High resolution turbulence flow reconstruction using flow time history deep learning
High-resolution time variant flow field data is the key to the study of turbulence flow.Limited by measurement methods,simulation efficiency and data storage,it is still difficult to obtain high-resolution tur-bulent flow data directly in some circumstances.In this paper,based on the low-dimensional representation model of flow time-history data,a neural network-based feature coding prediction model and high-resolution turbulence flow reconstruction method were proposed.Firstly,a low-dimensional representation model of the turbulence flow was established based on the one-dimensional convolution networks;then,an artificial neu-ral network model was employed to establish the mapping between the measuring point coordinates and fea-ture coding system,and the prediction of feature coding for the unknown measuring points was realized;final-ly,based on feature coding,the decoder in the representation model was utilized to generate turbulence flow time history data at unknown positions.Turbulence flow with Re=2.2×104 around a square cylinder was stud-ied,and the low dimensional representation model and flow generation model were trained and verified.The method proposed in this paper is a high-precision turbulence flow data reconstruction method which can be widely used in one-point-based sensor data processing.It is a new approach for the reconstruction of turbu-lence flow field time-history data.
turbulence flow reconstructionturbulence flow time historydeep learningfeature extractionunsupervised model
万方数据
维普
