Note

Go to the end to download the full example code.

Structural Analysis of a Lathe Cutter#

Basic walk through PyMAPDL capabilities.

Objective#

The objective of this example is to highlight some regularly used PyMAPDL features via a lathe cutter finite element model. Lathe cutters have multiple avenues of wear and failure, and the analyses supporting their design would most often be transient thermal-structural. However, for simplicity, this simulation example uses a non-uniform load.

**Figure 1: Lathe cutter geometry and load description.**#

Contents#

Variables and launch Define necessary variables and launch MAPDL.
Geometry, mesh, and MAPDL parameters Import geometry and inspect for MAPDL parameters. Define linear elastic material model with Python variables. Mesh and apply symmetry boundary conditions.
Coordinate system and load Create a local coordinate system for the applied load and verify with a plot.
Pressure load Define the pressure load as a sine function of the length of the application area using numpy arrays. Import the pressure array into MAPDL as a table array. Verify the applied load and solve.
Plotting Show result plotting, plotting with selection, and working with the plot legend.
Postprocessing: List a result two ways: use PyMAPDL and the Pythonic version of APDL. Demonstrate extended methods and writing a list to a file.
Advanced plotting Use of pyvista.UnstructuredGrid for additional postprocessing.

Step 1: Variables and launch#

Define variables and launch MAPDL.

import os

import numpy as np

from ansys.mapdl.core import launch_mapdl
from ansys.mapdl.core.examples.downloads import download_example_data

# cwd = current working directory
path = os.getcwd()
PI = np.pi
EXX = 1.0e7
NU = 0.27

Often used MAPDL command line options are exposed as Pythonic parameter names in ansys.mapdl.core.launcher.launch_mapdl(). For example, -dir has become run_location. You could use run_location to specify the MAPDL run location. For example:

mapdl = launch_mapdl(run_location=path)

Otherwise, the MAPDL working directory is stored in mapdl.directory. In this directory, MAPDL will create some of the images we will show later.

Options without a Pythonic version can be accessed by the additional_switches parameter. Here -smp is used only to keep the number of solver files to a minimum.

mapdl = launch_mapdl(additional_switches="-smp")

Step 2: Geometry, mesh, and MAPDL parameters#

Import geometry and inspect for MAPDL parameters.
Define material and mesh, and then create boundary conditions.

# First, reset the MAPDL database.
mapdl.clear()

Import the geometry file and list any MAPDL parameters.

lathe_cutter_geo = download_example_data("LatheCutter.anf", "geometry")
mapdl.input(lathe_cutter_geo)
mapdl.finish()
print(mapdl.parameters)

MAPDL Parameters
----------------
PRESS_LENGTH                     : 0.055
UNIT_SYSTEM                      : "bin"

Use pressure area per length in the load definition.

pressure_length = mapdl.parameters["PRESS_LENGTH"]

print(mapdl.parameters)

MAPDL Parameters
----------------
PRESS_LENGTH                     : 0.055
UNIT_SYSTEM                      : "bin"

Change the units and title.

mapdl.units("Bin")
mapdl.title("Lathe Cutter")

TITLE=
 Lathe Cutter

Set material properties.

mapdl.prep7()
mapdl.mp("EX", 1, EXX)
mapdl.mp("NUXY", 1, NU)

MATERIAL          1     NUXY =  0.2700000

The MAPDL element type SOLID285 is used for demonstration purposes. Consider using an appropriate element type or mesh density for your actual application.

mapdl.et(1, 285)
mapdl.smrtsize(4)
mapdl.aesize(14, 0.0025)
mapdl.vmesh(1)

mapdl.da(11, "symm")
mapdl.da(16, "symm")
mapdl.da(9, "symm")
mapdl.da(10, "symm")

CONSTRAINT AT AREA    10
      LOAD LABEL = SYMM

Step 3: Coordinate system and load#

Create a local Coordinate System (CS) for the applied pressure as a function of local X.

Local CS ID is 11

mapdl.cskp(11, 0, 2, 1, 13)
mapdl.csys(1)
mapdl.view(1, -1, 1, 1)
mapdl.psymb("CS", 1)
mapdl.vplot(
    color_areas=True,
    show_lines=True,
    cpos=[-1, 1, 1],
    smooth_shading=True,
)

VTK plots do not show MAPDL plot symbols. However, to use MAPDL plotting capabilities, you can set the keyword option vtk to False.

mapdl.lplot(vtk=False)

Step 4: Pressure load#

Create a pressure load, load it into MAPDL as a table array, verify the load, and solve.

# pressure_length = 0.055 inch

pts = 10
pts_1 = pts - 1

length_x = np.arange(0, pts, 1)
length_x = length_x * pressure_length / pts_1

press = 10000 * (np.sin(PI * length_x / pressure_length))

length_x and press are vectors. To combine them into the correct form needed to define the MAPDL table array, you can use numpy.stack.

press = np.stack((length_x, press), axis=-1)
mapdl.load_table("MY_PRESS", press, "X", csysid=11)

mapdl.asel("S", "Area", "", 14)
mapdl.nsla("S", 1)
mapdl.sf("All", "Press", "%MY_PRESS%")
mapdl.allsel()

SELECT ALL ENTITIES OF TYPE= ALL  AND BELOW

You can open the MAPDL GUI to check the model.

mapdl.open_gui()

Set up the solution.

mapdl.finish()
mapdl.slashsolu()
mapdl.nlgeom("On")
mapdl.psf("PRES", "NORM", 3, 0, 1)
mapdl.view(1, -1, 1, 1)
mapdl.eplot(vtk=False)

Solve the model.

mapdl.solve()
mapdl.finish()
if mapdl.solution.converged:
    print("The solution has converged.")

The solution has converged.

Step 5: Plotting#

mapdl.post1()
mapdl.set("last")
mapdl.allsel()

mapdl.post_processing.plot_nodal_principal_stress("1", smooth_shading=False)

Plotting - Part of Model#

mapdl.csys(1)
mapdl.nsel("S", "LOC", "Z", -0.5, -0.141)
mapdl.esln()
mapdl.nsle()
mapdl.post_processing.plot_nodal_principal_stress(
    "1", edge_color="white", show_edges=True
)

Plotting - Legend Options#

mapdl.allsel()
sbar_kwargs = {
    "color": "black",
    "title": "1st Principal Stress (psi)",
    "vertical": False,
    "n_labels": 6,
}
mapdl.post_processing.plot_nodal_principal_stress(
    "1",
    cpos="xy",
    background="white",
    edge_color="black",
    show_edges=True,
    scalar_bar_args=sbar_kwargs,
    n_colors=9,
)

Let’s try out some scalar bar options from the PyVista documentation. For example, let’s set black text on a beige background.

The scalar bar keywords defined as a Python dictionary are an alternate method to using {key:value}’s. You can use the click-and drag method to reposition the scalar bar. Left-click it and hold down the left mouse button while moving the mouse.

sbar_kwargs = dict(
    title_font_size=20,
    label_font_size=16,
    shadow=True,
    n_labels=9,
    italic=True,
    bold=True,
    fmt="%.1f",
    font_family="arial",
    title="1st Principal Stress (psi)",
    color="black",
)

mapdl.post_processing.plot_nodal_principal_stress(
    "1",
    cpos="xy",
    edge_color="black",
    background="beige",
    show_edges=True,
    scalar_bar_args=sbar_kwargs,
    n_colors=256,
    cmap="jet",
)

# cmap names *_r usually reverses values.  Try cmap='jet_r'

Step 6: Postprocessing#

Results List#

Get all principal nodal stresses.

mapdl.post_processing.nodal_principal_stress("1")

array([1095.57191833, 1162.29978117,  245.4551588 , ..., 1671.88717044,
       2256.97869911, 1949.82365643])

Get the principal nodal stresses of the node subset.

mapdl.nsel("S", vmin=1200, vmax=1210)
mapdl.esln()
mapdl.nsle()

print("The node numbers are:")
print(mapdl.mesh.nnum)  # get node numbers

print("The principal nodal stresses are:")
mapdl.post_processing.nodal_principal_stress("1")

The node numbers are:
[  18   97   98  168  169  248  251  255  265  361  362  364  368  419
  422  423  441  442  452  468  526  661  666  678  806  831  860  903
  918  919  920  928  929  931  958  967  970  993  994  996  997 1007
 1008 1039 1042 1050 1051 1055 1062 1063 1068 1069 1079 1089 1091 1094
 1095 1096 1108 1110 1111 1112 1113 1115 1116 1117 1119 1120 1122 1126
 1129 1155 1163 1164 1166 1177 1180 1181 1184 1185 1188 1190 1194 1195
 1196 1197 1198 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210
 1217 1224 1225 1226 1227 1229 1232 1238]
The principal nodal stresses are:

array([ 293.55119342,  360.98718875,  548.7375669 ,   58.53449938,
        862.13619361, 1710.3437169 , 1887.32668088, 1807.24198212,
       1728.93198985,  684.04093398,  973.90565793, 1835.02798428,
        996.83286654,  -87.477287  ,  556.60319322,  581.93533995,
        481.31445511,  723.59293859, 1153.36222414,  886.50859761,
        403.85334723,  439.8459606 ,  495.32384548,  403.63059375,
        316.10100914,  767.87856302,  851.38706331, 1190.28758079,
       1485.68078124, 1103.20892574, 1543.48347154, 1288.91353874,
        130.22051633,  738.60183259, 1897.42680197, 1481.8955267 ,
        388.66516805, 1297.10202937, 1408.26632515,  838.64717812,
        587.49559568, 1687.51244332, 1445.72818426, 1249.9561887 ,
       1455.3714412 , 1366.61822021, 1391.90483489, 1625.1769307 ,
       1144.05679862, 1327.46680635, 1027.89586754, 1112.87639164,
       1554.05911789, 1029.33397703,  773.36444309, 1367.93897077,
       1122.18758911, 1341.21534661, 2016.14764269, 1378.00544809,
       1544.88823819, 1533.71938467, 1201.68162147,  901.51487977,
        977.59388835, 1480.45133566, 1281.69835448, 1220.49842819,
       1150.13518877,  732.76675627,  358.89782204, 1300.24561257,
       1117.61991579, 1459.75358164, 1159.47137203,  797.90469382,
       1378.37814687, 1375.62754117, 1148.71763083,  553.21194545,
       1446.05670129, 1378.23911884, 1519.39456035, 1406.48652515,
       1587.45177522, 1397.54957538, 1302.10961245, 1753.27639711,
       1533.14150521, 1330.80797544, 1421.77663744, 1344.33692758,
        349.82554218,  679.74835079, 1591.73190186, 1079.95284751,
        650.07045872, 1186.27918715, 1732.23066805, 1247.57440958,
        997.70961778, 1443.05183943, 1291.27365035, 1549.3411336 ,
       1356.28591966, 1843.37098876])

Results as lists, arrays, and DataFrames#

Using mapdl.prnsol() to check

print(mapdl.prnsol("S", "PRIN"))

PRINT S    NODAL SOLUTION PER NODE
   *****MAPDL VERIFICATION RUN ONLY*****
     DO NOT USE RESULTS FOR PRODUCTION

  ***** POST1 NODAL STRESS LISTING *****

  LOAD STEP=     1  SUBSTEP=     1
   TIME=    1.0000      LOAD CASE=   0

    NODE     S1           S2           S3           SINT         SEQV
 293.55      -102.62      -2887.1       3180.7       3002.3
 360.99      -213.63      -2333.8       2694.8       2458.3
 548.74      -168.76      -1934.4       2483.2       2213.4
 58.534      -256.76      -3523.5       3582.0       3435.3
 862.14      -78.567      -2440.1       3302.2       2946.7
 1710.3       297.57      -90.587       1800.9       1641.6
 1887.3       697.04       9.7055       1877.6       1645.4
 1807.2       506.74      -103.39       1910.6       1690.3
 1728.9       698.37      -235.92       1964.8       1702.3
 684.04      -265.84      -1685.9       2370.0       2065.9
 973.91      -62.209      -1468.9       2442.8       2123.6
 1835.0       201.41      -874.31       2709.3       2362.9
 996.83      -22.522      -1429.2       2426.0       2109.9
-87.477      -521.45      -3963.3       3875.8       3678.1
 556.60      -424.40      -3508.7       4065.3       3674.4
 581.94      -28.195      -4049.7       4631.6       4358.7
 481.31      -174.42      -3199.5       3680.9       3400.7
 723.59      -94.017      -3282.2       4005.8       3666.0
 1153.4       119.02      -2228.8       3382.2       3001.8
 886.51       116.64      -1649.6       2536.1       2252.1
 403.85      -183.16      -2258.5       2662.3       2422.8
 439.85      -423.07      -2517.5       2957.4       2634.1
 495.32      -552.58      -2092.2       2587.5       2254.3
 403.63      -217.10      -2285.4       2689.1       2438.7
 316.10      -552.31      -2831.2       3147.3       2815.4
 767.88      -171.80      -2277.7       3045.6       2701.2
 851.39      -183.94      -1419.4       2270.8       1969.1
 1190.3      -110.73      -1056.8       2247.0       1954.1
 1485.7       6.3415      -1856.8       3342.5       2901.1
 1103.2      -271.42      -1858.5       2961.8       2567.2
 1543.5      -117.29      -1895.2       3438.7       2978.6
 1288.9      -63.544      -1765.8       3054.7       2651.3
 130.22      -492.02      -3309.5       3439.7       3174.7
 738.60      -710.70      -2727.8       3466.4       3015.4
 1897.4       121.77      -1652.0       3549.5       3073.9
 1481.9      -126.44      -1618.1       3100.0       2685.3
 388.67      -342.36      -2432.5       2821.2       2535.9
 1297.1      -69.232      -673.94       1971.0       1748.9
 1408.3       109.68      -527.75       1936.0       1708.9
   *****MAPDL VERIFICATION RUN ONLY*****
     DO NOT USE RESULTS FOR PRODUCTION

  ***** POST1 NODAL STRESS LISTING *****

  LOAD STEP=     1  SUBSTEP=     1
   TIME=    1.0000      LOAD CASE=   0

    NODE     S1           S2           S3           SINT         SEQV
 838.65      -104.11      -2011.8       2850.5       2515.3
 587.50      -168.66      -2216.8       2804.3       2513.0
 1687.5       590.80      -195.46       1883.0       1638.1
 1445.7       160.70      -399.18       1844.9       1638.4
 1250.0      -42.298      -829.33       2079.3       1818.3
 1455.4       56.014      -827.70       2283.1       1993.9
 1366.6       30.323      -558.14       1924.8       1708.3
 1391.9       123.37      -710.12       2102.0       1833.4
 1625.2       534.59      -104.88       1730.1       1515.2
 1144.1      -211.78      -1888.8       3032.9       2631.4
 1327.5      -110.33      -1929.0       3256.5       2826.6
 1027.9      -226.19      -1858.9       2886.8       2507.2
 1112.9      -309.26      -2054.9       3167.7       2748.1
 1554.1       7.6816      -952.58       2506.6       2190.5
 1029.3       28.635      -1997.0       3026.3       2670.5
 773.36      -180.67      -2035.9       2809.3       2474.3
 1367.9       137.66      -511.70       1879.6       1653.5
 1122.2      -160.47      -956.86       2079.1       1816.9
 1341.2      -59.158      -872.78       2214.0       1939.7
 2016.1       295.76      -1732.8       3749.0       3250.4
 1378.0       16.329      -1069.9       2447.9       2124.4
 1544.9      -6.0350      -1039.8       2584.7       2253.3
 1533.7       44.281      -838.13       2371.8       2076.4
 1201.7      -136.72      -1286.4       2488.1       2156.8
 901.51      -181.05      -1951.1       2852.7       2494.3
 977.59      -182.22      -1391.3       2368.9       2051.7
 1480.5       318.87      -396.04       1876.5       1640.4
 1281.7      -61.077      -1839.8       3121.5       2712.1
 1220.5      -7.9548      -1925.8       3146.3       2746.4
 1150.1      -84.100      -1847.6       2997.7       2609.6
 732.77      -578.00      -1996.9       2729.7       2364.6
 358.90      -623.35      -2611.7       2970.6       2621.3
 1300.2      -89.537      -1741.4       3041.7       2637.4
 1117.6      -338.37      -1811.8       2929.4       2537.0
 1459.8      -281.91      -1685.6       3145.4       2729.2
 1159.5      -241.30      -1793.1       2952.5       2558.1
 797.90      -187.81      -2210.6       3008.5       2656.5
 1378.4       34.377      -818.26       2196.6       1918.1
 1375.6       49.208      -732.58       2108.2       1846.0
   *****MAPDL VERIFICATION RUN ONLY*****
     DO NOT USE RESULTS FOR PRODUCTION

  ***** POST1 NODAL STRESS LISTING *****

  LOAD STEP=     1  SUBSTEP=     1
   TIME=    1.0000      LOAD CASE=   0

    NODE     S1           S2           S3           SINT         SEQV
 1148.7      -274.84      -1874.1       3022.8       2619.3
 553.21      -487.94      -2230.7       2783.9       2436.3
 1446.1      -27.180      -955.21       2401.3       2097.4
 1378.2      -20.625      -756.85       2135.1       1878.5
 1519.4       123.54      -572.17       2091.6       1844.9
 1406.5       179.12      -404.69       1811.2       1601.2
 1587.5       173.43      -762.20       2349.6       2048.9
 1397.5       23.468      -741.92       2139.5       1877.7
 1302.1      -42.291      -728.07       2030.2       1788.8
 1753.3       536.06      -115.22       1868.5       1642.7
 1533.1       82.256      -882.15       2415.3       2105.8
 1330.8      -71.842      -925.05       2255.9       1972.9
 1421.8       51.803      -678.37       2100.1       1846.7
 1344.3      -186.38      -1741.5       3085.8       2672.4
 349.83      -269.66      -3028.7       3378.6       3115.4
 679.75      -129.38      -2256.8       2936.6       2627.2
 1591.7      -138.70      -1747.2       3339.0       2892.3
 1080.0      -80.069      -1271.9       2351.8       2036.8
 650.07      -374.78      -2257.6       2907.6       2554.4
 1186.3      -174.00      -1904.1       3090.3       2682.7
 1732.2      -148.05      -1663.8       3396.0       2946.7
 1247.6      -118.49      -982.93       2230.5       1947.9
 997.71      -160.41      -1218.8       2216.5       1920.2
 1443.1      -99.023      -987.68       2430.7       2130.3
 1291.3      -152.91      -1103.4       2394.7       2088.5
 1549.3      -227.74      -1794.8       3344.1       2898.0
 1356.3      -98.844      -941.59       2297.9       2013.4
 1843.4      -69.019      -1799.8       3643.2       3156.4

 MINIMUM VALUES
 NODE           0             0             0             0             0
 VALUE   -87.477      -710.70      -4049.7       1730.1       1515.2

 MAXIMUM VALUES
 NODE           0             0             0             0             0
 VALUE    2016.1       698.37       9.7055       4631.6       4358.7

Use this command to obtain the data as a list.

mapdl_s_1_list = mapdl.prnsol("S", "PRIN").to_list()
print(mapdl_s_1_list)

[[18.0, 293.55, -102.62, -2887.1, 3180.7, 3002.3], [97.0, 360.99, -213.63, -2333.8, 2694.8, 2458.3], [98.0, 548.74, -168.76, -1934.4, 2483.2, 2213.4], [168.0, 58.534, -256.76, -3523.5, 3582.0, 3435.3], [169.0, 862.14, -78.567, -2440.1, 3302.2, 2946.7], [248.0, 1710.3, 297.57, -90.587, 1800.9, 1641.6], [251.0, 1887.3, 697.04, 9.7055, 1877.6, 1645.4], [255.0, 1807.2, 506.74, -103.39, 1910.6, 1690.3], [265.0, 1728.9, 698.37, -235.92, 1964.8, 1702.3], [361.0, 684.04, -265.84, -1685.9, 2370.0, 2065.9], [362.0, 973.91, -62.209, -1468.9, 2442.8, 2123.6], [364.0, 1835.0, 201.41, -874.31, 2709.3, 2362.9], [368.0, 996.83, -22.522, -1429.2, 2426.0, 2109.9], [419.0, -87.477, -521.45, -3963.3, 3875.8, 3678.1], [422.0, 556.6, -424.4, -3508.7, 4065.3, 3674.4], [423.0, 581.94, -28.195, -4049.7, 4631.6, 4358.7], [441.0, 481.31, -174.42, -3199.5, 3680.9, 3400.7], [442.0, 723.59, -94.017, -3282.2, 4005.8, 3666.0], [452.0, 1153.4, 119.02, -2228.8, 3382.2, 3001.8], [468.0, 886.51, 116.64, -1649.6, 2536.1, 2252.1], [526.0, 403.85, -183.16, -2258.5, 2662.3, 2422.8], [661.0, 439.85, -423.07, -2517.5, 2957.4, 2634.1], [666.0, 495.32, -552.58, -2092.2, 2587.5, 2254.3], [678.0, 403.63, -217.1, -2285.4, 2689.1, 2438.7], [806.0, 316.1, -552.31, -2831.2, 3147.3, 2815.4], [831.0, 767.88, -171.8, -2277.7, 3045.6, 2701.2], [860.0, 851.39, -183.94, -1419.4, 2270.8, 1969.1], [903.0, 1190.3, -110.73, -1056.8, 2247.0, 1954.1], [918.0, 1485.7, 6.3415, -1856.8, 3342.5, 2901.1], [919.0, 1103.2, -271.42, -1858.5, 2961.8, 2567.2], [920.0, 1543.5, -117.29, -1895.2, 3438.7, 2978.6], [928.0, 1288.9, -63.544, -1765.8, 3054.7, 2651.3], [929.0, 130.22, -492.02, -3309.5, 3439.7, 3174.7], [931.0, 738.6, -710.7, -2727.8, 3466.4, 3015.4], [958.0, 1897.4, 121.77, -1652.0, 3549.5, 3073.9], [967.0, 1481.9, -126.44, -1618.1, 3100.0, 2685.3], [970.0, 388.67, -342.36, -2432.5, 2821.2, 2535.9], [993.0, 1297.1, -69.232, -673.94, 1971.0, 1748.9], [994.0, 1408.3, 109.68, -527.75, 1936.0, 1708.9], [996.0, 838.65, -104.11, -2011.8, 2850.5, 2515.3], [997.0, 587.5, -168.66, -2216.8, 2804.3, 2513.0], [1007.0, 1687.5, 590.8, -195.46, 1883.0, 1638.1], [1008.0, 1445.7, 160.7, -399.18, 1844.9, 1638.4], [1039.0, 1250.0, -42.298, -829.33, 2079.3, 1818.3], [1042.0, 1455.4, 56.014, -827.7, 2283.1, 1993.9], [1050.0, 1366.6, 30.323, -558.14, 1924.8, 1708.3], [1051.0, 1391.9, 123.37, -710.12, 2102.0, 1833.4], [1055.0, 1625.2, 534.59, -104.88, 1730.1, 1515.2], [1062.0, 1144.1, -211.78, -1888.8, 3032.9, 2631.4], [1063.0, 1327.5, -110.33, -1929.0, 3256.5, 2826.6], [1068.0, 1027.9, -226.19, -1858.9, 2886.8, 2507.2], [1069.0, 1112.9, -309.26, -2054.9, 3167.7, 2748.1], [1079.0, 1554.1, 7.6816, -952.58, 2506.6, 2190.5], [1089.0, 1029.3, 28.635, -1997.0, 3026.3, 2670.5], [1091.0, 773.36, -180.67, -2035.9, 2809.3, 2474.3], [1094.0, 1367.9, 137.66, -511.7, 1879.6, 1653.5], [1095.0, 1122.2, -160.47, -956.86, 2079.1, 1816.9], [1096.0, 1341.2, -59.158, -872.78, 2214.0, 1939.7], [1108.0, 2016.1, 295.76, -1732.8, 3749.0, 3250.4], [1110.0, 1378.0, 16.329, -1069.9, 2447.9, 2124.4], [1111.0, 1544.9, -6.035, -1039.8, 2584.7, 2253.3], [1112.0, 1533.7, 44.281, -838.13, 2371.8, 2076.4], [1113.0, 1201.7, -136.72, -1286.4, 2488.1, 2156.8], [1115.0, 901.51, -181.05, -1951.1, 2852.7, 2494.3], [1116.0, 977.59, -182.22, -1391.3, 2368.9, 2051.7], [1117.0, 1480.5, 318.87, -396.04, 1876.5, 1640.4], [1119.0, 1281.7, -61.077, -1839.8, 3121.5, 2712.1], [1120.0, 1220.5, -7.9548, -1925.8, 3146.3, 2746.4], [1122.0, 1150.1, -84.1, -1847.6, 2997.7, 2609.6], [1126.0, 732.77, -578.0, -1996.9, 2729.7, 2364.6], [1129.0, 358.9, -623.35, -2611.7, 2970.6, 2621.3], [1155.0, 1300.2, -89.537, -1741.4, 3041.7, 2637.4], [1163.0, 1117.6, -338.37, -1811.8, 2929.4, 2537.0], [1164.0, 1459.8, -281.91, -1685.6, 3145.4, 2729.2], [1166.0, 1159.5, -241.3, -1793.1, 2952.5, 2558.1], [1177.0, 797.9, -187.81, -2210.6, 3008.5, 2656.5], [1180.0, 1378.4, 34.377, -818.26, 2196.6, 1918.1], [1181.0, 1375.6, 49.208, -732.58, 2108.2, 1846.0], [1184.0, 1148.7, -274.84, -1874.1, 3022.8, 2619.3], [1185.0, 553.21, -487.94, -2230.7, 2783.9, 2436.3], [1188.0, 1446.1, -27.18, -955.21, 2401.3, 2097.4], [1190.0, 1378.2, -20.625, -756.85, 2135.1, 1878.5], [1194.0, 1519.4, 123.54, -572.17, 2091.6, 1844.9], [1195.0, 1406.5, 179.12, -404.69, 1811.2, 1601.2], [1196.0, 1587.5, 173.43, -762.2, 2349.6, 2048.9], [1197.0, 1397.5, 23.468, -741.92, 2139.5, 1877.7], [1198.0, 1302.1, -42.291, -728.07, 2030.2, 1788.8], [1200.0, 1753.3, 536.06, -115.22, 1868.5, 1642.7], [1201.0, 1533.1, 82.256, -882.15, 2415.3, 2105.8], [1202.0, 1330.8, -71.842, -925.05, 2255.9, 1972.9], [1203.0, 1421.8, 51.803, -678.37, 2100.1, 1846.7], [1204.0, 1344.3, -186.38, -1741.5, 3085.8, 2672.4], [1205.0, 349.83, -269.66, -3028.7, 3378.6, 3115.4], [1206.0, 679.75, -129.38, -2256.8, 2936.6, 2627.2], [1207.0, 1591.7, -138.7, -1747.2, 3339.0, 2892.3], [1208.0, 1080.0, -80.069, -1271.9, 2351.8, 2036.8], [1209.0, 650.07, -374.78, -2257.6, 2907.6, 2554.4], [1210.0, 1186.3, -174.0, -1904.1, 3090.3, 2682.7], [1217.0, 1732.2, -148.05, -1663.8, 3396.0, 2946.7], [1224.0, 1247.6, -118.49, -982.93, 2230.5, 1947.9], [1225.0, 997.71, -160.41, -1218.8, 2216.5, 1920.2], [1226.0, 1443.1, -99.023, -987.68, 2430.7, 2130.3], [1227.0, 1291.3, -152.91, -1103.4, 2394.7, 2088.5], [1229.0, 1549.3, -227.74, -1794.8, 3344.1, 2898.0], [1232.0, 1356.3, -98.844, -941.59, 2297.9, 2013.4], [1238.0, 1843.4, -69.019, -1799.8, 3643.2, 3156.4]]

Use this command to obtain the data as an array:

mapdl_s_1_array = mapdl.prnsol("S", "PRIN").to_array()
print(mapdl_s_1_array)

[[   18.       293.55    -102.62   -2887.1     3180.7     3002.3   ]
 [   97.       360.99    -213.63   -2333.8     2694.8     2458.3   ]
 [   98.       548.74    -168.76   -1934.4     2483.2     2213.4   ]
 [  168.        58.534   -256.76   -3523.5     3582.      3435.3   ]
 [  169.       862.14     -78.567  -2440.1     3302.2     2946.7   ]
 [  248.      1710.3      297.57     -90.587   1800.9     1641.6   ]
 [  251.      1887.3      697.04       9.7055  1877.6     1645.4   ]
 [  255.      1807.2      506.74    -103.39    1910.6     1690.3   ]
 [  265.      1728.9      698.37    -235.92    1964.8     1702.3   ]
 [  361.       684.04    -265.84   -1685.9     2370.      2065.9   ]
 [  362.       973.91     -62.209  -1468.9     2442.8     2123.6   ]
 [  364.      1835.       201.41    -874.31    2709.3     2362.9   ]
 [  368.       996.83     -22.522  -1429.2     2426.      2109.9   ]
 [  419.       -87.477   -521.45   -3963.3     3875.8     3678.1   ]
 [  422.       556.6     -424.4    -3508.7     4065.3     3674.4   ]
 [  423.       581.94     -28.195  -4049.7     4631.6     4358.7   ]
 [  441.       481.31    -174.42   -3199.5     3680.9     3400.7   ]
 [  442.       723.59     -94.017  -3282.2     4005.8     3666.    ]
 [  452.      1153.4      119.02   -2228.8     3382.2     3001.8   ]
 [  468.       886.51     116.64   -1649.6     2536.1     2252.1   ]
 [  526.       403.85    -183.16   -2258.5     2662.3     2422.8   ]
 [  661.       439.85    -423.07   -2517.5     2957.4     2634.1   ]
 [  666.       495.32    -552.58   -2092.2     2587.5     2254.3   ]
 [  678.       403.63    -217.1    -2285.4     2689.1     2438.7   ]
 [  806.       316.1     -552.31   -2831.2     3147.3     2815.4   ]
 [  831.       767.88    -171.8    -2277.7     3045.6     2701.2   ]
 [  860.       851.39    -183.94   -1419.4     2270.8     1969.1   ]
 [  903.      1190.3     -110.73   -1056.8     2247.      1954.1   ]
 [  918.      1485.7        6.3415 -1856.8     3342.5     2901.1   ]
 [  919.      1103.2     -271.42   -1858.5     2961.8     2567.2   ]
 [  920.      1543.5     -117.29   -1895.2     3438.7     2978.6   ]
 [  928.      1288.9      -63.544  -1765.8     3054.7     2651.3   ]
 [  929.       130.22    -492.02   -3309.5     3439.7     3174.7   ]
 [  931.       738.6     -710.7    -2727.8     3466.4     3015.4   ]
 [  958.      1897.4      121.77   -1652.      3549.5     3073.9   ]
 [  967.      1481.9     -126.44   -1618.1     3100.      2685.3   ]
 [  970.       388.67    -342.36   -2432.5     2821.2     2535.9   ]
 [  993.      1297.1      -69.232   -673.94    1971.      1748.9   ]
 [  994.      1408.3      109.68    -527.75    1936.      1708.9   ]
 [  996.       838.65    -104.11   -2011.8     2850.5     2515.3   ]
 [  997.       587.5     -168.66   -2216.8     2804.3     2513.    ]
 [ 1007.      1687.5      590.8     -195.46    1883.      1638.1   ]
 [ 1008.      1445.7      160.7     -399.18    1844.9     1638.4   ]
 [ 1039.      1250.       -42.298   -829.33    2079.3     1818.3   ]
 [ 1042.      1455.4       56.014   -827.7     2283.1     1993.9   ]
 [ 1050.      1366.6       30.323   -558.14    1924.8     1708.3   ]
 [ 1051.      1391.9      123.37    -710.12    2102.      1833.4   ]
 [ 1055.      1625.2      534.59    -104.88    1730.1     1515.2   ]
 [ 1062.      1144.1     -211.78   -1888.8     3032.9     2631.4   ]
 [ 1063.      1327.5     -110.33   -1929.      3256.5     2826.6   ]
 [ 1068.      1027.9     -226.19   -1858.9     2886.8     2507.2   ]
 [ 1069.      1112.9     -309.26   -2054.9     3167.7     2748.1   ]
 [ 1079.      1554.1        7.6816  -952.58    2506.6     2190.5   ]
 [ 1089.      1029.3       28.635  -1997.      3026.3     2670.5   ]
 [ 1091.       773.36    -180.67   -2035.9     2809.3     2474.3   ]
 [ 1094.      1367.9      137.66    -511.7     1879.6     1653.5   ]
 [ 1095.      1122.2     -160.47    -956.86    2079.1     1816.9   ]
 [ 1096.      1341.2      -59.158   -872.78    2214.      1939.7   ]
 [ 1108.      2016.1      295.76   -1732.8     3749.      3250.4   ]
 [ 1110.      1378.        16.329  -1069.9     2447.9     2124.4   ]
 [ 1111.      1544.9       -6.035  -1039.8     2584.7     2253.3   ]
 [ 1112.      1533.7       44.281   -838.13    2371.8     2076.4   ]
 [ 1113.      1201.7     -136.72   -1286.4     2488.1     2156.8   ]
 [ 1115.       901.51    -181.05   -1951.1     2852.7     2494.3   ]
 [ 1116.       977.59    -182.22   -1391.3     2368.9     2051.7   ]
 [ 1117.      1480.5      318.87    -396.04    1876.5     1640.4   ]
 [ 1119.      1281.7      -61.077  -1839.8     3121.5     2712.1   ]
 [ 1120.      1220.5       -7.9548 -1925.8     3146.3     2746.4   ]
 [ 1122.      1150.1      -84.1    -1847.6     2997.7     2609.6   ]
 [ 1126.       732.77    -578.     -1996.9     2729.7     2364.6   ]
 [ 1129.       358.9     -623.35   -2611.7     2970.6     2621.3   ]
 [ 1155.      1300.2      -89.537  -1741.4     3041.7     2637.4   ]
 [ 1163.      1117.6     -338.37   -1811.8     2929.4     2537.    ]
 [ 1164.      1459.8     -281.91   -1685.6     3145.4     2729.2   ]
 [ 1166.      1159.5     -241.3    -1793.1     2952.5     2558.1   ]
 [ 1177.       797.9     -187.81   -2210.6     3008.5     2656.5   ]
 [ 1180.      1378.4       34.377   -818.26    2196.6     1918.1   ]
 [ 1181.      1375.6       49.208   -732.58    2108.2     1846.    ]
 [ 1184.      1148.7     -274.84   -1874.1     3022.8     2619.3   ]
 [ 1185.       553.21    -487.94   -2230.7     2783.9     2436.3   ]
 [ 1188.      1446.1      -27.18    -955.21    2401.3     2097.4   ]
 [ 1190.      1378.2      -20.625   -756.85    2135.1     1878.5   ]
 [ 1194.      1519.4      123.54    -572.17    2091.6     1844.9   ]
 [ 1195.      1406.5      179.12    -404.69    1811.2     1601.2   ]
 [ 1196.      1587.5      173.43    -762.2     2349.6     2048.9   ]
 [ 1197.      1397.5       23.468   -741.92    2139.5     1877.7   ]
 [ 1198.      1302.1      -42.291   -728.07    2030.2     1788.8   ]
 [ 1200.      1753.3      536.06    -115.22    1868.5     1642.7   ]
 [ 1201.      1533.1       82.256   -882.15    2415.3     2105.8   ]
 [ 1202.      1330.8      -71.842   -925.05    2255.9     1972.9   ]
 [ 1203.      1421.8       51.803   -678.37    2100.1     1846.7   ]
 [ 1204.      1344.3     -186.38   -1741.5     3085.8     2672.4   ]
 [ 1205.       349.83    -269.66   -3028.7     3378.6     3115.4   ]
 [ 1206.       679.75    -129.38   -2256.8     2936.6     2627.2   ]
 [ 1207.      1591.7     -138.7    -1747.2     3339.      2892.3   ]
 [ 1208.      1080.       -80.069  -1271.9     2351.8     2036.8   ]
 [ 1209.       650.07    -374.78   -2257.6     2907.6     2554.4   ]
 [ 1210.      1186.3     -174.     -1904.1     3090.3     2682.7   ]
 [ 1217.      1732.2     -148.05   -1663.8     3396.      2946.7   ]
 [ 1224.      1247.6     -118.49    -982.93    2230.5     1947.9   ]
 [ 1225.       997.71    -160.41   -1218.8     2216.5     1920.2   ]
 [ 1226.      1443.1      -99.023   -987.68    2430.7     2130.3   ]
 [ 1227.      1291.3     -152.91   -1103.4     2394.7     2088.5   ]
 [ 1229.      1549.3     -227.74   -1794.8     3344.1     2898.    ]
 [ 1232.      1356.3      -98.844   -941.59    2297.9     2013.4   ]
 [ 1238.      1843.4      -69.019  -1799.8     3643.2     3156.4   ]]

or as a DataFrame:

mapdl_s_1_df = mapdl.prnsol("S", "PRIN").to_dataframe()
mapdl_s_1_df.head()

	NODE	S1	S2	S3	SINT	SEQV
0	18.0	293.550	-102.620	-2887.1	3180.7	3002.3
1	97.0	360.990	-213.630	-2333.8	2694.8	2458.3
2	98.0	548.740	-168.760	-1934.4	2483.2	2213.4
3	168.0	58.534	-256.760	-3523.5	3582.0	3435.3
4	169.0	862.140	-78.567	-2440.1	3302.2	2946.7

Use this command to obtain the data as a DataFrame, which is a. Pandas data type. Because the Pandas module is imported, you can use its functions. For example, you can write principal stresses to a file.

# mapdl_s_1_df.to_csv(path + '\prin-stresses.csv')
# mapdl_s_1_df.to_json(path + '\prin-stresses.json')
mapdl_s_1_df.to_html(path + "\prin-stresses.html")

Step 7: Advanced plotting#

mapdl.allsel()
principal_1 = mapdl.post_processing.nodal_principal_stress("1")

Load this result into the VTK grid.

grid = mapdl.mesh.grid
grid["p1"] = principal_1

sbar_kwargs = {
    "color": "black",
    "title": "1st Principal Stress (psi)",
    "vertical": False,
    "n_labels": 6,
}

Generate a single horizontal slice along the XY plane.

Note

PyVista’s eye_dome_lighting method is used here to enhance the plots of the slices. For more information, see`Eye Dome Lighting <pyvista_eye_dome_lighting>`_.

single_slice = grid.slice(normal=[0, 0, 1], origin=[0, 0, 0])
single_slice.plot(
    scalars="p1",
    background="white",
    lighting=False,
    eye_dome_lighting=True,
    show_edges=False,
    cmap="jet",
    n_colors=9,
    scalar_bar_args=sbar_kwargs,
)

Generate a plot with three slice planes.

slices = grid.slice_orthogonal()
slices.plot(
    scalars="p1",
    background="white",
    lighting=False,
    eye_dome_lighting=True,
    show_edges=False,
    cmap="jet",
    n_colors=9,
    scalar_bar_args=sbar_kwargs,
)

Generate a grid with multiple slices in the same plane.

slices = grid.slice_along_axis(12, "x")
slices.plot(
    scalars="p1",
    background="white",
    show_edges=False,
    lighting=False,
    eye_dome_lighting=True,
    cmap="jet",
    n_colors=9,
    scalar_bar_args=sbar_kwargs,
)

Finally, exit MAPDL.

mapdl.exit()

Total running time of the script: (0 minutes 54.172 seconds)