Efficient large-scale graph learning for predicting the 3D multi-physics flow fields of axial compressor Rotor37 with variable geometry

Yichen Hao, Qinglong Liu, Jia Li, Siyuan Zuo, Yiyang Liu, Xiaofang Wang, Xiaomo Jiang and Haitao Liu

Energy, 2025, vol. 326, issue C

Abstract: Accurate prediction of the multi-scale, multi-physics flow characteristics in axial compressors is essential for optimizing the aerodynamic performance. Traditional experimental and numerical simulation methods are costly and difficult to achieve agile design of modern compressors. To achieve fast yet accurate predictions of the complicated flow characteristics of axial compressor, a data-driven deep forecasting model, named GraphRotorNet, has been built via graph neural networks (GNNs) for NASA’s Rotor37 axial compressor. The model employs sub-graph clustering to efficiently process large-scale multi-physics snapshots from Rotor37’s 3D single-passage fluid domain. Simultaneously, a deep message-passing mechanism is developed to enhance the ability to capture multi-scale spatial features of compressible flow fields under varying geometric parameters. Besides, extensive numerical experiments show that the GraphRotorNet outperforms state-of-the-art competitors in accurately predicting Rotor37’s 3D multi-physics fields and performance characteristics. Finally, the model also imposes a lower GPUs burden when processing large-scale graphs for real-world engineering applications.

Keywords: Axial compressor; Rotor37; 3D flow field prediction; Graph neural networks; Sub-graph clustering; Message passing mechanism (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:326:y:2025:i:c:s0360544225017256

DOI: 10.1016/j.energy.2025.136083

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