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• W3118415265 abstract "In gradient-based aerodynamic shape optimization, the functional gradient required by the optimizer can be obtained as a product of efficiently-computed adjoint-based functional sensitivities to volume grid nodes and the volume mesh sensitivities to the design parameters. For turbomachinery applications, it is desirable to use the actual blade design variables as degrees of freedom of the optimization process but this can lead to tedious programming tasks. As an alternative, a robust Machine Learning model is created to model the blade geometry generation and the mesh generation process, using one single polynomial layer. The typical forward pass is followed by a back differentiation operation that allows the computation of the volume mesh derivatives to design parameters. The model is tested by comparing the predicted grid against the actual grid, and the predicted sensitivities are verified through algorithmic differentiation. The modeled mesh sensitivities are successfully employed for adjoint-based reverse engineering and design optimization problems on a turbine blade." @default.
• W3118415265 created "2021-01-18" @default.
• W3118415265 creator A5008232129 @default.
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• W3118415265 date "2021-01-04" @default.
• W3118415265 modified "2023-10-18" @default.
• W3118415265 title "Differentiated ML-based Modeling of Structured Grids for Gradient-based Optimization" @default.
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• W3118415265 doi "https://doi.org/10.2514/6.2021-0895" @default.
• W3118415265 hasPublicationYear "2021" @default.
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