CAD-integration for Gradient-based Shape Optimization using the Algorithmically Differentiated pythonOCC Library

Abstract

pythonOCC is a library that provides Python bindings for the Open CASCADE Technology (OCCT) C++ geometric modelling kernel. To integrate it into a gradient-based shape optimization, one requires to compute the so-called geometric sensitivities, e.g., derivatives of surface nodes with respect to the design parameters. To obtain this information, pythonOCC and OCCT were algorithmically differentiated. Here, they are modularly integrated in the form of a CAD plugin into a framework for multidisciplinary design analysis and optimization (MDAO) based on the DLR's FlowSimulator HPC ecosystem. The CAD plugin allows a robust and metadata-enabled mesh-to-CAD association between MPI domain-decomposed mesh objects and the underlying CAD patches, as well as the computation of geometric sensitivities. The framework integration of pythonOCC is demonstrated in a context of gradient-based, aerodynamic shape optimization for a RAE2822 configuration in fully turbulent, transonic flow.