Large/small eddy simulations: A high-fidelity method for studying high-Reynolds number turbulent flows

物理 雷诺数 湍流 机械 大涡模拟 统计物理学 经典力学
作者
Arnab Moitro,Sai Sandeep Dammati,Alexei Poludnenko
出处
期刊:Physics of Fluids [American Institute of Physics]
卷期号:36 (9)
标识
DOI:10.1063/5.0225079
摘要

Direct numerical simulations (DNS) are one of the main ab initio tools to study turbulent flows. However, due to their considerable computational cost, DNS are primarily restricted to canonical flows at moderate Reynolds numbers, in which turbulence is isolated from the realistic, large-scale flow dynamics. In contrast, lower fidelity techniques, such as large eddy simulations (LES), are employed for modeling real-life systems. Such approaches rely on closure models that make multiple assumptions, including turbulent equilibrium, small-scale universality, etc., which require prior knowledge of the flow and can be violated. We propose a method, which couples a lower-fidelity, unresolved, time-dependent calculation of an entire system (LES) with an embedded small eddy simulation (SES) that provides a high-fidelity, fully resolved solution in a sub-region of interest of the LES. Such coupling is achieved by continuous replacement of the large SES scales with a low-pass filtered LES velocity field. The method is formulated in physical space, with no assumptions of equilibrium, small-scale structure, and boundary conditions. A priori tests of both steady and unsteady homogeneous, isotropic turbulences are used to demonstrate the method's accuracy in recovering turbulence properties, including spectra, probability density functions of the intermittent quantities, and sub-grid dissipation. Finally, SES is compared with two alternative approaches: one embedding a high-resolution region through static mesh refinement and a generalization of the traditional volumetric spectral forcing. Unlike these methods, SES is shown to achieve DNS-level accuracy at a fraction of the cost of the full DNS, thus opening the possibility to study high-Re flows.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
SamuelLiu发布了新的文献求助10
刚刚
txfxx完成签到,获得积分10
刚刚
xrc发布了新的文献求助10
1秒前
1秒前
2秒前
潇潇雨歇发布了新的文献求助10
3秒前
3秒前
星辰大海应助科研通管家采纳,获得10
3秒前
迟大猫应助科研通管家采纳,获得10
3秒前
kingwill应助科研通管家采纳,获得20
3秒前
科研通AI5应助科研通管家采纳,获得10
4秒前
飘逸樱桃发布了新的文献求助10
4秒前
bkagyin应助科研通管家采纳,获得10
4秒前
Akim应助科研通管家采纳,获得10
4秒前
科研通AI5应助科研通管家采纳,获得10
4秒前
Owen应助科研通管家采纳,获得10
4秒前
4秒前
4秒前
所所应助科研通管家采纳,获得30
4秒前
汉堡包应助科研通管家采纳,获得10
4秒前
4秒前
stuffmatter应助科研通管家采纳,获得10
4秒前
香蕉觅云应助科研通管家采纳,获得10
4秒前
科研通AI5应助科研通管家采纳,获得10
4秒前
完美世界应助科研通管家采纳,获得10
4秒前
Owen应助科研通管家采纳,获得10
4秒前
FashionBoy应助科研通管家采纳,获得10
4秒前
5秒前
不配.应助科研通管家采纳,获得10
5秒前
CipherSage应助科研通管家采纳,获得10
5秒前
5秒前
英姑应助科研通管家采纳,获得10
5秒前
JamesPei应助科研通管家采纳,获得10
5秒前
5秒前
香蕉觅云应助科研通管家采纳,获得10
5秒前
5秒前
斯文的茹嫣完成签到,获得积分10
5秒前
5秒前
6秒前
Amy发布了新的文献求助10
6秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
Continuum thermodynamics and material modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Population Genetics 2000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Theory of Block Polymer Self-Assembly 750
지식생태학: 생태학, 죽은 지식을 깨우다 700
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3496374
求助须知:如何正确求助?哪些是违规求助? 3081314
关于积分的说明 9166581
捐赠科研通 2774132
什么是DOI,文献DOI怎么找? 1522339
邀请新用户注册赠送积分活动 705861
科研通“疑难数据库(出版商)”最低求助积分说明 703123