Flow time history representation and reconstruction based on machine learning

Abstract

Based on deep learning technology, a new spatiotemporal flow data representation and reconstruction scheme is proposed by using flow time history (FTH) data instead of flow snapshots. First, the high-dimensional nonlinear flow system is reduced to a low-dimensional representation latent code using the FTH autoencoder model. Second, the mapping from physical space to latent code space is built using mathematical and machine-learning schemes. Finally, FTH at unavailable positions in physical space is generated by the FTH generator. The proposed scheme is validated by three case studies: (i) representing and recovering the FTH data of periodic laminar flow around a circular cylinder at Re = 200 and generating high-resolution laminar flow data; (ii) reconstructing complex FTH of flow past cylinder at Re = 3900 which including laminar and turbulent flow region and generating three-dimensional high-resolution turbulent flow data, respectively; (iii) representing and generating multi-variable turbulent flow data simultaneously using the multi-channel model. The results show that the proposed scheme is an effective low-dimensional representation for complex flow time variant features, which is suitable for both laminar and turbulent FTH data to generate spatiotemporal high-resolution FTH data in three-dimensional space.