How to import VMAP meshes

This notebook demonstrates how to import finite element meshes in VMAP format in pylife, e.g., to be used for a FKM nonlinear assessment.

import pyvista as pv

import pylife.vmap
import pylife.mesh
import pylife.stress.equistress

First we read the file into a VMAPImport.

filename = 'data/kt1.vmap'
vmap_mesh = pylife.vmap.VMAPImport(filename)

Now we can check for geometries and states that we found inside the VMAP file.

list(vmap_mesh.geometries()), list(vmap_mesh.states())

(['KT1-1'], ['Step-1'])

Now we select the forst geometry and the first state and check which field variables are available.

geometry = 'KT1-1'
state = 'Step-1'
vmap_mesh.variables(geometry, state)

['E', 'STRESS_CAUCHY']

For our purpose we are only interested in the stress tensor, so we create our mesh out of the vmap mesh, join coordinates and the STRESS_CAUCHY variable. Finally we get everything as a pd.DataFrame

pylife_mesh = (
    vmap_mesh.make_mesh(geometry, state)
    .join_coordinates()
    .join_variable('STRESS_CAUCHY')
    .to_frame()
)
pylife_mesh

		x	y	z	S11	S22	S33	S12	S13	S23
element_id	node_id
1	68	-0.058024	0.005909	0.001042	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926
	95	-0.058024	0.004247	0.000926	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926
	96	-0.058024	0.003611	0.002350	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926
	58	-0.058024	0.005196	0.003000	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926
	14	-0.060000	0.005909	0.001042	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926
...	...	...	...	...	...	...	...	...	...	...
2684	3315	0.060000	-0.000494	0.002577	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831
	3290	0.058024	-0.001621	0.002328	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831
	3287	0.058024	-0.002545	0.003241	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831
	3288	0.058024	-0.001180	0.003989	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831
	3261	0.058024	-0.000494	0.002577	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831

21472 rows × 9 columns

With the mesh we can perform calculations like for example calculate the Mises stress and add it back to the mesh.

pylife_mesh['mises'] = pylife_mesh.equistress.mises()
pylife_mesh

		x	y	z	S11	S22	S33	S12	S13	S23	mises
element_id	node_id
1	68	-0.058024	0.005909	0.001042	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926	91.658809
	95	-0.058024	0.004247	0.000926	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926	91.658809
	96	-0.058024	0.003611	0.002350	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926	91.658809
	58	-0.058024	0.005196	0.003000	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926	91.658809
	14	-0.060000	0.005909	0.001042	107.280235	15.659771	23.321180	-13.440862	-5.005887	-3.322926	91.658809
...	...	...	...	...	...	...	...	...	...	...	...
2684	3315	0.060000	-0.000494	0.002577	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831	100.006590
	3290	0.058024	-0.001621	0.002328	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831	100.006590
	3287	0.058024	-0.002545	0.003241	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831	100.006590
	3288	0.058024	-0.001180	0.003989	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831	100.006590
	3261	0.058024	-0.000494	0.002577	100.007462	0.000952	0.000791	0.000580	-0.000230	-0.000831	100.006590

21472 rows × 10 columns

Next, we visualize the absolute stress gradient G using the pyvista package.

grid = pv.UnstructuredGrid(*pylife_mesh.mesh.vtk_data())
plotter = pv.Plotter()
plotter.add_mesh(
    grid,
    scalars=pylife_mesh.groupby('element_id')['mises'].mean().to_numpy(),
    show_edges=True,
    cmap='jet'
)
plotter.add_scalar_bar()
plotter.show()