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湍流流动的基础理论与数值模拟研究(Pope.turbulent.flows)

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摘要:

本文是 Pope《Turbulent Flows》核心章节节选,系统搭建湍流统计理论基础体系。开篇阐释湍流随机性源于初始与边界微小扰动,借助洛伦兹系统直观说明混沌敏感性;引入概率密度、均值、协方差等统计工具,介绍均匀、高斯、对数正态等常用分布,定义随机场、自相关与能谱。推导雷诺分解与雷诺平均 N-S 方程,点明湍流封闭难题,阐述布辛涅斯克涡粘假设、梯度扩散假设及其局限性。以圆形射流为典型自由剪切流案例,结合实验数据论证自相似特性,分析射流速度衰减、展宽规律与雷诺数无关的核心特征,为湍流建模、CFD 数值模拟提供完备理论根基。


Preface                                                                              

knowledge of probability theory,and consequently the necessary material is provided in the text (e.g.,Sections 3.2-3.5).

For a less demanding pace,Parts I and II can be covered in two semesters

-there  is  ample  material.Alternatively,if  a  coverage  of modelling  is  not required,Part  I  by  itself provides  a  reasonably  complete  introduction  to turbulent flows.

Many of the exercises ask the reader to 'show that..,'and thereby intro- duce  additional  results  and  observations.Consequently,it  is  recommended that all the exercises be read,even if they are not performed.The book is designed to be a self-contained text,but sufficient references are given to provide an entry into the research literature.

However much care is taken in the preparation of a book of this nature, it is inevitable that there will be errors in the first printing.A list of known corrections     is     given      at     http://mae.cornell.edu/~pope/TurbulentFlows. The  reader  is  asked  to  report  any  further  corrections  to  the  author  at pope@mae.cornell.edu.

I  am profoundly  grateful  to  many people  for  their help  in  the prepa- ration  of  this  work.For  their   support  and  technical   input  I  thank  my colleagues  at  Cornell,David  Caughey,Sidney  Leibovich,John  Lumley,Di- etmar Rempfer,and Zellman Warhaft.For their valuable suggestions based on  reading  draft  chapters,I  am  grateful  to  Peter  Bradshaw,Paul  Durbin, Rodney  Fox,Kemo  Hanjalic,Charles  Meneveau,Robert  Moser,Blair  Perot, Ugo  Piomelli,P.K.Yeung,and  Norman  Zabusky.Similarly,I   am   grateful to the following Cornell graduates for their feedback on drafts of the book: Bertrand  Delarue,Thomas  Dreeben,Matthew  Overholt,Paul  Van   Slooten, Jun  Xu,Cem  Albukrek,Dawn  Chamberlain,Timothy  Fisher,Laurent  Myd- larski,Gad  Reinhorn,Shankar   Subramaniam,and  Walter  Welton.The   first five mentioned are also thanked for their assistance in producing the figures. Most of the typescript was prepared by June Meyermann,whose patience, accuracy,and enthusiasm are greatly appreciated.The accuracy of the bib- liography has been much improved by the careful checking performed by Sarah  Pope.Above  all,I  wish  to  thank  my  wife,Linda,for  her  patience, support,and encouragement during this project and over the years.


Nonenclature







The notation used is given here in the following order:upper-case Roman, lower-case   Roman,upper-case   Greek,lower-case    Greek,superscripts,sub- scripts,symbols,and  abbreviations.Then  the   symbols   O(),o(),and~ that are used to denote the order of a quantity are explained.

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Use of symbols for order and scaling

The  statement  that  ‘the  variable  f  is  of  order  g'has  different  meanings depending on the context and the type of 'order'implied.The symbols O(h) (read  'big order h’or 'big O of h')and o(h)(read  'little order h’or little O of h’)indicate  quantities,dependent  on h,such that

image.png

Thus,for example,the Taylor series for a function f(x)can be written

image.png

In the expression f(h)~hP,the symbol~ can be read ‘varies as'or 'scales with’,and  it  indicates  that  the  quantity  f(h)/h  is  approximately  constant (possibly over a limited range of h).In some contexts this type of relation is also  stated  as  'f(h)is  of order  h?”:for  example,the  FFT  of N  data  points can be computed in of order Nlog N operations.

A statement such as 'f is of order 100'is used to indicate the approximate magnitude of f to the nearest power of ten.Thus,in this case,the value of f is roughly between 30 and 300.


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