Towards a Framework for Parametric Models and Geometric Sensitivity Computations with SU2 and pyCAPS
Published in AIAA Aviation 2023, 2023
Shape design optimization relies on obtaining objective and constraint functional sensitivities to geometric parameters in order to explore the design space effectively. To facilitate computer-aided design (CAD) applications in industry, this study presents a workflow for effectively handling geometric constraints and computing analytic sensitivities. The workflow is capable of computing parametric sensitivities by combining geometric sensitivities obtained using Engineering Sketch Pad with adjoint surface sensitivities from the SU2 adjoint solver through a dot product within the pyCAPS framework. The effectiveness and limitations of this workflow are evaluated through three test cases, including a preliminary demonstration of a shape optimization problem in wing design. The results indicate the conceptual effectiveness of the proposed workflow, while noting the need for further improvement in the mesh generation routine.
Paper DOI: https://arc.aiaa.org/doi/10.2514/6.2023-3312
Paper PDF: https://yiren6.github.io/files/shen_aviation_2023_3851508.pdf
Recommended citation: Shen, Y.; Patel, H.; Galbraith, M.; Alonso, J. (2023). "Towards a Framework for Parametric Models and Geometric Sensitivity Computations with SU2 and pyCAPS." AIAA Aviation 2023. AIAA 2023-3312.
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