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

  1. 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 ...

  2. 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- ...

  3. [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 ...

  4. EF 5 最佳实践白皮书

    Performance Considerations for Entity Framework 5 By David Obando, Eric Dettinger and others Publish ...

  5. (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 ...

  6. 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 ...

  7. (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 ...

  8. 【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 ...

  9. 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

  1. Basic Model Theory of XPath on Data Trees

    w https://openproceedings.org/2014/conf/icdt/FigueiraFA14.pdf From a database perspective, however, ...

  2. oracle常用sql语句和函数

    --查询表的字段数 select count(*) from user_tab_columns where table_name = '表名'; --查询数据库用户密码的profile(一般为defa ...

  3. 架构-数据库访问-SQL语言进行连接数据库服务器-OLE:OLE

    ylbtech-架构-数据库访问-SQL语言进行连接数据库服务器-OLE:OLE Object Linking and Embedding,对象连接与嵌入,简称OLE技术.OLE 不仅是桌面应用程序集 ...

  4. JS text节点无innerHTML

    在线预览 text节点无innerHTML这个属性!!! 如果直接修改text节点的属性(data,nodeValue,textContent),或者使用js原生的修改text节点的内容的方法都会将H ...

  5. tail()函数

    与head()函数类似,默认是取dataframe中的最后五行. 函数源码: def tail(self, n=): """ Returns last n rows &q ...

  6. JDK和SDK的区别:

    参考链接:https://www.cnblogs.com/vaelailai/p/7976158.html jdk,是Java开发工具包,主要用于编写Java程序:也就是说你要使用Java语言,就需要 ...

  7. kafka+hbase+hive实现实时接入数据至hive

    整体架构: 项目目标,实现配置mysql,便可以自动化入湖至Hive,入湖至Hive方便后期数据分析. 首先在Mysql中配置好kafka的topic.Server以及入户表等信息,java程序初始化 ...

  8. python 二维数组转一维数组

    三种方法 比如 a = [[1, 2], [3, 4], [5, 6]] 列表推导式 [i for j in a for i in j] 库函数 from itertools import chain ...

  9. HDU-1754 I Hate It(线段树,区间最大值)

    很多学校流行一种比较的习惯.老师们很喜欢询问,从某某到某某当中,分数最高的是多少.  这让很多学生很反感. 不管你喜不喜欢,现在需要你做的是,就是按照老师的要求,写一个程序,模拟老师的询问.当然,老师 ...

  10. bzoj3188 [Coci 2011]Upit(分块)

    Time Limit: 10 Sec  Memory Limit: 128 MB Description 你需要维护一个序列,支持以下4种操作.一,将区间(u,v)的数覆盖为C:二,将区间(u,v)的 ...