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Numerical investigation of minimum drag profiles in laminar flow using deep learning surrogates | Li-Wei Chen
; Berkay Alp Cakal
; Xiangyu Hu
; Nils Thuerey
; | Date: |
30 Sep 2020 | Abstract: | Efficiently predicting the flowfield and load in aerodynamic shape
optimisation remains a highly challenging and relevant task. Deep learning
methods have been of particular interest for such problems, due to their
success for solving inverse problems in other fields. In the present study,
U-net based deep neural network (DNN) models are trained with high-fidelity
datasets to infer flow fields, and then employed as surrogate models to carry
out the shape optimisation problem, i.e. to find a drag minimal profile with a
fixed cross-section area subjected to a two-dimensional steady laminar flow. A
level-set method as well as Bezier-curve method are used to parameterise the
shape, while trained neural networks in conjunction with automatic
differentiation are utilized to calculate the gradient flow in the optimisation
framework. The optimised shapes and drag force values calculated from the
flowfields predicted by DNN models agree well with reference data obtained via
a Navier-Stokes solver and from the literature, which demonstrates that the DNN
models are capable of predicting not only flowfield but also yield satisfactory
aerodynamic forces. This is particularly promising as the DNNs were not
specifically trained to infer aerodynamic forces. In conjunction with the fast
runtime, the DNN-based optimisation framework shows promise for general
aerodynamic design problems. | Source: | arXiv, 2009.14339 | Services: | Forum | Review | PDF | Favorites |
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