What are the differences between an LES-SGS model and a RANS based turbulence model?
The biggest difference between LES and RANS is that, contrary to LES, RANS assumes that \(\overline{u'_i} = 0\) (see the Reynolds-averaged Navier–Stokes equations). In LES the filter is spatially based and acts to reduce the amplitude of the scales of motion, whereas in RANS the time filter removes ALL scales of motion with timescales less than the filter width.
I would recommend reading Fröhlich, Jochen, and Dominic von Terzi. "Hybrid LES/RANS methods for the simulation of turbulent flows." Progress in Aerospace Sciences 44.5 (2008): 349-377.
From that paper, specifically the section 'Structural similarity of LES and RANS equations', you can see that the equations being solved are essentially the same for LES and RANS, however, the physics are different. The main difference being that in RANS the unclosed term is a function of the turbulent kinetic energy and the turbulent dissipation rate whereas in LES the closure term is dependent on the length scale of the numerical grid. So in RANS the results are independent of the grid resolution!
A model qualifies as an LES model if it explicitly involves in one or
the other way the step size of the computational grid. RANS models, in
contrast, only depend on physical quantities, including geometric
features like the wall distance.
As far as typical processes, this figure summarizes it pretty well. DNS resolves all scales of motion, all the way down to the Kolmogorov scale. LES is next up and resolves most of the scales, with the smallest eddies being modeled. RANS is on the other end of the spectrum from DNS, where only the large-scale eddies are resolved and the remaining scales are modeled.

The figure above is from André Bakker's lectures: http://www.bakker.org/dartmouth06/engs150/10-rans.pdf
DNS: Very small scale flow (ex:turbulent boundary layers). Currently computationally intractable for most problems.
LES: Aims to solve the computational cost that DNS poses and reveals the eddies hidden behind the mean in RANS. Good for coastal scale scale 2D simulations and possibly lab-scale 3D simulations with a highly optimized parallel code.
RANS: It is the least computationally expensive method that is used for turbulent modeling, but it is really not very good when certain phenomena cannot be averaged, such as instabilities. Acoustic waves are also incorrectly modeled because they are inherently unsteady processes which can't be averaged, so typically modelers will crank up the turbulent and numerical viscosity to remove acoustic waves from the system.
This shows the main difference between LES and RANS.

What are the differences between an LES-SGS model and a RANS based turbulence model?的更多相关文章
- stall and flow separation on airfoil or blade
stall stall and flow separation Table of Contents 1. Stall and flow separation 1.1. Separation of Bo ...
- Core - Provide an easy way to store administrator and user model differences in a custom store (e.g., in a database)
https://www.devexpress.com/Support/Center/Question/Details/S32444/core-provide-an-easy-way-to-store- ...
- [翻译+山寨]Hangfire Highlighter Tutorial
前言 Hangfire是一个开源且商业免费使用的工具函数库.可以让你非常容易地在ASP.NET应用(也可以不在ASP.NET应用)中执行多种类型的后台任务,而无需自行定制开发和管理基于Windows ...
- EF 5 最佳实践白皮书
Performance Considerations for Entity Framework 5 By David Obando, Eric Dettinger and others Publish ...
- (转)LSTM NEURAL NETWORK FOR TIME SERIES PREDICTION
LSTM NEURAL NETWORK FOR TIME SERIES PREDICTION Wed 21st Dec 2016 Neural Networks these days are th ...
- 转:python获取linux系统及性能信息
原文:http://amitsaha.github.io/site/notes/articles/python_linux/article.html In this article, we will ...
- (转)分布式深度学习系统构建 简介 Distributed Deep Learning
HOME ABOUT CONTACT SUBSCRIBE VIA RSS DEEP LEARNING FOR ENTERPRISE Distributed Deep Learning, Part ...
- 【ASP.NET MVC 5】第27章 Web API与单页应用程序
注:<精通ASP.NET MVC 3框架>受到了出版社和广大读者的充分肯定,这让本人深感欣慰.目前该书的第4版不日即将出版,现在又已开始第5版的翻译,这里先贴出该书的最后一章译稿,仅供大家 ...
- Why Apache Beam? A data Artisans perspective
https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison https://github.com/apache/ ...
随机推荐
- WCF - Hosting WCF Service 四种托管方式
https://www.tutorialspoint.com/wcf/wcf_hosting_service.htm After creating a WCF service, the next st ...
- 杂项-职位-DBA:DBA
ylbtech-杂项-职位-DBA:DBA 数据库管理员(Database Administrator,简称DBA),是从事管理和维护数据库管理系统(DBMS)的相关工作人员的统称,属于运维工程师的 ...
- 008-elasticsearch5.4.3【二】ES使用、ES客户端、索引操作【增加、删除】、文档操作【crud】
一.ES使用,以及客户端 1.pom引用 <dependency> <groupId>org.elasticsearch.client</groupId> < ...
- 011-Spring Boot 运行流程分析SpringApplication.run
一.程序入口 1.1.静态方法 //直接调用run方法 ConfigurableApplicationContext context = SpringApplication.run(App.class ...
- loc() iloc() at() iat()函数
1 四个函数都是用于dataframe的定位 []用于直接定位. loc()函数是用真实索引,iloc()函数是用索引序号. loc()函数切片是左闭右闭,iloc()函数切片是左闭右开. at(), ...
- window.open弹窗阻止问题解决之道
https://segmentfault.com/a/1190000015381923https://segmentfault.com/a/1190000014988094https://www.cn ...
- sync_binlog innodb_flush_log_at_trx_commit 深入理解
innodb_flush_log_at_trx_commit和sync_binlog 两个参数是控制MySQL 磁盘写入策略以及数据安全性的关键参数.本文从参数含义,性能,安全角度阐述两个参数为不同的 ...
- 005/搭建fabric环境(一)
一.安装虚拟机VMware 参考博客:https://blog.csdn.net/u013142781/article/details/50529030 Step1:下载ubuntu镜像 (约1.8G ...
- Java 位运算符和移位运算符
一,运算的位运算符: & ~ | ^ 主要是对二进制的位计算 : & : 两个操作数中位都为1 结果才为1 其他结果为0 forExample: 128 ...
- Linux系统中tomcat的安装及优化
Linux系统中Tomcat 8 安装 Tomcat 8 安装 官网:http://tomcat.apache.org/ Tomcat 8 官网下载:http://tomcat.apache.org/ ...