Accuracy of environmental flows time history reconstruction model based on sparse observation

ZHAN Qing-liang; LIU Xin; CHAO Yang; GE Yao-jun

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China Environmental Science ›› 2023, Vol. 43 ›› Issue (12) : 6592-6600.

Environmental Ecology

Accuracy of environmental flows time history reconstruction model based on sparse observation

ZHAN Qing-liang¹, LIU Xin¹, CHAO Yang¹, GE Yao-jun²

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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.

Key words

environmental flow reconstruction / environmental flow time history / number of monitoring points / physical constrain / sparse observation data

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ZHAN Qing-liang, LIU Xin, CHAO Yang, GE Yao-jun. Accuracy of environmental flows time history reconstruction model based on sparse observation[J]. China Environmental Science. 2023, 43(12): 6592-6600

References

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