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arXiv 2107.09591physics.flu-dyncs.AIcs.NAmath.NA
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含几何参数湍流的混合神经网络降阶模型

Hybrid neural network reduced order modelling for turbulent flows with geometric parameters

SISSA(国际高等研究院);Volkswagen AG(大众汽车集团)

Matteo Zancanaro, Markus Mrosek, Giovanni Stabile, Carsten Othmer, Gianluigi Rozza

摘要:

几何参数化偏微分方程现在被广泛用于许多不同的领域,例如形状优化过程或患者特定的手术研究。这项工作的重点是这个主题的一些进展,能够提高精度,同时依赖于高成本效益比的性能与以前的方法。本文的主要范围是介绍一种新的技术混合了一个经典的Galerkin投影方法与数据驱动的方法,以获得一个通用的和准确的算法的分辨率几何参数化不可压缩湍流Navier-Stokes问题。两个不同的测试案例:一个经典的学术回步问题和形状变形艾哈迈德体的应用程序,证明了这个程序的有效性。结果详细显示了我们开发的架构的属性,同时揭示了这项工作未来可能的前景。

英文摘要:

Geometrically parametrized Partial Differential Equations are nowadays widely used in many different fields as, for example, shape optimization processes or patient specific surgery studies. The focus of this work is on some advances for this topic, capable of increasing the accuracy with respect to previous approaches while relying on a high cost-benefit ratio performance. The main scope of this paper is the introduction of a new technique mixing up a classical Galerkin-projection approach together with a data-driven method to obtain a versatile and accurate algorithm for the resolution of geometrically parametrized incompressible turbulent Navier-Stokes problems. The effectiveness of this procedure is demonstrated on two different test cases: a classical academic back step problem and a shape deformation Ahmed body application. The results show into details the properties of the architecture we developed while exposing possible future perspectives for this work.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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