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. C++中的集合和字典

    https://blog.csdn.net/sinat_39037640/article/details/74080509

  2. 从进度条和alert的出现顺序来了解浏览器 UI 渲染 & JS进程

    项目里有一个需求是在上传文件的时候需要显示进度条,那么理所当然的在上传完成后就需要提示用户上传完毕并且更新进度条. 之前的预期表现是,上传完毕后,先更新进度条到100%,再alert出提示,所以代码如 ...

  3. javaweb学习总结(十)——HttpServletRequest对象(一) https://www.cnblogs.com/xdp-gacl/p/3798347.html

    一.HttpServletRequest介绍 HttpServletRequest对象代表客户端的请求,当客户端通过HTTP协议访问服务器时,HTTP请求头中的所有信息都封装在这个对象中,通过这个对象 ...

  4. hbase的API

    import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.hbase.*; import org.apache.had ...

  5. Java课堂笔记(二):面向对象

    几乎每一本介绍Java语言的书中都会提到“面向对象”的这个概念,然而博主初学Java时看到这方面的内容一般都是草草地看一看,甚至是直接略过.原因很简单:考试基本不考,而且初学阶段写代码也很少用上.但事 ...

  6. 应用安全-安全设备-Waf系列-软Waf-安全狗(Safedog)

    安装 - Linux 下载 http://download.safedog.cn/safedog_linux64.tar.gz wget http://download.safedog.cn/safe ...

  7. javascript 阻止事件冒泡

    阻止冒泡 冒泡简单的举例来说,儿子知道了一个秘密消息,它告诉了爸爸,爸爸知道了又告诉了爷爷,一级级传递从而引起事件的混乱,而阻止冒泡就是不让儿子告诉爸爸,爸爸自然不会告诉爷爷了. 举个栗子: 父容器是 ...

  8. Java判断一个类里是否存在某个属性

    Java判断一个类里是否存在某个属性 测试pojo类,比方我有个User类 @Getter @Setter public class User { private Long id; private S ...

  9. VSphere服务器ESXI4.1.0设置虚拟主机来电开机自启动

    vSphere服务器ESXI设置虚拟主机来电自启动 首先查看我自己VMware vSphere版本为4.1.0(需要在虚拟主机电源为关闭状态下编辑) 然后双击主机,点击配置---虚拟机启动/关机 点击 ...

  10. [Python3] 012 元组:list,我们不一样!

    目录 0. 元组的独白 1. 元组的创建 2. 元组的特性 (1) 概述 (2) 少废话,上例子 1) 索引 2) 分片 3) 序列运算 4) 成员检测 3. 元组的遍历 (1) 简单总结 (2) 少 ...