Accuracy of environmental flows time history reconstruction model based on sparse observation
ZHAN Qing-liang1, LIU Xin1, CHAO Yang1, GE Yao-jun2
1. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China; 2. College of Civil Engineering, Tongji University, Shanghai 200092, China
Abstract：Obtaining more environmental flow data at more monitoring sites based on limited and sparse available flow monitoring points can provide data for the study of atmospheric monitoring, water monitoring and pollutant dispersion issues. In this study, the influence of physical constraints on the accuracy of the machine learning flow field time history representation model was investigated, and the results of different available measurement points and physical constraint influence weights were compared. The environmental flow around a low Reynolds number square column was tested as an example. Results show that when the number of monitoring points was increased to 500, the accuracy did not improve. When the number of monitoring points was reduced to only 50, the error was minimised when the physical constraint weights were set to 10. The results indicate that the problem of less available data can be effectively compensated by choosing appropriate physical constraint influence weights. Providing a new method and basis for data processing and high-resolution flow field reconstruction for environmental flow problems.
战庆亮, 刘鑫, 晁阳, 葛耀君. 基于稀疏环境监测点的流动时程重构模型精度研究[J]. 中国环境科学, 2023, 43(12): 6592-6600.
ZHAN Qing-liang, LIU Xin, CHAO Yang, GE Yao-jun. Accuracy of environmental flows time history reconstruction model based on sparse observation. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(12): 6592-6600.
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