Shape optimization with computational fluid dynamics

In many product design and development applications, computational fluid dynamics CFD has become a useful analytical simulation tool. CFD simulations are quite useful in predicting several response parameters for a given design condition. However, like any analysis tool CFD simulations provide limited insight into the design space and the changes needed to find the optimum design parameters.This paper deals with the shape optimization of fluid flows using CFD and numerical optimization techniques. By integrating a commercial optimization code with a CFD code, a CFD shape optimization tool was developed. To study the effectiveness of the developed tool and its ability to produce results with reasonable CPU time, the shape optimization of an airfoil and S-shaped duct are studied with different numbers of design variables. The developed shape optimization tool along with the optimization and CPU time results are discussed.     

Automated domain decomposition for computational fluid dynamics

Automation of flow-field zoning in two-dimensions is an important step towards easing the three-dimensional grid generation bottleneck in computational fluid dynamics. A knowledge-based approach works well, but several aspects of flow-field zoning make the use of such an approach challenging. A proposed model and language to describe the process of zoning a flow field are presented, followed by a discussion of the implementation of EZGrid, a knowledge-based two-dimensional (2-D) flow-field zoner. Results are shown for representative 2-D aerodynamic configurations. Finally, an approach to the evaluation of flow-field zonings is described and used to compare the performance of EZGrid with that of a human expert.     

Numerical algorithms in computational fluid dynamics on vector computers

Modern vector computers tend to favour certain classes of algorithms (e.g. explicit, Jacobi-type) while other important algorithms, such as implicit ones or Monte Carlo, in their serial versions are not very suitable for these machines. However, restructuring of serial algorithms often enables the user to exploit fully the potential of vector machines, which will often result in remarkable performance improvements. In the following contribution, the vectorization of five well-known algorithms, the explicit-implicit MacCormack scheme, the implicit scheme of Beam and Warming, a boundary-layer algorithm, a Galerkin procedure and a Monte Carlo simulation, for the solution of problems in computational fluid dynamics is discussed and computation times are given.     

Asymptotic behavior and best approximation in computational fluid dynamics

Computational fluid dynamics has many successes for the solution of simple standard problems. For relatively complex problems, especially if nonlinear and of mixed type, the computed approximate solutions are mostly of dubious accuracy and credibility. The difficulty appears fundamental. Model studies in one space dimension suggest that most of such discrete problems are poorly posed. The sequence of computed solutions at successively refined meshes need not converge; and apparently “smooth” computed approximations can “converge” to wrong limits with large global errors. For certain discrete formulations the sequence is asymptotic in the sense of displaying minimum error at some fairly large critical mesh Reynolds number (coarse meshes). This error minimum can be as small as those promised by the correct “convergent approximations” at much smaller meshes. Certain behavior of the computed solutions around such a critical mesh Reynolds number help to identify the “best approximation”. Such analytic inferences have been tested and verified in the computational solutions of successively more complex flows governed by Navier-Stokes equations in two space dimensions. The flow fields due to shockwave-laminar-boundary layer interaction were computed with different discrete formulations and various perturbations. The computed “best approximations” differ little and all compare favorably with available experimental data. Some of such formulations give the “best approximations” at reasonably coarse meshes, requiring much smaller computational effort; and should therefore be favorably considered.     

Visualization's role in analyzing computational fluid dynamics data

Visualization has fueled the growth and understanding of many scientific and engineering fields. In computational fluid dynamics, for example, engineers now use numerical calculations to accurately simulate many engineering problems that once required the use of physical experiments involving wind and water tunnels. CFD has come to serve as an instrument in the design of many familiar engineering processes. These developments have produced massive amounts of data for analysis. Feature detection has become a critical tool in understanding key structures within these volumes of data. We briefly discuss the growth of CFD and investigate why feature detection and visualization have become such a vital part of analyzing CFD simulation results.     

计算流体力学在烟气脱硫中的应用

为优化脱硫吸收塔内流场分布,提高脱硫效率,降低脱硫投资和运行成本,提出基于RANS方程和多相流模型的烟气脱硫数值模拟方法,基于FLUENT采用多相流模型针对吸收塔内烟气脱硫过程进行模拟.通过对吸收塔烟气和浆液区域进行建模及网格划分,并对内部和喷枪局部处流场进行数值模拟,分析得到浆液和烟气在吸收塔内的流动规律.模拟结果验...     

Complementing computational fluid dynamics methods with classical analytical techniques

New aerospace vehicle designs must have greater performance and versatility at affordable cost. This requires multi-disciplinary analysis and optimization which in turn requires more accurate and efficient numerical simulation tools. The need for greater accuracy and efficiency of computational fluid dynamics (CFD) tools is further amplified by the industry trend toward distributed computing (e.g. workstation clusters) and away from supercomputers. Complementary analytic methods coupled with traditional CFD approaches offer the means for increased simulation capability by incorporating more essential physics into solution algorithms and reducing reliance on grid density for achieving accuracy. McDonnell Douglas Aerospace has a focused activity directed at improving affordability of CFD tools with complementary analytic techniques and has developed a strong capability. Results have proven very successful. Several examples of ongoing work are discussed, including improved far-field boundary conditions for CFD codes and analytic-based aerodynamic analysis and design optimization methods.     

计算流体力学在化学反应器模拟中的应用进展

计算流体力学(CFD)能够准确地描述流体流动、混合、传热规律,近年来逐渐开始耦合到化学反应中应用于化学工程领域,并表现出巨大潜力。本文综述了CFD在不同化学反应器中针对不同反应体系模拟的基本原理以及应用。相比于传统的面向理想反应器的反应动力学模拟和单纯面向流动传递的CFD模拟方法而言,采用CFD耦合化学反应动力学的方法同时考虑了传递过程和反应过程,能够对非理想化学反应器的操作特性(转化率、选择性、分子量及其分布等)进行模拟、分析与预测,在化工过程强化和化工产品控制方面优势明显。开发新的耦合数学模型和数值算法、考虑亚格子尺度的微观过程和采用直接数值模拟等方法,将是利用CFD深入研究非理想反应器特性的重要方向。     

非结构有限体积CFD计算的网格重排序优化

网格重排序是提升流体力学CPU和GPU并行计算效率的重要手段之一。对于非结构网格,由于其数据存储无规律,数据的间接访问会导致访存延迟,尤其是在GPU并行计算时,数据的间接访问将引起内存的非对齐访问,放大了访存延迟的影响。对此,采用Reverse Cuthill-Mckee网格重排序方法优化了非结构网格的数据局部性,并设计了一种面向编号重排序方法。算例测试表明,网格重排序不影响最终计算结果。对比分析了网格重排序对非结构求解器在CPU和GPU上的性能影响：对CPU计算,可以使部分热点函数运行时间降低约20%,整体运行时间降低15%~20%;对GPU计算,大部分热点函数运行时间可降低35%~60%,程序整体运行时间降低约40%。     

A decade of progress on anisotropic mesh adaptation for computational fluid dynamics

In the context of scientific computing, the mesh is used as a discrete support for the considered numerical methods. As a consequence, the mesh greatly impacts the efficiency, the stability and the accuracy of numerical methods. The goal of anisotropic mesh adaptation is to generate a mesh which fits the application and the numerical scheme in order to achieve the best possible solution. It is thus an active field of research which is progressing continuously. This review article proposes a synthesis of the research activity of the INRIA Gamma3 team in the field of anisotropic mesh adaptation applied to inviscid flows in computational fluid dynamics since 2000. It shows the evolution of the theoretical and numerical results during this period. Finally, challenges for the next decade are discussed.     

An efficient parallel algorithm with application to computational fluid dynamics

When solving time-dependent partial differential equations on parallel computers using the nonoverlapping domain decomposition method, one often needs numerical boundary conditions on the boundaries between subdomains. These numerical boundary conditions can significantly affect the stability and accuracy of the final algorithm.In this paper, a stability and accuracy analysis of the existing methods for generating numerical boundary conditions will be presented, and a new approach based on explicit predictors and implicit correctors will be used to solve convection-diffusion equations on parallel computers, with application to aerospace engineering for the solution of Euler equations in computational fluid dynamics simulations. Both theoretical analyses and numerical results demonstrate significant improvement in stability and accuracy by using the new approach.     

The use of computational fluid dynamics in TsAGI experimental works

The paper describes methods to calculate the flow around an aircraft model in a wind tunnel. It formulates special boundary conditions to achieve the necessary flow parameters in the control section, as well as to simulate some features of wind tunnel walls, for example, perforation. In order to accelerate the numerical solution convergence, a method for implicit smoothing is suggested allowing the calculation duration to be reduced several times. The cases of practical use of this methodology are given. It is shown that in the conditions of the TsAGI subsonic wind tunnel, it is possible to simulate the effect on the model from the elements of the structure that are missing in this tunnel, for example, the running track. A mathematical model of the European Transonic Windtunnel (ETW) with slotted walls is presented. It is shown that the flow in the reentry affects the main flow in the test section of this tunnel. Data on the effect of the model support in the TsAGI wind tunnel T-128 are given. The peniche height used in the half-model tests has been justified. The conclusion is made that the mathematical model of the wind tunnel is an obligatory part in experimental studies.     

Feature-based intelligent system for steam simulation using computational fluid dynamics

In the development of products involving fluids, computational fluid dynamics (CFD) has been increasingly applied to investigate the flow associated with various product operating conditions or product designs. The batch simulation is usually conducted when CFD is heavily used, which is not able to respond to the changes in flow regime when the fluid domain changes. In order to overcome this defect, a rule-based intelligent CFD simulation system for steam simulation is proposed to analyze the specific product design and generate the corresponding robust simulation model with accurate results. The rules used in the system are based on physical knowledge and CFD best practices which make this system easy to be applied in other application scenarios by changing the relevant knowledge base. Fluid physics features and dynamic physics features are used to model the intelligent functions of the system. Incorporating CAE boundary features, the CFD analysis view is fulfilled, which maintains the information consistency in a multi-view feature modeling environment. The prototype software tool is developed by Python 3 with separated logics and settings. The effectiveness of the proposed system is proven by the case study of a disk-type gate valve and a pipe reducer in a piping system.     

Vorton dynamics: a case study of developing a fluid dynamics model for a vector processor

The raw performance of vector processors such as the CDC CYBER-205 has been well documented. The ability to apply this raw power to ever more complex algebraic algorithms has been reported in [9]. The final step in making computers of this class truly the revolutionary tools they are claimed to be is to develop whole applications that perform at a significant fraction of the raw power. This involves two distinct subclasses of problems. On the one hand, there are those pre-existing applications that must be mapped onto vector processors in such a way that not only is performance maintained, but also a (sometimes vague) set of computational boundary conditions of the user community is satisfied. On the other hand, there are those models which are developed ab initio with machines such as the CYBER-205 in mind. The development of solutions to problems in the former class involves psychology and politics as well as mathematics and computer science. We limit ourselves here to reporting on an example of the latter class, viz. a model to study a particular fluid-dynamic phenomenon, that was specifically designed with the CYBER-205 in mind.     

Recent progress and challenges in exploiting graphics processors in computational fluid dynamics

The progress made in accelerating simulations of fluid flow using GPUs, and the challenges that remain, are surveyed. The review first provides an introduction to GPU computing and programming, and discusses various considerations for improved performance. Case studies comparing the performance of CPU- and GPU-based solvers for the Laplace and incompressible Navier–Stokes equations are performed in order to demonstrate the potential improvement even with simple codes. Recent efforts to accelerate CFD simulations using GPUs are reviewed for laminar, turbulent, and reactive flow solvers. Also, GPU implementations of the lattice Boltzmann method are reviewed. Finally, recommendations for implementing CFD codes on GPUs are given and remaining challenges are discussed, such as the need to develop new strategies and redesign algorithms to enable GPU acceleration.