensions; cells: 30 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
31 points in 3 dimensions; cells: 30 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
31 points in 3 dimensions; cells: 30 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
31 points in 3 dimensions; cells: 30 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
31 points in 3 dimensions; cells: 30 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
1201 points in 3 dimensions; cells: 1125 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_24

2761 points in 3 dimensions; cells: 230 line, 2645 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
2761 points in 3 dimensions; cells: 2645 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
230 points in 3 dimensions; cells: 230 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
47 points in 3 dimensions; cells: 46 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
47 points in 3 dimensions; cells: 46 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
47 points in 3 dimensions; cells: 46 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
47 points in 3 dimensions; cells: 46 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
47 points in 3 dimensions; cells: 46 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
2761 points in 3 dimensions; cells: 2645 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_40

7801 points in 3 dimensions; cells: 390 line, 7605 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
7801 points in 3 dimensions; cells: 7605 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
390 points in 3 dimensions; cells: 390 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
79 points in 3 dimensions; cells: 78 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
79 points in 3 dimensions; cells: 78 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
79 points in 3 dimensions; cells: 78 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
79 points in 3 dimensions; cells: 78 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
79 points in 3 dimensions; cells: 78 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
7801 points in 3 dimensions; cells: 7605 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_60

17701 points in 3 dimensions; cells: 590 line, 17405 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
17701 points in 3 dimensions; cells: 17405 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
590 points in 3 dimensions; cells: 590 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
119 points in 3 dimensions; cells: 118 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
119 points in 3 dimensions; cells: 118 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
119 points in 3 dimensions; cells: 118 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
119 points in 3 dimensions; cells: 118 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
119 points in 3 dimensions; cells: 118 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
17701 points in 3 dimensions; cells: 17405 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_80

31601 points in 3 dimensions; cells: 790 line, 31205 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
31601 points in 3 dimensions; cells: 31205 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
790 points in 3 dimensions; cells: 790 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
159 points in 3 dimensions; cells: 158 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
159 points in 3 dimensions; cells: 158 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
159 points in 3 dimensions; cells: 158 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
159 points in 3 dimensions; cells: 158 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
159 points in 3 dimensions; cells: 158 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
31601 points in 3 dimensions; cells: 31205 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished
Output: /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_240

286801 points in 3 dimensions; cells: 2390 line, 285605 quad; point_data=['gmsh:dim_tags']; cell_data=['gmsh:physical', 'gmsh:geometrical']; cell_sets=['Bottom', 'Right', 'Top', 'Left', 'Hole', 'Plate', 'gmsh:bounding_entities']
##
Detected mesh dimension: 2
##
Domain mesh (written)
286801 points in 3 dimensions; cells: 285605 quad; point_data=['original_node_number']; cell_data=['MaterialIDs']; cell_sets=[]
##
Boundary mesh (written)
2390 points in 3 dimensions; cells: 2390 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Bottom (written)
479 points in 3 dimensions; cells: 478 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Right (written)
479 points in 3 dimensions; cells: 478 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Top (written)
479 points in 3 dimensions; cells: 478 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Left (written)
479 points in 3 dimensions; cells: 478 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Hole (written)
479 points in 3 dimensions; cells: 478 line; point_data=['bulk_node_ids']; cell_data=['bulk_elem_ids']; cell_sets=[]
##
Submesh Plate (written)
286801 points in 3 dimensions; cells: 285605 quad; point_data=['bulk_node_ids']; cell_data=['MaterialIDs']; cell_sets=[]
##
msh2vtu successfully finished


## Visualize the meshes

To get a better sense of cell sizes and the additional refinement around the hole, the meshes of refinement indices 8 and 80 are shown below.

Click to toggle input cell
import pyvista as pv

pv.set_plot_theme("document")
pv.set_jupyter_backend("static")
Click to toggle input cell
domain_8 = pv.read(f"{out_dir}/disc_with_hole_idx_is_8_" + "domain.vtu")

p = pv.Plotter(shape=(1, 2), border=False)
p.subplot(0, 0)
p.view_xy()
p.show_bounds(ticks="outside", xlabel="x / m", ylabel="y / m")
p.camera.zoom(1.3)

p.subplot(0, 1)
p.view_xy()
p.show_bounds(ticks="outside", xlabel="x / m", ylabel="y / m")
p.camera.zoom(1.3)
p.window_size = [1000, 500]

p.show()
/var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/.venv/lib/python3.11/site-packages/pyvista/plotting/renderer.py:1472: PyVistaDeprecationWarning: xlabel is deprecated. Use xtitle instead.
warnings.warn(
/var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/.venv/lib/python3.11/site-packages/pyvista/plotting/renderer.py:1478: PyVistaDeprecationWarning: ylabel is deprecated. Use ytitle instead.
warnings.warn( ## Run OGS

Click to toggle input cell
from ogs6py import ogs
import shutil
Click to toggle input cell
# ATTENTION: We exclude the last study index, because its simulation takes
#            too long to be included in the OGS CI pipelines.
for idx in STUDY_indices[:-1]:
prj_file = f"disc_with_hole_idx_is_{idx}.prj"

shutil.copy2(prj_file, out_dir)

prj_path = os.path.join(out_dir, prj_file)

model = ogs.OGS(INPUT_FILE=prj_path, PROJECT_FILE=prj_path)
model.run_model(logfile=f"{out_dir}/out.txt", args=f"-o {out_dir}")
OGS finished with project file /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_8.prj.
Execution took 0.35428643226623535 s
OGS finished with project file /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_16.prj.
Execution took 0.46759510040283203 s
OGS finished with project file /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_24.prj.
Execution took 0.94236159324646 s
OGS finished with project file /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_40.prj.
Execution took 2.4975359439849854 s
OGS finished with project file /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_60.prj.
Execution took 8.260191679000854 s
OGS finished with project file /var/lib/gitlab-runner/builds/e3EQ9HiK/0/ogs/build/release-all/Tests/Data/Mechanics/Linear/DiscWithHole/Linear_Disc_with_hole_convergence_analysis/disc_with_hole_idx_is_80.prj.
Execution took 17.372860431671143 s


## Error Analysis

### Comparison with analytical solution

In this section we compare the numerical solution for the different refinement levels to Kirsch’s analytical solution. We show plots of the radial, tangential and shear stress distribution along the x-axis. Additionally, the absolute error of each refinement level to the analytical solution is plotted for better interpretation of variations.

The plots show that–for the considered axis–the stress distribution around the hole converges to Kirsch´s solution with decreasing size of the mesh cells. This decrease of the extrapolation errors can also be seen in the plots for the absolute error. Especially in the region close to the hole the error values for finer meshes shrink more and more.

Another effect that is particularly visible in the plots of $$\sigma_{rr}$$ and $$\sigma_{\theta\theta}$$ along the x-axis is an increasing convergence of the solutions for each refinement step to the solution for the finest mesh at the outer boundaries. The results of the numerical solution therefore do not converge against the Kirsch’s solution, but against the numerical solution itself for very fine meshes. This discrepancy is caused by the finite size of the plate in the numerical solution: Kirsch’s analytical solution assumes an infinite plate.

For this reason, an error analysis against the analytical solution does not make sense. Instead, the individual numerical solutions are compared with a “pseudo” analytical solution. For this purpose, a numerical result with extremely fine mesh resolution (refinement index = 240) is generated.

Click to toggle input cell
def kirsch_sig_rr(sig, r, theta, a):
return (
0.5
* sig
* (
(1 - a**2 / r**2)
+ (1 + 3 * np.power(a, 4) / np.power(r, 4) - 4 * a**2 / r**2)
* np.cos(2 * np.pi * theta / 180)
)
* np.heaviside(r + 1e-7 - a, 1)
)

def kirsch_sig_tt(sig, r, theta, a):
return (
0.5
* sig
* (
(1 + a**2 / r**2)
- (1 + 3 * np.power(a, 4) / np.power(r, 4))
* np.cos(2 * np.pi * theta / 180)
)
* np.heaviside(r + 1e-7 - a, 1)
)

def kirsch_sig_rt(sig, r, theta, a):
return (
-0.5
* sig
* (
(1 - 3 * np.power(a, 4) / np.power(r, 4) + 2 * a**2 / r**2)
* np.sin(2 * np.pi * theta / 180)
)
* np.heaviside(r + 1e-7 - a, 1)
)
Click to toggle input cell
def vec4_to_mat3x3cart(vec4):
m = np.zeros((3, 3))
m[0, 0] = vec4
m[1, 1] = vec4
m[2, 2] = vec4
m[0, 1] = vec4
m[1, 0] = vec4

return np.matrix(m)

def vec4_to_mat3x3cart_multi(vec4):
assert vec4.shape == 4

n_pts = vec4.shape

m = np.zeros((n_pts, 3, 3))
m[:, 0, 0] = vec4[:, 0]
m[:, 1, 1] = vec4[:, 1]
m[:, 2, 2] = vec4[:, 2]
m[:, 0, 1] = vec4[:, 3]
m[:, 1, 0] = vec4[:, 3]

return m

def vec4_to_mat3x3polar_single(vec4, xs, ys):
m_cart = vec4_to_mat3x3cart(vec4)

theta = np.arctan2(ys, xs)

rot = np.matrix(np.eye(3))
rot[0, 0] = np.cos(theta)
rot[0, 1] = -np.sin(theta)
rot[1, 0] = np.sin(theta)
rot[1, 1] = np.cos(theta)

return rot.T * m_cart * rot

def vec4_to_mat3x3polar_multi(vecs4, xs, ys):
"""Convert 4-vectors (Kelvin vector in 2D) to 3x3 matrices in polar coordinates at multiple points at once.

Parameters
----------
vecs4:
NumPy array of 4-vectors, dimensions: (N x 4)
xs:
NumPy array of x coordinates, length: N
ys:
NumPy array of y coordinates, length: N

Returns
-------
A Numpy array of the symmetric matrices corresponding to the 4-vectors, dimensions: (N x 3 x 3)
"""

n_pts = vecs4.shape
assert n_pts == xs.shape
assert n_pts == ys.shape
assert vecs4.shape == 4

m_carts = vec4_to_mat3x3cart_multi(vecs4)  # vecs4 converted to symmetric matrices

thetas = np.arctan2(ys, xs)

rots = np.zeros((n_pts, 3, 3))  # rotation matrices at each point
rots[:, 0, 0] = np.cos(thetas)
rots[:, 0, 1] = -np.sin(thetas)
rots[:, 1, 0] = np.sin(thetas)
rots[:, 1, 1] = np.cos(thetas)
rots[:, 2, 2] = 1

# rot.T * m_cart * rot for each point
m_polars = np.einsum("...ji,...jk,...kl", rots, m_carts, rots)

assert m_polars.shape == n_pts
assert m_polars.shape == 3
assert m_polars.shape == 3

return m_polars

def vec4_to_mat3x3polar(vec4, xs, ys):
if len(vec4.shape) == 1:
# only a single 4-vector will be converted
return vec4_to_mat3x3polar_single(vec4, xs, ys)
else:
return vec4_to_mat3x3polar_multi(vec4, xs, ys)
Click to toggle input cell
# Here we'll collect all simulation results
# accessible via their idx value
# We'll be able to reuse/read them many times
STUDY_num_result_meshes_by_index = {}

for idx in STUDY_indices:
STUDY_num_result_meshes_by_index[idx] = mesh

read_simulation_result_meshes()
Click to toggle input cell
STUDY_num_result_xaxis_meshes_by_index = {}

def compute_xaxis_meshes():
for idx in STUDY_indices:
mesh = STUDY_num_result_meshes_by_index[idx]
pt1 = (0, 1e-6, 0)
pt2 = (10, 1e-6, 0)
line_mesh = slice_along_line(mesh, pt1, pt2)

STUDY_num_result_xaxis_meshes_by_index[idx] = line_mesh

compute_xaxis_meshes()
Click to toggle input cell
STUDY_num_result_yaxis_meshes_by_index = {}

def compute_yaxis_meshes():
for idx in STUDY_indices:
mesh = STUDY_num_result_meshes_by_index[idx]
pt1 = (1e-6, 0, 0)
pt2 = (1e-6, 10, 0)
line_mesh = slice_along_line(mesh, pt1, pt2)

STUDY_num_result_yaxis_meshes_by_index[idx] = line_mesh

compute_yaxis_meshes()
Click to toggle input cell
STUDY_num_result_diagonal_meshes_by_index = {}

def compute_diagonal_meshes():
for idx in STUDY_indices:
mesh = STUDY_num_result_meshes_by_index[idx]
pt1 = (1e-6, 1e-6, 0)
pt2 = (28.28, 28.28, 0)
line_mesh = slice_along_line(mesh, pt1, pt2)

STUDY_num_result_diagonal_meshes_by_index[idx] = line_mesh

compute_diagonal_meshes()

### Stress distribution along the x-axis

Click to toggle input cell
def plot_stress_distribution_along_xaxis():
### Step 1: Compute data ##########################################

# These variables will hold the error data for all STUDY_indices
f_abs_rr = {}
f_abs_tt = {}
f_abs_rt = {}

# Plot setup
fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(22, 10))
for i in range(2):
for j in range(3):
ax[i][j].grid(True)
ax[i][j].set(xlim=(0, STUDY_mesh_size))
ax[i][j].set_xlabel("$r$ / cm")
ax[i].set_ylabel("$\Delta\\sigma$ / kPa")
ax[i].set_ylabel("$\Delta\\sigma$ / $\sigma_{\mathrm{analytical}}$")

for iteration, idx in enumerate(STUDY_indices):
# we use the line mesh we extracted before
line_mesh = STUDY_num_result_xaxis_meshes_by_index[idx]

sig_rr, sig_tt, sig_rt = get_sigma_polar_components(line_mesh)

(
indices_sorted,
dist_sorted,
) = get_sort_indices_and_distances_by_distance_from_origin_2D(line_mesh)

# sort sigma by distance from origin
sig_rr_sorted = sig_rr[indices_sorted]
sig_tt_sorted = sig_tt[indices_sorted]
sig_rt_sorted = sig_rt[indices_sorted]

# compute errors
f_abs_rr, f_rel_rr = compute_abs_and_rel_stress_error_rr(
sig_rr_sorted, dist_sorted, -90
)
f_abs_tt, f_rel_tt = compute_abs_and_rel_stress_error_tt(
sig_tt_sorted, dist_sorted, -90
)
f_abs_rt, f_rel_rt = compute_abs_and_rel_stress_error_rt(
sig_rt_sorted, dist_sorted, -90
)

### Step 2: Plot data ##############################################

ax.set_ylabel("$\\sigma_{rr}$ / kPa")
ax.set_ylabel("$\\sigma_{\\theta\\theta}$ / kPa")
ax.set_ylabel("$\\sigma_{r\\theta}$ / kPa")

# analytical results
if iteration == 0:
r = np.linspace(2, STUDY_mesh_size, 1000)
ax.plot(
r,
kirsch_sig_rr(10, r, -90, 2),
color="deepskyblue",
linestyle=":",
label="analytical",
)
ax.plot(
r,
kirsch_sig_tt(10, r, -90, 2),
color="yellowgreen",
linestyle=":",
label="analytical",
)
ax.plot(
r,
kirsch_sig_rt(10, r, -90, 2),
color="orangered",
linestyle=":",
label="analytical",
)

# numerical results
cell_size = compute_cell_size(idx, STUDY_num_result_meshes_by_index[idx])

if idx == 8:
ax.plot(
dist_sorted,
sig_rr_sorted * 1000,
color="lightskyblue",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_tt_sorted * 1000,
color="limegreen",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_rt_sorted * 1000,
color="lightcoral",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(dist_sorted, f_abs_rr, color="lightskyblue")
ax.plot(dist_sorted, f_abs_tt, color="limegreen")
ax.plot(dist_sorted, f_abs_rt, color="lightcoral")

if idx == 16:
ax.plot(
dist_sorted,
sig_rr_sorted * 1000,
color="cornflowerblue",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_tt_sorted * 1000,
color="forestgreen",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_rt_sorted * 1000,
color="firebrick",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(dist_sorted, f_abs_rr, color="cornflowerblue")
ax.plot(dist_sorted, f_abs_tt, color="forestgreen")
ax.plot(dist_sorted, f_abs_rt, color="firebrick")

if idx == 24:
ax.plot(
dist_sorted,
sig_rr_sorted * 1000,
color="royalblue",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_tt_sorted * 1000,
color="darkgreen",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_rt_sorted * 1000,
color="darkred",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(dist_sorted, f_abs_rr, color="royalblue")
ax.plot(dist_sorted, f_abs_tt, color="darkgreen")
ax.plot(dist_sorted, f_abs_rt, color="darkred")

if idx == 240:
ax.plot(
dist_sorted,
sig_rr_sorted * 1000,
color="black",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_tt_sorted * 1000,
color="black",
label=f"h = {cell_size:.3f} cm",
)
ax.plot(
dist_sorted,
sig_rt_sorted * 1000,
color="black",
label=f"h = {cell_size:.3f} cm",
)

# final plot settings
for i in range(3):
ax[i].legend()

ax.set_title("Tangential stress distribution")
ax.set_title("Shear stress distribution")

fig.tight_layout()

plot_stress_distribution_along_xaxis() ## Comparison with “pseudo” analytical solution

As described before the numerical solutions for each refinement step are compared to the solution for the finest refinement index of 240 in the following section. Although the approximation is already clear from the previous plots of the stress curves along the x axis, mathematical norms are used hereafter for a better representation and interpretation of the relationship. In particular, these norms reveal how fast the solution converges to the pseudo analytical one.

### Norm Plots

To quantify the total error of a numerical calculation, various error norms can be used:

\begin{align} \ell_{2}=|w|=\sqrt{\sum_{i=0}^N (w_{i})^2} \ RMS =\sqrt{\frac{\sum_{i=0}^N (w_{i})^2}{N}} \ L_{2}=||w||=\sqrt{\int_{\Omega} (w(x))^2 dx} \end{align}

The $$\ell_{2}$$ norm or Euclidean norm is the square root of the sum of the squared absolute errors at each point of the mesh.

The root mean square ($$RMS$$) is calculated similarly. Here, however, the influence of the number of points is taken into account by dividing by the square root of the number of points.

The $$L_{2}$$ norm as integral norm represents a generalization of the $$\ell_{2}$$ norm for continuous functions. While the Euclidean norm only considers the values on individual mesh nodes, the integral norm considers the solution on the entire mesh. Therefore an advantage of the $$L_{2}$$ norm is, that big elements are considered with a higher impact than small ones, which produces more homogeneous results.

The following plots represent the development of the Euclidean and Integral norm and $$RMS$$ for the refinement of the mesh. How fast the considered element converge is expressed by the slope of the lines in the plot. First the detailed discussed error norms for the stresses are visualised and in addition to them the norms for the associated displacements to draw conclusions about the quality of convergence.

The main conclusion that can be drawn is that the solution for the displacements converge significantly faster than those for the stresses. As a practical consequence, it might be possible to get a sufficiently accurate displacement solution already on a relatively coarse mesh, whereas for an accurate stress solution a much finer mesh might be necessary.

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from vtkmodules.vtkFiltersParallel import vtkIntegrateAttributes
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def integrate_mesh_attributes(mesh):
integrator = vtkIntegrateAttributes()
integrator.SetInputData(mesh)
integrator.Update()
return pv.wrap(
integrator.GetOutputDataObject(0)
)  # that is an entire mesh with one point and one cell
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def compute_ell_2_norm_sigma(idx, sigmas_test, sigmas_reference):
sig_rr, sig_tt, sig_rt = sigmas_test
sig_rr_240, sig_tt_240, sig_rt_240 = sigmas_reference

list_rr = (sig_rr_240 - sig_rr) ** 2
list_tt = (sig_tt_240 - sig_tt) ** 2
list_rt = (sig_rt_240 - sig_rt) ** 2

l2_rr = np.sqrt(sum(list_rr))
l2_tt = np.sqrt(sum(list_tt))
l2_rt = np.sqrt(sum(list_rt))

return l2_rr, l2_tt, l2_rt
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def compute_ell_2_norm_displacement(idx, mesh_resampled_to_240_resolution, mesh_fine):
dis = mesh_resampled_to_240_resolution.point_data["displacement"]
dis_x = dis[:, 0]
dis_y = dis[:, 1]

dis_240 = mesh_fine.point_data["displacement"]
dis_x_240 = dis_240[:, 0]
dis_y_240 = dis_240[:, 1]

list_x = (dis_x_240 - dis_x) ** 2
list_y = (dis_y_240 - dis_y) ** 2

l2_x = np.sqrt(sum(list_x))
l2_y = np.sqrt(sum(list_y))

return l2_x, l2_y
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def compute_root_mean_square_sigma(idx, sigmas_test, sigmas_reference):
sig_rr, sig_tt, sig_rt = sigmas_test
sig_rr_240, sig_tt_240, sig_rt_240 = sigmas_reference

l2_rr = np.linalg.norm(sig_rr_240 - sig_rr)
l2_tt = np.linalg.norm(sig_tt_240 - sig_tt)
l2_rt = np.linalg.norm(sig_rt_240 - sig_rt)

points = sig_rr.shape
return l2_rr / np.sqrt(points), l2_tt / np.sqrt(points), l2_rt / np.sqrt(points)
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def compute_root_mean_square_displacement(
idx, mesh_resampled_to_240_resolution, mesh_fine
):
points = mesh_resampled_to_240_resolution.point_data["sigma"].shape

dis = mesh_resampled_to_240_resolution.point_data["displacement"]
dis_x = dis[:, 0]
dis_y = dis[:, 1]

dis_240 = mesh_fine.point_data["displacement"]
dis_x_240 = dis_240[:, 0]
dis_y_240 = dis_240[:, 1]

l2_x = np.linalg.norm(dis_x_240 - dis_x)
l2_y = np.linalg.norm(dis_y_240 - dis_y)

return l2_x / np.sqrt(points), l2_y / np.sqrt(points)
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def compute_Ell_2_norm_sigma(
idx, mesh_resampled_to_240_resolution, sigmas_test, sigmas_reference
):
sig_rr, sig_tt, sig_rt = sigmas_test
sig_rr_240, sig_tt_240, sig_rt_240 = sigmas_reference

list_rr = (sig_rr_240 - sig_rr) ** 2
list_tt = (sig_tt_240 - sig_tt) ** 2
list_rt = (sig_rt_240 - sig_rt) ** 2

# We add the squared differences as new point data to the mesh
mesh_resampled_to_240_resolution.point_data["diff_rr_squared"] = list_rr
mesh_resampled_to_240_resolution.point_data["diff_tt_squared"] = list_tt
mesh_resampled_to_240_resolution.point_data["diff_rt_squared"] = list_rt

# this will integrate all fields at once, so you can add the tt and rt components above and call this only once.
integration_result_mesh = integrate_mesh_attributes(
mesh_resampled_to_240_resolution
)

# new: integral norm, the index  accesses the data of the single point contained in the mesh
L2_rr = np.sqrt(integration_result_mesh.point_data["diff_rr_squared"])
L2_tt = np.sqrt(integration_result_mesh.point_data["diff_tt_squared"])
L2_rt = np.sqrt(integration_result_mesh.point_data["diff_rt_squared"])

return L2_rr, L2_tt, L2_rt
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def compute_Ell_2_norm_displacement(idx, mesh_resampled_to_240_resolution, mesh_fine):
dis = mesh_resampled_to_240_resolution.point_data["displacement"]
dis_x = dis[:, 0]
dis_y = dis[:, 1]

dis_240 = mesh_fine.point_data["displacement"]
dis_x_240 = dis_240[:, 0]
dis_y_240 = dis_240[:, 1]

list_x = (dis_x_240 - dis_x) ** 2
list_y = (dis_y_240 - dis_y) ** 2

# We add the squared differences as new point data to the mesh
mesh_resampled_to_240_resolution.point_data["diff_x_squared"] = list_x
mesh_resampled_to_240_resolution.point_data["diff_y_squared"] = list_y

# this will integrate all fields at once, so you can add the tt and rt components above and call this only once.
integration_result_mesh = integrate_mesh_attributes(
mesh_resampled_to_240_resolution
)

# new: integral norm, the index  accesses the data of the single point contained in the mesh
L2_x = np.sqrt(integration_result_mesh.point_data["diff_x_squared"])
L2_y = np.sqrt(integration_result_mesh.point_data["diff_y_squared"])

return L2_x, L2_y
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# empty dictionaries
size = {}
l2_rr = {}
l2_tt = {}
l2_rt = {}
rms_rr = {}
rms_tt = {}
rms_rt = {}
L2_rr = {}
L2_tt = {}
L2_rt = {}
l2_x = {}
l2_y = {}
rms_x = {}
rms_y = {}
L2_x = {}
L2_y = {}

def compute_error_norms():
mesh_fine = STUDY_num_result_meshes_by_index
sigmas_reference = get_sigma_polar_components(mesh_fine)

for idx in STUDY_indices:
if idx != 240:  # 240 is the "pseudo" analytical solution we compare against.
mesh_coarse = STUDY_num_result_meshes_by_index[idx]
mesh_resampled_to_240_resolution = mesh_fine.sample(mesh_coarse)
sigmas_test = get_sigma_polar_components(mesh_resampled_to_240_resolution)

l2_rr[idx], l2_tt[idx], l2_rt[idx] = compute_ell_2_norm_sigma(
idx, sigmas_test, sigmas_reference
)
rms_rr[idx], rms_tt[idx], rms_rt[idx] = compute_root_mean_square_sigma(
idx, sigmas_test, sigmas_reference
)
L2_rr[idx], L2_tt[idx], L2_rt[idx] = compute_Ell_2_norm_sigma(
idx, mesh_resampled_to_240_resolution, sigmas_test, sigmas_reference
)

l2_x[idx], l2_y[idx] = compute_ell_2_norm_displacement(
idx, mesh_resampled_to_240_resolution, mesh_fine
)
rms_x[idx], rms_y[idx] = compute_root_mean_square_displacement(
idx, mesh_resampled_to_240_resolution, mesh_fine
)
L2_x[idx], L2_y[idx] = compute_Ell_2_norm_displacement(
idx, mesh_resampled_to_240_resolution, mesh_fine
)
size[idx] = compute_cell_size(idx, mesh_coarse)

compute_error_norms()
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def plot_slope_sketch(ax, x0, y0, slopes, xmax=None):
"""Plot sketch for slopes. All slopes cross at (x0, y0)"""
if xmax is None:
xmax = 2 * x0
xs = np.linspace(x0, xmax, 20)

for slope in slopes:
y_ = xs ** slope
ys = y0 / y_ * xs**slope
ax.plot(xs, ys, color="black")
ax.text(xs[-1] * 1.05, ys[-1], slope)
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h = np.linspace(size, size, 1000)
k = np.linspace(1, 2, 20)

fig, ax = plt.subplots(ncols=3, figsize=(20, 5))

ax.plot(
size.values(),
l2_rr.values(),
color="firebrick",
linestyle=":",
label="$\ell_{2, rr}$",
)
ax.plot(
size.values(),
l2_tt.values(),
color="firebrick",
linestyle="--",
label="$\ell_{2, \\theta\\theta}$",
)
ax.plot(
size.values(), l2_rt.values(), color="firebrick", label="$\ell_{2, r\\theta}$"
)
ax.plot(
size.values(),
l2_x.values(),
color="royalblue",
linestyle="--",
label="$\ell_{2, x}$",
)
ax.plot(size.values(), l2_y.values(), color="royalblue", label="$\ell_{2, y}$")

plot_slope_sketch(ax, 1.5e-1, 5e-2, [1, 2, 3], xmax=2.5e-1)

ax.set_title("$\ell_2$ norms")
ax.set_ylabel("$\ell_2$ / kPa or cm")

ax.plot(
size.values(), rms_rr.values(), color="firebrick", linestyle=":", label="$RMS_{rr}$"
)
ax.plot(
size.values(),
rms_tt.values(),
color="firebrick",
linestyle="--",
label="$RMS_{\\theta\\theta}$",
)
ax.plot(size.values(), rms_rt.values(), color="firebrick", label="$RMS_{r\\theta}$")
ax.plot(
size.values(), rms_x.values(), color="royalblue", linestyle="--", label="$RMS_{x}$"
)
ax.plot(size.values(), rms_y.values(), color="royalblue", label="$RMS_{y}$")

plot_slope_sketch(ax, 1.5e-1, 1e-4, [1, 2, 3], xmax=2.5e-1)

ax.set_title("Root-mean-square")
ax.set_ylabel("RMS / kPa or cm")

ax.plot(
size.values(), L2_rr.values(), color="firebrick", linestyle=":", label="$L_{2, rr}$"
)
ax.plot(
size.values(),
L2_tt.values(),
color="firebrick",
linestyle="--",
label="$L_{2, \\theta\\theta}$",
)
ax.plot(size.values(), L2_rt.values(), color="firebrick", label="$L_{2, r\\theta}$")
ax.plot(
size.values(), L2_x.values(), color="royalblue", linestyle="--", label="$L_{2, x}$"
)
ax.plot(size.values(), L2_y.values(), color="royalblue", label="$L_{2, y}$")

plot_slope_sketch(ax, 1.5e-1, 1e-3, [1, 2, 3], xmax=2.5e-1)

ax.set_title("$L_2$ norms (integral norms)")
ax.set_ylabel("$L_2$ /kPa or cm")
for i in range(3):
ax[i].legend()
ax[i].set_xlabel("h / cm")
ax[i].loglog(base=10) Click to toggle input cell

This article was written by Linda Günther, Sophia Einspänner, Robert Habel, Christoph Lehmann and Thomas Nagel. If you are missing something or you find an error please let us know.
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