A CAD-enabled MDAO Framework Approach for Gradient-based Aerodynamic Shape Optimization
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We aim at a seamless CAD-integration into a framework-based approach for mul-tidisciplinary design analysis and optimization (MDAO) that allows an automated forward andreverse accumulation of AD-based gradients throughout complex high-fidelity workflows. Thesuggested framework approach relies on the FlowSimulator HPC ecosystem, in which a numberof high-fidelity simulation tools, called plug-ins, share large mesh-based coupling data sets inmemory via the FlowSimulator Data Manager (FSDM) in workflows that are MPI-parallel fromend-to-end. Based on the FlowSimulator infrastructure and a granular plug-in integration, theMDAO framework OpenMDAO [1] was used to drive the CAD-enabled, gradient-based opti-mization in conjunction with the CFD software CODA. A fully-resolved system representationis generated for the MDAO problem at hand based on a systematic registration of plug-in in-put/output dependencies. In this work, the algorithmically differentiated CAD kernel OpenCas-cade Technology (OCCT) was integrated into the FlowSimulator ecosystem to centrally providea gradient-enabled CAD link for all the simulation plug-ins involved in the problem. The indi-vidual FSDM surface mesh objects—that can be (MPI) domain-decomposed—are linked to thecorresponding CAD surfaces using a reliable, meta-data enabled mesh-to-CAD association torobustly deal with fine meshes in the context of high Reynolds-number flows. Selected aerody-namic wing configurations in 2D and 3D are considered in this study to verify and demonstratethe AD-enabled sensitivity analysis with the focus on the framework integration of the CADkernel OCCT in the context of shape optimization.
