Study On Vortex Characteristics And Key Time-steps Extraction Of Large-scale Flow Field Data

With the continuous improvement of the accuracy requirements of Computational Fluid Dynamics(CFD)applications and the increasing number of grids,the flow field data volume generated by CFD reaches the magnitude of TB or even PB.As the temporal and spatial complexity of flow field data increases,it is difficult to identify the temporal and spatial features,and researchers need to spend more time manually extracting key information to help understand the complex flow mechanism in the flow field.How to automatically extract flow field features and key time steps will become a research hotspot and a huge challenge faced by researchers.In recent years,the rapid development of deep learning has provided new ideas for solving problems in various fields.Deep learning technology can find features and extract information from massive data,greatly improving the efficiency and accuracy of data analysis,and has become the mainstream technology of data mining and feature extraction.This paper relies on deep learning method to extract vortex features and key time-steps from flow field data,and the main work is as follows:1.To investigate the current research progress of vortex feature extraction and key time-steps extraction in flow field at home and abroad,summarize the current main research methods of flow field data feature extraction,analyze the advantages and disadvantages of various research methods,and lay a technical foundation for the method proposed in this paper.2.Research on Vortex Feature Recognition Algorithm Based on Deep Learning.Vortex Feature is an important means to understand the potential physical mechanism of flow field.The local vortex identification method needs to be combined with artificial selection of appropriate threshold value to judge whether it is a vortex,and its robustness is poor.Global vortex recognition method identification method is computationally complicated and time-consuming.The machine learning method is related to the size and shape of the flow field,which has poor universality and scalability.To solve these problems,a vortex recognition method based on convolutional extreme learning machine is proposed in this paper.This method can detect vortexes from the flow field quickly,objectively and robustly.The effectiveness of this method is proved by a large number of experimental results.3.Research on key time-steps extraction algorithms based on deep learning.The self-codec is introduced into the selection of the key time steps of the computational fluid dynamics flow field,and a global selection method based on the self-codec is proposed.Compared with the existing single dynamic programming and clustering methods,this method can more accurately select representative flow field data in the selection of key time-step flow field,and the selected results can accurately represent the change trend of flow field data.A large number of experimental results show that this method is effective in CFD data set.Experiments show that the two algorithm frameworks proposed in this paper can achieve ideal results in their respective flow field data characteristics and key time-steps extraction problems.