Efficient modeling of liquid splashing via graph neural networks with adaptive filter and aggregator fusion
International Journal of Numerical Methods for Heat & Fluid Flow
ISSN: 0961-5539
Article publication date: 26 June 2024
Abstract
Purpose
The purpose of this study is to advance the computational modeling of liquid splashing dynamics, while balancing simulation accuracy and computational efficiency, a duality often compromised in high-fidelity fluid dynamics simulations.
Design/methodology/approach
This study introduces the fluid efficient graph neural network simulator (FEGNS), an innovative framework that integrates an adaptive filtering layer and aggregator fusion strategy within a graph neural network architecture. FEGNS is designed to directly learn from extensive liquid splash data sets, capturing the intricate dynamics and intrinsically complex interactions.
Findings
FEGNS achieves a remarkable 30.3% improvement in simulation accuracy over traditional methods, coupled with a 51.6% enhancement in computational speed. It exhibits robust generalization capabilities across diverse materials, enabling realistic simulations of droplet effects. Comparative analyses and empirical validations demonstrate FEGNS’s superior performance against existing benchmark models.
Originality/value
The originality of FEGNS lies in its adaptive filtering layer, which independently adjusts filtering weights per node, and a novel aggregator fusion strategy that enriches the network’s expressive power by combining multiple aggregation functions. To facilitate further research and practical deployment, the FEGNS model has been made accessible on GitHub (https://github.com/nanjinyao/FEGNS/tree/main).
Keywords
Acknowledgements
This study was supported by National Natural Science Foundation of China (52275441) and Shenzhen Science and Technology Program (WDZC20231129101903002).
Declaration of competing interest: The authors have no competing interests to declare that are relevant to the content of this article.
Citation
Nan, J., Feng, P., Xu, J. and Feng, F. (2024), "Efficient modeling of liquid splashing via graph neural networks with adaptive filter and aggregator fusion", International Journal of Numerical Methods for Heat & Fluid Flow, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/HFF-01-2024-0077
Publisher
:Emerald Publishing Limited
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