用于外部空气动力学三维流场预测的大规模图机器学习代用模型

Davide Roznowicz, Giovanni Stabile, Nicola Demo, Davide Fransos, Gianluigi Rozza
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引用次数: 0

摘要

文章介绍了归纳图机器学习代用模型在准确、高效地预测工业几何形状的三维流动方面的应用,并明确将重点放在赛车运动的外部空气动力学方面。最终目的是建立一个能提供快速预测的代用模型,从而避免传统计算流体动力学(CFD)模拟所带来的不可行的计算负担。鉴于图神经网络能够流畅地处理非结构化数据,而这正是工业仿真的典型环境,因此我们在本文中研究了图神经网络的使用。我们将一种高效的子图抽样方法与我们的模型相结合,专门用于大型数据集的训练。REV-GNN 是我们选择的图机器学习模型,它能够从相邻图区域中提取更深入的见解。此外,它的独特之处还在于其可逆架构,可以在增加网络层数的同时保持内存用量不变。我们将该方法应用于参数化 Navier-Stokes 问题,对其进行了测试,在该问题中,参数控制着手头工业产品(此处为摩托车)的表面形状。
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Large-scale graph-machine-learning surrogate models for 3D-flowfield prediction in external aerodynamics
The article presents the application of inductive graph machine learning surrogate models for accurate and efficient prediction of 3D flow for industrial geometries, explicitly focusing here on external aerodynamics for a motorsport case. The final aim is to build a surrogate model that can provide quick predictions, bypassing in this way the unfeasible computational burden of traditional computational fluid dynamics (CFD) simulations. We investigate in this contribution the usage of graph neural networks, given their ability to smoothly deal with unstructured data, which is the typical context for industrial simulations. We integrate an efficient subgraph-sampling approach with our model, specifically tailored for large dataset training. REV-GNN is the chosen graph machine learning model, that stands out for its capacity to extract deeper insights from neighboring graph regions. Additionally, its unique feature lies in its reversible architecture, which allows keeping the memory usage constant while increasing the number of network layers. We tested the methodology by applying it to a parametric Navier–Stokes problem, where the parameters control the surface shape of the industrial artifact at hand, here a motorbike.
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来源期刊
Advanced Modeling and Simulation in Engineering Sciences
Advanced Modeling and Simulation in Engineering Sciences Engineering-Engineering (miscellaneous)
CiteScore
6.80
自引率
0.00%
发文量
22
审稿时长
30 weeks
期刊介绍: The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.
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