Abstract
Optimizations based on adjoint sensitivity data are presented in this Chapter. RBF are adopted to set up an advanced filtering tool suitable for removing the noise usually observed when shape sensitivities data are computed using CFD so as to enable the adjoint sculpting method where surfaces are updated according to the information provided by the flow solution to get the desired performances (as drag reduction or pressure loss control). Advanced mesh morphing is used to propagate, once properly filtered if needed, the shape data known at surfaces into the full volume mesh required for the calculation. The concept is demonstrated for FEM as well showing how a bracket and a T beam can be updated to control a target displacement. The adjoint preview approach, which consists of the computation of derivatives with respect of shape variations known in advance is then detailed. A collection of fluid shape optimizations, taking into account both internal and external flows, is provided at the end of the Chapter.
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Biancolini, M.E. (2017). Adjoint Sensitivities and RBF Mesh Morphing. In: Fast Radial Basis Functions for Engineering Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-75011-8_8
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DOI: https://doi.org/10.1007/978-3-319-75011-8_8
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