Parallel Computational Fluid Dynamics 2001, Practice and Theory (eBook)

A. Ecer, P. Fox, Jacques Periaux, N. Satofuka, P. Wilders (Herausgeber)

eBook Download: PDF

2002 | 1. Auflage
416 Seiten
Elsevier Science (Verlag)
978-0-08-053841-9 (ISBN)

Infos

"ParCFD 2001, the thirteenth international conference on Parallel Computational Fluid Dynamics took place in Egmond aan Zee, the Netherlands, from May 21-23, 2001. The specialized, high-level ParCFD conferences are organized yearly on traveling locations all over the world. A strong back-up is given by the central organization located in the USA http://www.parcfd.org.

These proceedings of ParCFD 2001 represent 70% of the oral lectures presented at the meeting. All published papers were subjected to a refereeing process, which resulted in a uniformly high quality.

The papers cover not only the traditional areas of the ParCFD conferences, e.g. numerical schemes and algorithms, tools and environments, interdisciplinary topics, industrial applications, but, following local interests, also environmental and medical issues. These proceedings present an up-to-date overview of the state of the art in parallel computational fluid dynamics.
"

Inhalt

Front Cover 1
Parallel Computational Fluid Dynamics: Practice and Theory 4
Copyright Page 5
Table of Contents 10
Preface 6
Acknowledgements 8
Part 1: Opening paper: 14
Chapter 1. An overview of ParCFD activities at Delft University of Technology 16
Part 2: Invited and contributed papers: 34
Chapter 2. Noise predictions for shear layers 36
Chapter 3. Framework for parallel simulations in air pollution modeling with local refinements 44
Chapter 4. Aerodynamic studies on a Beowulf cluster 52
Chapter 5. Scalable numerical algorithms for efficient meta-computing of elliptic equations 60
Chapter 6. Direct numerical simulation of jet noise 68
Chapter 7. Migrating from a parallel single block to a parallel multiblock flow solver 76
Chapter 8. Parallel multidimensional residual distribution solver for turbulent flow simulations 84
Chapter 9. Parallel implementation of a line-implicit time-stepping algorithm 92
Chapter 10. Parallel simulation of dense gas and liquid flows based on the quasi gas dynamic system 100
Chapter 11. DLB 2.0 – A distributed environment tool for supporting balanced execution of multiple parallel jobs on networked computers 108
Chapter 12. Parallel computation of thrust reverser flows for subsonic transport aircraft 116
Chapter 13. On a fast parallel solver for reaction-diffusion problems: application to air quality simulation 124
Chapter 14. Algebraic coarse grid operators for domain decomposition based preconditioners 132
Chapter 15. Efficient parallel simulation of disperse gas-particle flows on cluster computers 140
Chapter 16. Large scale CFD data handling with off-the-shelf pc-clusters in a VR-based rhinological operation planning system 148
Chapter 17. An optimised recoupling strategy for the parallel computation of turbomachinery flows with domain decomposition 156
Chapter 18. Implementation of underexpanded jet problems on multiprocessor systems 164
Chapter 19. Numerical simulation of scramjet engine inlets on a vector-parallel supercomputer 172
Chapter 20. Parallel computation of multigrid method for overset grid 180
Chapter 21. Parallel computing of transonic cascade flows using the Lattice-Boltzmann method 188
Chapter 22. Parallel computation of multi-species flow using a Lattice-Boltzmann method 196
Chapter 23. A weakly overlapping parallel domain decomposition preconditioner for the finite element solution of convection-dominated problems in three dimensions 204
Chapter 24. Lattice-Boltzmann simulations of inter-phase momentum transfer in gas-solid flows 212
Chapter 25. Parallel CFD simulations of multiphase systems: jet into a cylindrical bath and rotary drum on a rectangular bath 220
Chapter 26. Zooming in on 3D magnetized plasmas with grid-adaptive simulations 228
Chapter 27. Parallel calculations for transport equations in a fast neutron reactor 236
Chapter 28. Parallel large scale computations for aerodynamic aircraft design with the German CFD system MEGAFLOW 240
Chapter 29. Towards stability analysis of three-dimensional ocean circulations on the TERAS 250
Chapter 30. Code parallelization effort of the flux module of the National Combustion Code 258
Chapter 31. Parallelization of a chaotic dynamical systems analysis procedure 266
Chapter 32. Performance optimization of GeoFEM fluid analysis code on various computer architectures 274
Chapter 33. Large scale CFD computations at CEA 280
Chapter 34. Parallel computation of gridless type solver for unsteady flow problems 288
Chapter 35. Clusters in the GRID: Power plants for CFD 298
Chapter 36. An efficient parallel algorithm for solving unsteady Euler equations 306
Chapter 37. Parallel Kalman filtering for a shallow water flow model 314
Chapter 38. A parallel solenoidal basis method for incompressible fluid flow problems 322
Chapter 39. A multilevel, parallel, domain decomposition, finite-difference Poisson solver 328
Chapter 40. Parallelization of a large scale Kalman filter: comparison between mode and domain decomposition 336
Chapter 41. A direct algorithm for the efficient solution of the Poisson equations arising in incompressible flow problems 344
Chapter 42. Current status of CFD platform-UPACS- 352
Chapter 43. A symmetry preserving discretization method, allowing coarser grids 360
Chapter 44. Multitime multigrid convergence acceleration for periodic problems with future applications to rotor simulations 368
Chapter 45. Direct numerical simulation of turbulence on a SGI Origin 3800 378
Chapter 46 .Parallel shallow water simulation for operational use 386
Chapter 47. Parallel deflated Krylov methods for incompressible flow 394
Chapter 48. Parallel CFD applications under DLB environment 402
Chapter 49. Parallel performance of a CFD code on SMP nodes 410

Textauszug

An Overview of ParCFD activities at Delft University of Technology

P. Wildersa,*[e-mail]; B.J. Boersmaa,†; J.J. Derksena,‡; A.W. Heeminka,§; B. Nicenoa,¶; M. Pourquiea,||; C. Vuika,**    a Delft University of Technology, J.M. Burgers Centre, Leeghwaterstraat 21, 2628 CJ Delft, The Netherlands,
* Dept. Applied Physics, Kramers Laboratorium
† Dept. Mechanical Engineering, Section Fluid Mechanics
‡ Dept. Applied Physics, Kramers Laboratorium
§ Dept. Applied Mathematical Analysis, Section Large Scale Systems
¶ Dept. Applied Physics, Section Thermofluids
|| Dept. Mechanical Engineering, Section Fluid Mechanics
** Dept. Applied Mathematical Analysis, Section Numerical Mathematics

At Delft University of Technology much research is done in the area of computational fluid dynamics with underlying models ranging from simple desktop-engineering models to advanced research-oriented models. The advanced models have the tendency to grow beyond the limit of single-processor computing. In the last few years research groups, studying such models, have extended their activities towards parallel computational fluid dynamics on distributed memory machines. We present several examples of this, including fundamental studies in the field of turbulence, LES modelling with industrial background and environmental studies for civil engineering purposes. Of course, a profound mathematical back-up helps to support the more engineering oriented studies and we will also treat some aspects regarding this point.

1 Introduction

We present an overview of research, carried out at Delft University of Technology and involving parallel computational fluid dynamics. The overview will not present all activities carried out in this field at our University. We have chosen to present work of those groups, that are or have been active at the yearly ParCFD conferences, which indicates that these groups focus to some extent on purely parallel issues as well. This strategy for selecting contributing groups enabled the main author to work quite directly without extensive communication overhead and results in an overview presenting approximately 70% of the activities at our University in this field. We apologize on forehand if we have overseen major contributions from other groups.

At Delft University of Technology parallel computational fluid dynamics is an ongoing research activity within several research groups. Typically, this research is set up and hosted within departments. For this purpose they use centrally supported facilities, most often only operational facilities. In rare cases, central support is given as well for developing purposes. Central support is provided by HPαC, http://www.hpac.tudelft.nl/, an institution for high performance computing splitted off from the general computing center in 1996. Their main platform is a Cray T3E with 128 DEC-Alpha processors, installed in 1997 and upgraded in 1999.

From the paralllel point of view most of the work is based upon explicit parallel programming using message passing interfaces. The usage of high-level parallel supporting tools is not very common at our university. Only time accurate codes are studied with time stepping procedures ranging from fully explicit to fully implicit. Typically, the explicit codes show a good parallel performance, are favorite in engineering applications and have been correlated with measurements using fine-grid 3D computations with millions of grid points. The more implicit oriented codes are still in the stage of development, can be classified as research-oriented codes using specialized computational linear algebra for medium size grids and show a reasonable parallel performance.

The physical background of the parallel CFD codes is related to the individual research themes. Traditionally, Delft University of Technology is most active in the incompressible or low-speed compressible flow regions. Typically, Delft University is also active in the field of civil engineering, including environmental questions. The present overview reflects both specialisms.

Of course, studying turbulence is an important issue. Direct numerical simulation (DNS) and large eddy simulation (LES) are used, based upon higher order difference methods or Lattice- Boltzmann methods, both to study fundamental questions as well as applied questions, such as mixing properties or sound generation. Parallel distributed computing enables to resolve the smallest turbulent scales with moderate turn-around times. In particular, the DNS codes are real number crunchers with excessive requirements.

A major task in many CFD codes is to solve large linear systems efficiently on parallel platforms. As an example, we mention the pressure correction equation in a non-Cartesian incompressible code. In Delft, Krylov subspace methods combined with domain decomposition are among the most popular methods for solving large linear systems. Besides applying these methods in our implicit codes, separate mathematical model studies are undertaken as well with the objective to improve robustness, convergence speed and parallel performance.

At the level of civil engineering, contaminant transport forms a source of inspiration. Both atmospheric transport as well as transport in surface and subsurface regions is studied. In the latter case the number of contaminants is in general low and there is a need to increase the geometrical flexibility and spatial resolution of the models. For this purpose parallel transport solvers based upon domain decomposition are studied. In the atmospheric transport models the number of contaminants is high and the grids are regular and of medium size. However, in this case a striking feature is the large uncertainty. One way to deal with this latter aspect is to explore the numerous measurements for improvement of the predictions. For this purpose parallel Kalman filtering techniques are used in combination with parallel transport solvers.

We will present various details encountered in the separate studies and discuss the role of parallel computing, quoting some typical parallel aspects and results. The emphasis will be more on showing where parallel CFD is used for and how this is done than on discussing parallel CFD as a research object on its own.

2 Turbulence

Turbulence research forms a major source of inspiration for parallel computing. Of all activities taking place at Delft University we want to mention two, both in the field of incompressible flow.

A research oriented code has been developed in [12], [13]. Both DNS and LES methods are investigated and compared. The code explores staggered second-order finite differencing on Cartesian grids and the pressure correction method with an explicit Adams-Bathford or Runge- Kutta method for time stepping. The pressure Poisson equation is solved directly using the Fast Fourier transform in two spatial directions, leaving a tridiagonal system in the third spatial direction. The parallel MPI-based implementation relies upon the usual ghost-cell type communication, enabling the computation of fluxes, etc., as well as upon a more global communication operation, supporting the Poisson solver. For a parallel implementation of the Fast Fourier transform it suffices to distribute the frequencies over the processors. However, when doing a transform along a grid line all data associated with this line must be present on the processor. This means that switching to the second spatial direction introduces the necessity of a global exchange of data. Of course, the final tridiagonal system is parallelized by distributing the lines in the associated spatial direction over the processors. Despite the need of global communication, the communication overhead remains in general below 10%. Figure 1 gives an example of the measured wall clock time. The speed-up is nearly linear.

Figure 1 Wall clock time for 643 model problem

In figure 2 a grid type configuration at inflow generates a number of turbulent jet flows in a channel (modelling wind tunnel turbulence). Due to the intensive interaction and mixing, the distribution of turbulence becomes very quickly homogeneous in the lateral direction. A way to access the numerical results, see figure 3, is to compute the Kolmogorov length scales (involving sensitive derivatives of flow quantities). The grid size is 600 × 48 × 48 (1.5 million points), which is reported to be sufficient to resolve all scales in the mixing region with DNS for R = 1000. For R = 4000 subgrid LES modelling is needed: measured subgrid contributions are of the order of 10 %.

Figure 2 Contour plot of the instantaneous velocity for a flow behind a grid.

Figure 3 Kolmogorov scale.

A second example of turbulence modelling can be found in [11]. In this study the goals are directed towards industrial applications with complex geometries using LES. Unstructured co-located second-order finite volumes, slightly stabilized, are used in combination with the pressure correction method and implicit time stepping. Solving the linear systems is done with diagonally preconditioned Krylov methods, i.e. CGS for...

Produkt-Details

• Erscheint lt. Verlag: 17.4.2002
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Algebra
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Strömungsmechanik
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Maschinenbau
• ISBN-10: 0-08-053841-X / 008053841X
• ISBN-13: 978-0-08-053841-9 / 9780080538419