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DPF Framework 2026 R1

invariant:scalar invariants (field)

Last update: 31.07.2026

Version: 0.0.0

Description

Computes the element-wise invariants of a tensor field.

Inputs

This table lists the input pins for this operator. Input pins define the data that the operator requires to perform its operation. Some inputs are required, while others are optional and provide additional configuration. Each parameter is detailed in the sections that follow the table.

Pin number Name Status Expected type(s)
0 field Required field

field (Pin 0)

  • Required: Yes
  • Expected type(s): field

Outputs

This table lists the output pins for this operator. Output pins provide the results of the operator's computation and can be connected to inputs of other operators or retrieved for further processing. Each output is detailed in the sections that follow the table.

Pin number Name Expected type(s)
0 field_int field
1 field_eqv field
2 field_max_shear field

field_int (Pin 0)

  • Expected type(s): field

stress intensity field

field_eqv (Pin 1)

  • Expected type(s): field

stress equivalent intensity

field_max_shear (Pin 2)

  • Expected type(s): field

max shear stress field

Configurations

This operator supports configuration options that modify its behavior.

mutex

  • Expected type(s): bool
  • Default value: false

If this option is set to true, the shared memory is prevented from being simultaneously accessed by multiple threads.

num_threads

  • Expected type(s): int32
  • Default value: 0

Number of threads to use to run in parallel

run_in_parallel

  • Expected type(s): bool
  • Default value: true

Loops are allowed to run in parallel if the value of this config is set to true.

Scripting

This operator can be accessed through scripting interfaces using these identifiers.

Category: invariant

Plugin: core

Scripting name: invariants

Full name: invariant.invariants

Internal name: invariants_deriv

License: None

Examples

These examples demonstrate how to use this operator in different programming environments. Each example shows how to instantiate the operator, connect the required inputs, and retrieve the output.

C++
#include "dpf_api.h"

ansys::dpf::Operator op("invariants_deriv"); // operator instantiation
op.connect(0, my_field);
ansys::dpf::Field my_field_int = op.getOutput<ansys::dpf::Field>(0);
ansys::dpf::Field my_field_eqv = op.getOutput<ansys::dpf::Field>(1);
ansys::dpf::Field my_field_max_shear = op.getOutput<ansys::dpf::Field>(2);
CPython
import ansys.dpf.core as dpf

op = dpf.operators.invariant.invariants() # operator instantiation
op.inputs.field.connect(my_field)
my_field_int = op.outputs.field_int()
my_field_eqv = op.outputs.field_eqv()
my_field_max_shear = op.outputs.field_max_shear()
IPython
import mech_dpf
import Ans.DataProcessing as dpf

op = dpf.operators.invariant.invariants() # operator instantiation
op.inputs.field.Connect(my_field)
my_field_int = op.outputs.field_int.GetData()
my_field_eqv = op.outputs.field_eqv.GetData()
my_field_max_shear = op.outputs.field_max_shear.GetData()

Changelog

  • Version 0.0.0: Initial release.

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