Investigating embedded data distribution strategy on reconstruction accuracy of flow field around the crosswind-affected train based on physics-informed neural networks
International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Article publication date: 28 May 2024
Issue publication date: 2 September 2024
Abstract
Purpose
Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.
Design/methodology/approach
PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.
Findings
The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.
Originality/value
In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.
Keywords
Acknowledgements
The work was supported by a grant from the Research Grants Council (RGC) of the Hong Kong Special Administrative Region (SAR), China (Grant No. 15205723; PolyU 152308/22E), the National Natural Science Foundation of China (Grant No. 52202426) and a grant from The Hong Kong Polytechnic University (Grant No. P0045325). The authors also appreciate the funding support by the Innovation and Technology Commission of Hong Kong SAR Government to the Hong Kong Branch of National Engineering Research Center on Rail Transit Electrification and Automation (Grant No. K-BBY1).
Citation
Zeng, G.-Z., Chen, Z.-W., Ni, Y.-Q. and Rui, E.-Z. (2024), "Investigating embedded data distribution strategy on reconstruction accuracy of flow field around the crosswind-affected train based on physics-informed neural networks", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. 34 No. 8, pp. 2963-2985. https://doi.org/10.1108/HFF-11-2023-0709
Publisher
:Emerald Publishing Limited
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