Convolutional Neural Networks for the Construction of Surrogate Models of Fluid Flows

Autor(es)

Lui, Hugo F. S. ; Wolf, William R.

• Tipo de documento: Artigo Científico
• Fonte: AIAA SCITECH 2021 FORUM; v. N/A, p. 15-pg., 2021-01-01.

Resumo

In this work, we present a numerical methodology for construction of surrogate models of fluid flows which combine data-driven system identification and convolutional neural networks. The framework is implemented in a context similar to that of the sparse identification of nonlinear dynamics (SINDy) algorithm with some modifications regarding the regression step. The approach presented in this work allows us to obtain an ODE for each flow variable at each mesh point. This should be beneficial for flow control approaches since every flow state can be modified by a control law. The method is tested for two unsteady compressible flows: the flow past a cylinder at low Reynolds number and the turbulent flow past a plunging airfoil under deep dynamical stall. Results demonstrate that the current methodology provides accurate reconstructions of the high fidelity model. (AU)

• Processo FAPESP: 13/08293-7 - CECC - Centro de Engenharia e Ciências Computacionais
• Beneficiário: Munir Salomao Skaf
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 19/26196-5 - Simulações de grandes escalas de turbinas axiais supersônicas
• Beneficiário: Hugo Felippe da Silva Lui
• Modalidade de apoio: Bolsas no Brasil - Doutorado