Question about generalized_inner_product() and handling of symmetric tensors

Hello!

I am working on a script with pyDPF-core and had a question about how generalized_inner_product() works. I am computing the traction vector from the stress vector and surface normal. However, when I use the generalized_inner_product() operator, I got unexpected results.

Made a minimal example:

from ansys.dpf import core as dpf

# Initialize Fields
field1 = dpf.Field(nentities=2, nature=dpf.natures.symmatrix)
field2 = dpf.Field(nentities=1)

field1.data = [1, 2, 3, 4, 5, 6, 11, 2, 3, 4, 5, 6]
field2.data = [1, 1, 1]

field1.scoping.ids = range(2)
field2.scoping.ids = range(1)

dot_op = dpf.operators.math.generalized_inner_product(field1, field2)

field3 = dot_op.eval()
print(field3.data)

Output:
[[6. 6.5 8.5]
[0. 0. 0. ]]

Expected Output:
[[11. 11. 14.]
[0. 0. 0. ]]

This confirmed that when acting on fields with nature=symmatrix, generalized_inner_product() divides the off-diagonal components by 2 before computing the product.

Wanted to know if this is intended behavior. Is there some property I can set to get around this?

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Mike.Thompson

Have you considered using the element nodal forces of the nodes on the surface.
In that case you would have a Force vector.

Is your original stress tensor from solid elements, contact, or something else?

Jiyansh

Haven't looked into using the element nodal forces, will take a look. But the question still stand since I will be using the dot product for rotating the stress tensor (critical plane analysis).

My original stress/strain tensor is from solid elements and is represented as 6 components. The field nature is dpf.natures.symmatrix.

My main question is really if I'm missing something about generalized_inner_product() and if there's a work-around.