{"title":"A direct quantification of numerical dissipation towards improved large eddy simulations","authors":"Guangrui Sun , Xingyi Wang , Yongliang Yang","doi":"10.1016/j.physd.2024.134433","DOIUrl":null,"url":null,"abstract":"<div><div>In implicit large eddy simulations (ILES), it becomes increasingly clear that numerical errors are essential to simulation accuracy. Nevertheless, whether the numerical dissipation in a CFD solver can be regarded as a means of turbulence modeling cannot be known <span><math><mi>a</mi></math></span> <span><math><mrow><mi>p</mi><mi>r</mi><mi>i</mi><mi>o</mi><mi>r</mi><mi>i</mi></mrow></math></span>. In the present work, we propose a general method to quantify the numerical dissipation rate for arbitrary flow solvers. Unlike previous approaches in which the numerical dissipation is estimated from the perspective of kinetic energy transfer, our method focuses on direct comparisons with the SGS dissipation from explicit models. The new method is both self-contained and self-consistent, which can be applied to any numerical solver through a simple post-processing step in the physical space. We show that for two common techniques to introduce numerical dissipation (through numerical schemes and solution filtering), the quantification results help to determine if a simulation can be considered as a legitimate ILES run and provide direct guidance for designing better models. When the numerical dissipation is already significant, an improved ILES filtering approach is proposed, which reduces the native numerical dissipation and works better for low order codes. The methods are general and work well for different Reynolds numbers, grid resolutions, and numerical schemes.</div></div>","PeriodicalId":20050,"journal":{"name":"Physica D: Nonlinear Phenomena","volume":"471 ","pages":"Article 134433"},"PeriodicalIF":2.7000,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physica D: Nonlinear Phenomena","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S016727892400383X","RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
In implicit large eddy simulations (ILES), it becomes increasingly clear that numerical errors are essential to simulation accuracy. Nevertheless, whether the numerical dissipation in a CFD solver can be regarded as a means of turbulence modeling cannot be known . In the present work, we propose a general method to quantify the numerical dissipation rate for arbitrary flow solvers. Unlike previous approaches in which the numerical dissipation is estimated from the perspective of kinetic energy transfer, our method focuses on direct comparisons with the SGS dissipation from explicit models. The new method is both self-contained and self-consistent, which can be applied to any numerical solver through a simple post-processing step in the physical space. We show that for two common techniques to introduce numerical dissipation (through numerical schemes and solution filtering), the quantification results help to determine if a simulation can be considered as a legitimate ILES run and provide direct guidance for designing better models. When the numerical dissipation is already significant, an improved ILES filtering approach is proposed, which reduces the native numerical dissipation and works better for low order codes. The methods are general and work well for different Reynolds numbers, grid resolutions, and numerical schemes.
期刊介绍:
Physica D (Nonlinear Phenomena) publishes research and review articles reporting on experimental and theoretical works, techniques and ideas that advance the understanding of nonlinear phenomena. Topics encompass wave motion in physical, chemical and biological systems; physical or biological phenomena governed by nonlinear field equations, including hydrodynamics and turbulence; pattern formation and cooperative phenomena; instability, bifurcations, chaos, and space-time disorder; integrable/Hamiltonian systems; asymptotic analysis and, more generally, mathematical methods for nonlinear systems.