Volume and Mass Conservation in Lagrangian Meshfree Methods

IF 2.9 3区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY International Journal for Numerical Methods in Engineering Pub Date : 2025-01-23 DOI:10.1002/nme.7657
Pratik Suchde, Christian Leithäuser, Jörg Kuhnert, Stéphane P. A. Bordas
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Abstract

Meshfree Lagrangian frameworks for free surface flow simulations do not conserve fluid volume. Meshfree particle methods like SPH are not mimetic, in the sense that discrete mass conservation does not imply discrete volume conservation. On the other hand, meshfree collocation methods typically do not use any notion of mass. As a result, they are neither mass conservative nor volume conservative at the discrete level. In this paper, we give an overview of various sources of conservation errors across different meshfree methods. The present work focuses on one specific issue: inconsistent volume and mass definitions. We introduce the concept of representative masses and densities, which are essential for accurate post-processing in meshfree collocation methods. Using these, we introduce an artificial compressibility in the fluid to reduce errors in volume conservation. Numerical experiments show that the introduced frameworks significantly improve volume conservation behaviour in meshfree collocation methods, even for complex industrial test cases such as automotive water crossing.

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拉格朗日无网格法中的体积和质量守恒
用于自由表面流动模拟的无网格拉格朗日框架不能保留流体体积。像SPH这样的无网格粒子方法不是模拟的,因为离散质量守恒并不意味着离散体积守恒。另一方面,无网格配置方法通常不使用任何质量的概念。因此,它们在离散水平上既不是质量保守,也不是体积保守。在本文中,我们概述了不同的无网格方法的各种来源的守恒误差。目前的工作集中在一个具体的问题:不一致的体积和质量的定义。我们引入了代表性质量和密度的概念,这是在无网格配置方法中精确后处理所必需的。利用这些,我们在流体中引入了一个人工的可压缩性,以减少体积守恒的误差。数值实验表明,所引入的框架显著改善了无网格配置方法的体积守恒行为,即使是在复杂的工业测试用例(如汽车渡水)中也是如此。
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来源期刊
CiteScore
5.70
自引率
6.90%
发文量
276
审稿时长
5.3 months
期刊介绍: The International Journal for Numerical Methods in Engineering publishes original papers describing significant, novel developments in numerical methods that are applicable to engineering problems. The Journal is known for welcoming contributions in a wide range of areas in computational engineering, including computational issues in model reduction, uncertainty quantification, verification and validation, inverse analysis and stochastic methods, optimisation, element technology, solution techniques and parallel computing, damage and fracture, mechanics at micro and nano-scales, low-speed fluid dynamics, fluid-structure interaction, electromagnetics, coupled diffusion phenomena, and error estimation and mesh generation. It is emphasized that this is by no means an exhaustive list, and particularly papers on multi-scale, multi-physics or multi-disciplinary problems, and on new, emerging topics are welcome.
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