Modeling the wall shear stress in large-eddy simulation using graph neural networks

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2023-03-09 DOI:10.1017/dce.2023.2
D. Dupuy, N. Odier, C. Lapeyre, D. Papadogiannis
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Abstract

Abstract As the Reynolds number increases, the large-eddy simulation (LES) of complex flows becomes increasingly intractable because near-wall turbulent structures become increasingly small. Wall modeling reduces the computational requirements of LES by enabling the use of coarser cells at the walls. This paper presents a machine-learning methodology to develop data-driven wall-shear-stress models that can directly operate, a posteriori, on the unstructured grid of the simulation. The model architecture is based on graph neural networks. The model is trained on a database which includes fully developed boundary layers, adverse pressure gradients, separated boundary layers, and laminar–turbulent transition. The relevance of the trained model is verified a posteriori for the simulation of a channel flow, a backward-facing step and a linear blade cascade.
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大涡模拟中壁面剪应力的图神经网络建模
随着雷诺数的增加,近壁湍流结构变得越来越小,复杂流动的大涡模拟变得越来越棘手。墙壁建模通过允许在墙壁上使用较粗的单元,减少了LES的计算需求。本文提出了一种机器学习方法来开发数据驱动的墙壁剪切应力模型,该模型可以直接在模拟的非结构化网格上进行后验操作。模型结构基于图神经网络。该模型是在一个数据库上训练的,该数据库包括完全发育的边界层、逆压梯度、分离的边界层和层流-湍流过渡。通过对通道流、后向阶跃和线性叶栅的后验仿真,验证了所训练模型的相关性。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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