| Citation: | XIAO R Y，YU J，MA Z X. Applicability of convolutional autoencoder in reduced-order model of unsteady compressible flows[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（12）：3445-3455 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0085
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To effectively reduce the design cost and cycle time of using computational fluid dynamics (CFD) methods, the reduced-order model (ROM) has gained wide attention in recent years. For complex compressible flows, using linear methods such as proper orthogonal decomposition (POD) for flow field dimensionality reduction requires a large number of modes to ensure reconstruction accuracy. It has been shown that the mode number can be effectively reduced by using nonlinear dimensionality reduction methods. Convolutional autoencoder (CAE) is a neural network composed of the encoder and decoder, which can realize data dimensionality reduction and reconstruction, regarded as a nonlinear extension of POD method. CAE is used for nonlinear dimensionality reduction, and long short-term memory (LSTM) neural network is used for time evolution. To address flow incompressibility, the combination of Autoencoder and LSTM for flow field reconstruction has been extensively studied. We examine the one-dimensional Sod shock tube, Shu-Osher problem, two-dimensional Riemann problem and Kelvin-Helmholtz instability problem to test the validity of the ROM for unsteady compressible flows. The ROMs of Sod shock tube and Riemann problem are constructed based on POD by different modes for comparison. The results show that CAE-LSTM method can obtain high reconstruction and prediction accuracy on the premise of using less latents for unsteady compressible flows.
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