首页/文章/ 详情

计算流体动力学:原理与应用(第三版)核心内容梳理

1月前浏览1122

计算流体动力学:原理与应用(第三版)核心内容梳理(Computational Fluid Dynamics Principles and Applications, Third Edition 3rd Edition)

摘要:

本文是 Jiri Blazek 所著 CFD 经典教材第三章、第四章核心内容,系统阐述 CFD 控制方程数值求解框架,梳理有限差分、有限体积、有限元等主流空间离散方法,对比结构化与非结构化网格适配特性。重点详解有限体积法,划分单元中心、顶点重叠、对偶控制体积三类格式,剖析中心、矢通量分裂、Roe 格式等对流离散方案,介绍显式 / 隐式时间推进、多重网格加速、各类湍流模型与边界条件处理思路。完整给出二维、三维结构化网格几何量计算方法,推导多种通量离散公式,兼顾理论推导与工程实现,系统讲解高精度插值、限制器、预处理等关键数值技术,是可压缩流动仿真算法的权威理论参考。

ACKNOWLEDGMENTS

My  first  thanks  are  to  our  Creator,  without  whom  nothing  would  be  possible. Furthermore, I wish to thank my father for the initial motivation to start this project, as well as for his continuous help with the text and in particular with the drawings. I also gratefully acknowledge the support of the staff at Elsevier Ltd., foremost of C. Owen and H. Gray, during the preparation of this edition.


LIST OF SYMBOLS

image.png

image.png

image.png

image.png

image.png

ABBREVIATIONS

image.png


CHAPTER 1  Introduction

The history of the computational fluid dynamics  (CFD)  started in the  early  1970s. Around that time, it became an acronym for the combination of physics, numerical mathematics, and, to some extent, computer sciences—all employed to simulate fluid flows. The beginning of CFD was triggered by the availability of increasingly more powerful mainframes, and still the advances in CFD are closely linked to the evolution of the computer technology. Among the first applications of the  CFD methods was the simulation  of transonic flows based  on the solution  of the non-linear potential equation. With  the beginning  of the  1980s,  first  the  solutions  of two-dimensional (2-D) and later three-dimensional (3-D) Euler equations became feasible. Thanks to the rapidly increasing speed of supercomputers, and due to the development of a variety of numerical acceleration techniques like multigrid, it became possible to compute inviscid flows either past complete aircraft configurations or inside of turbomachinery. With the mid-1980s, the focus started to shift to the significantly more demanding simulations of viscous flows governed by the Navier-Stokes equations. Together with this, a variety of turbulence  models  evolved  with  different  degree  of numerical  complexity  and accuracy. The leading edge in turbulence modeling is represented by the direct numerical simulation and the large eddy simulation (LES).

With  the  advances  of the  numerical  methodologies,  particularly  of the  implicit schemes, solution offlow problems that require real gas modeling also became feasible by the end of the 1980s. Among the first large scale application, 3-D hypersonic flow past re-entry vehicles, like the European HERMES shuttle, was computed using equilibrium and later non-equilibrium chemistry models. Many research activities were and still are devoted to the numerical simulation of combustion and particularly to flame modeling. These efforts are very important for the development of low emission gas turbines and engines. Also, the modeling of steam and in particular condensation of steam became a key factor in designing efficient steam turbines.

Due  to  the  steadily  increasing  demands  on  the  complexity  and  the  fidelity  of flow simulations, grid generation methods became more and more sophisticated. The development started first with relatively simple structured meshes, constructed either by algebraic methods or by using partial differential equations. But with the increasing geometrical complexity of the configurations, the grids had to be divided into a number of topologically simpler blocks (multiblock approach). The next logical step was to allow for non-matching interfaces between the grid blocks, in order to relieve the constraints  

imposed  on  the  grid  generation  in  a  single  block.  Finally,  solution  methodologies were introduced that can deal with grids overlapping each other (Chimera technique). This  allowed,  for  example,  to  simulate  the  flow  past  the  complete  Space  Shuttle vehicle  with  the  external  tank  and  boosters  attached.  However,  the  generation  of a  structured,  multiblock  grid  for  a  complicated  geometry  may  still  take  weeks  to accomplish. Therefore, the research also focused on the development of unstructured grid generators and flow solvers, which promise significantly reduced setup times, with only a minor user intervention. Another very important feature of the unstructured methodology is the possibility of solution-based grid adaptation. The first unstructured grids consisted exclusively of isotropic tetrahedra, which was fully sufficient for inviscid flows governed by the Euler equations. However, the solution of the Navier-Stokes equations at higher Reynolds numbers requires grids, which are highly stretched in the shear layers. Although such grids can also be constructed from tetrahedral elements, it is advisable to use pris ms or hexahedra in the viscous flow regions and tetrahedra outside. This improves not only the solution accuracy, but it also saves the number of elements, faces, and edges. Thus, the memory and run-time requirements of the simulation are reduced significantly.

Nowadays,  CFD  methodologies  are  routinely  employed  in  the  fields  of aircraft, turbomachinery, car, and ship design. Furthermore, CFD is also applied in meteorology, oceanography, astrophysics, biology, oil recovery, and in architecture. Many numerical techniques developed for CFD are also utilized in the solution of the Maxwell equations or in aeroacoustics. Hence, CFD has become an important design tool in engineering, and  also  an  indispensable  research  tool  in  various  sciences.  Due  to  the  advances in numerical solution methods and in the  computer technology,  geometrically and physically  complex  cases  can  be  run  even  on  PCs  or  on  PC  clusters.  Large  scale simulations of viscous flows on grids consisting of dozens of millions of elements can be accomplished within only a few hours on today’s supercomputers. However, it would be completely wrong to think that CFD represents a mature technology now, like, for example, the finite-element methods in solid mechanics. No, there are still many open questions like turbulence and combustion modeling, heat transfer, efficient solution techniques for viscous flows, robust but accurate  discretization methods, automated grid generators, etc. The coupling between CFD and other disciplines (like the solid mechanics) requires further research as well. Quite new opportunities also arise in the design optimization by using CFD.

The objective of this book is to provide university students with a solid foundation for understanding the numerical methods employed in today’s CFD and to familiarize them with modern CFD codes by hands-on experience. The book is also intended for engineers and scientists starting to work in the field of CFD, or who are applying CFD codes. The mathematics used is always connected to the underlying physics to

facilitate the understanding of the matter. The text can serve as a reference handbook too. Each chapter contains an extensive bibliography, which may form the basis for further studies.

CFD methods are concerned with the solution of equations of fluid motion as well as with the interaction of the fluid with solid bodies. The equations governing the motion of an inviscid fluid (Euler equations) and of viscous fluid (Navier-Stokes equations) are derived in Chapter 2. Additional thermodynamic relations for a perfect gas as well as for a real gas are also discussed. Chapter 3 deals with the principles of solution of the governing equations. The most important methodologies are briefly described and the corresponding references are provided. Chapter 3 can be used together with Chapter 2 to get acquainted with the fundamental principles of CFD.

Numerous schemes were developed in the past for the spatial discretization of the Euler and the Navier-Stokes equations. A unique feature of the present book is that it deals with both the structured (Chapter 4) as well as with the unstructured finite-volume schemes (Chapter 5), because of their broad application possibilities, especially for the treatment of complex flow problems routinely encountered in an industrial environment. The attention is particularly devoted to the definition of the various types of control volumes together with spatial discretization methodologies for convective and viscous fluxes. The 3-D finite-volume formulations of the most popular central and upwind schemes are presented in detail.

The methodologies for the temporal discretization of the governing equations can be divided into two main classes. One class comprises explicit time-stepping schemes (Section 6.1), and the other one consists of implicit schemes (Section 6.2). In order to provide a more complete overview, recently developed solution methods based on the Newton-iteration as well as standard techniques like the explicit Runge-Kutta schemes are discussed.

Two qualitatively different types of viscous fluid flows are encountered in general: laminar and  turbulent.  The  solution  of the  Navier-Stokes  equations  does  not  raise any  fundamental  difficulties  in  the  case  of laminar  flows.  However,  the  simulation of turbulent flows  continues to present a significant challenge as before. A relatively simple way of modeling the turbulence is offered by the so-called Reynolds-averaged Navier-Stokes equations. On the other hand, Reynolds stress models or LES enable considerably  more  accurate  predictions  of  turbulent  flows.  In  Chapter  7,  various well-proven and widely applied turbulence models of varying level of complexity are presented in detail.

In order to account for the specific features of a particular problem, and to obtain an unique solution of the governing equations, it is necessary to specify appropriate boundary conditions. Basically, there are two types of boundary conditions: physical and numerical. Chapter 8 deals with both types in different situations like solid walls, inlet, outlet, injection, and far-field. Symmetry planes, periodic and block boundaries are treated as well.

In order to reduce the computer time required to solve the governing equations for complex flow problems, it is quite essential to employ numerical acceleration techniques. Chapter 9 deals extensively, among others, with approaches like the implicit residual s moothing and multigrid. Another important methodology which is also described in Chapter 9 is preconditioning of the governing equations. It allows the application of a single numerical scheme for flows, where the Mach number varies between nearly zero and transonic or higher values. Finally, Chapter 9 contains a section on the parallelization of numerical computer codes by using different approaches.

Each  discretization  of  the  governing  equations  introduces  a  certain  error—the discretization error. Several consistency requirements have to be fulfilled by the dis- cretization scheme, in order to ensure the solution of the discretized equations closely approximates the solution of the original equations. This problem is addressed in the first two parts of Chapter 10. Before a particular numerical solution method is implemented, it is important to know, at least approximately, how the method will influence the stability and the convergence behavior of the CFD code. It was frequently confirmed that the Von Neumann stability an alysis can provide a good assess ment of the properties of a numerical scheme. Therefore, the third part of Chapter 10 deals with stability an alysis for various model equations.

One of the challenging tasks in CFD is the generation of structured or unstructured body-fitted  grids  around  complex  geometries.  The  grid  is  used  to  discretize  the governing equations in space. The accuracy of the flow  solution is therefore closely linked to the quality of the grid. In Chapter 11, the most important methodologies for the generation of structured as well as unstructured grids are discussed in depth.

In order to demonstrate the practical aspects of different numerical solution method- ologies, various source codes are available for download. Provided are the sources of quasi 1-D Euler, as well as of 2-D Euler and Navier-Stokes structured and unstructured flow solvers. Furthermore, source codes of 2-D structured algebraic and elliptic grid generators are included together with a converter from structured to unstructured grids. Furthermore, two programs are provided to conduct the linear stability an alysis of explicit and implicit time-stepping schemes. The source codes are completed by a set of worked out examples including the grids, the input files and the results. The code package also contains several programs for the demonstration of parallelization techniques. Chapter 12 describes the contents ofthe directories, the capabilities of the particular programs, and provides examples of their usage.

The present book is finalized with an Appendix and Index. The Appendix contains the governing equations presented in a differential form as well as their characteristic properties.  Formulations  of the  governing  equations  in  rotating  frame  of reference  and for moving grids are discussed along with some simplified forms. Furthermore, Jacobian and transformation matrices from conservative to characteristic variables are presented for two and three dimensions. The GMRES conjugate gradient method for the solution of linear equations systems is described next. The Appendix closes with a brief explanation of the tensor notation.


原资料见附件

免责声明:

本页面/内容部分素材来源于互联网公 开 信 息,旨在传递更多信息,不代表本平台立场。

版权归原作者或机构所有,如涉及侵权,请通过平台联系我们,我们将在核实后第一时间处理。

本平台对转载内容的真实性、准确性不作任何保证,用户需自行判断并承担使用风险。

附件

免费Q16-计算流体动力学:原理与应用(第三版)核心内容梳理(Computational Fluid Dynamics Principles and Applications, Third Edition 3rd Edition).pdf
ACTMaxwellSystemFluxFidelity湍流控制有限差分理论
著作权归作者所有,欢迎分享,未经许可,不得转载
首次发布时间:2026-07-10
最近编辑:1月前
仿真支持爱好者
在仿真的路上越走越远
获赞 333粉丝 21文章 318课程 0
点赞
收藏
作者推荐

STAR-CCM+中文案例教程 用户指南_V9.06_1401-2685

6.选择液膜混合物-液滴>模型>液膜-拉格朗日相间相互作用,然后将液膜相和拉格朗日相分别设为液膜和液滴。组分映射出现在模拟树中,这表明液膜和液滴相的组分之间需要映射。设置两相间映射:7.选择多相交互作用>相间相互作用>液膜混合物-液滴>模型>组分映射,然后单击连接属性的(自定义编辑器)。8.在连接对话框中,将液膜组分映射至其对应的气体组分,如下所示。9.单击确定。选择边缘剥离模型并设置其属性:10.右键单击多相交互作用>相间相互作用>液膜混合物-液滴>模型,然后选择选择模型。在液膜混合物-液滴模型选择对话框中:11.从可选模型框中选择边缘剥离。已自动选择多相材料选项。12.单击关闭。13.选择液膜混合物-液滴>模型>边缘剥离节点。14.为确保弯曲处出现剥离,请将最小棱角设为10.0deg。如果已定义液膜混合物-液滴相间相互作用,现在您可以通过编辑全局拉格朗日相的边界规格来设置液膜边界的边界相互作用模式。15.选择连续体>物理1>模型>拉格朗日多相>拉格朗日相>液滴>边界条件>液膜边界>物理条件>模式,然后将激活模式设为液膜。液膜模式仅在液膜连续体和拉格朗日相之间正确匹配之后方可使用。当成分已正确映射并且液膜和拉格朗日相使用的模型相同时,则会实现正确匹配。如果您对验证液膜混合物-液滴相间相互作用的液膜或拉格朗日相模型做出任何更改,激活模式会恢复为反弹。液膜选项在验证相间相互作用后方可使用。您必须将激活模式设置回液膜。16.保存模拟。设置参考值设置物理连续体的参考值。定义物理1连续体的参考值,以确保在预期的方向因重力作用而加速。在本教程中,在负Z方向施加重力。设置参考值:1.编辑物理1>参考值节点,然后设置下列属性:2.保存模拟。设置求解器参数和停止条件设置该模拟适当的求解器参数和停止条件。要设置求解器参数和停止条件:1.编辑求解器节点,然后设置下列属性:2.编辑停止条件节点,然后设置下列属性:3.保存模拟。设置蒸发率报告、监视器和绘图设置报告、监视器和绘图,以沿液膜边界监视液膜蒸发率。设置蒸发率报告、监视器和绘图:1.右键单击报告节点,然后选择新报告>表面平均值。2.将新创建的表面平均值1节点重命名为H2O蒸发率。3.将标量场函数设置为液膜蒸发率>水的液膜蒸发率。4.单击零部件属性右侧的省略号。5.在出现的对话框中,展开区域>流体节点,然后选择湿壁面[原位1].6.单击确定。H2O蒸发率-属性窗口如下所示:复制该报告,以创建乙二醇蒸发率报告:7.右键单击报告>H2O蒸发率,然后选择复制。8.右键单击报告,然后选择粘贴。9.将H2O蒸发率副本重命名为C2H6O2蒸发率。10.选择报告>C2H6O2蒸发率,然后将标量场函数设置为液膜蒸发率>C2H6O2液膜蒸发率。为两个报告创建监视器和绘图。11.选择H2O蒸发率和C2H6O2蒸发率节点,然后右键单击其中一项并选择从报告创建监视器和绘图。12.单击单个绘图,在同一张绘图中显示两个蒸发率。设置监视器,在每个时间步进行更新:13.选择监视器>C2H6O2蒸发率监视器和H2O蒸发率监视器节点,然后将触发器设为时间步。格式化监视器绘图外观:14.将绘图>报告绘图重命名为蒸发率。15.右键单击绘图>蒸发率节点,然后选择打开。16.选择绘图>蒸发率>轴>X轴>标题,然后将标题设为时间(s)。17.选择绘图>蒸发率>轴>Y轴>标题,然后将标题设为液膜蒸发率的表面平均值(kg/m^2-s)。设置标量场景设置标量场景,以显示液膜厚度。设置标量场景:创建液膜厚度的标量场景。1.右键单击场景节点,然后选择新建场景>标量。已根据场景节点创建新子节点标量场景1。2.将场景>标量场景1节点重命名为温度。3.单击场景/绘图。4.选择温度>显示器>标量1>零部件节点,然后单击零部件属性右侧的省略号。在显示的对话框中,展开区域>湿壁面壳节点,然后选择湿壁面。5.展开拉格朗日相节点,然后选择液滴,以可视化边缘液膜剥离。6.单击确定。7.选择温度>显示器>标量1>标量场节点,然后单击函数属性的右侧并将其设为温度。8.单击工具栏中的(保存-恢复-选择视图),然后选择恢复视图>视图1。9.保存模拟。运行模拟模拟准备现已结束,可以运行模拟。要运行模拟:1.单击求解工具栏中的(运行)。输出窗口中会显示求解进度。图形窗口中会自动创建残差屏幕,并在其中显示求解器的进度。模拟运行期间,您可单击图形窗口顶部的选项卡,查看场景和绘图。您可在运行期间,单击工具栏上的(停止)以停止进程。如果您停止模拟,可单击(运行)将其恢复。2.激活温度场景并观测壁面的变化和求解过程中的的液滴温度。上例显示了运行初期的温度。模拟继续,直至完成5秒的物理时间为止。当完成最后一次迭代时,输出窗口中会显示下列信息:已满足停止标准最大物理时间。3.当模拟完成运行时保存模拟。可视化结果检查模拟结果。最后一步的求解温度轮廓如下所示。1.激活蒸发率监视器绘图。收敛求解的蒸发率绘图如下所示。总结本教程介绍了如何使用STAR-CCM+的液膜建模功能来设置经过蒸发和边缘剥离的多成分液膜场景。本教程介绍了STAR-CCM+的下列功能:•定义液膜模型的模型。•设置液膜模型。•设置液膜-欧拉和液膜-拉格朗日相界面。•设置液膜蒸发和冷凝模型。•设置壳区域和边界条件。•设置边缘剥离模型•按设定的物理时间运行求解器。原资料见附件免责声明:本页面/内容部分素材来源于互联网公开信息,旨在传递更多信息,不代表本平台立场。版权归原作者或机构所有,如涉及侵权,请通过平台联系我们,我们将在核实后第一时间处理。本平台对转载内容的真实性、准确性不作任何保证,用户需自行判断并承担使用风险。

有附件
未登录
还没有评论
课程
培训
服务
行家
VIP会员 学习计划 福利任务
下载APP
联系我们
帮助与反馈