Parallel Computational Fluid Dynamics 2002 (eBook)

New Frontiers and Multi-Disciplinary Applications

A. Ecer, P Fox, K. Matsuno, Jacques Periaux, N. Satofuka (Autoren)

eBook Download: EPUB

2003 | 1. Auflage
620 Seiten
Elsevier Science (Verlag)
978-0-08-053842-6 (ISBN)

Infos

This volume is proceedings of the international conference of the Parallel Computational Fluid Dynamics 2002. In the volume, up-to-date information about numerical simulations of flows using parallel computers is given by leading researchers in this field. Special topics are ",Grid Computing", and ",Earth Simulator",. Grid computing is now the most exciting topic in computer science. An invited paper on grid computing is presented in the volume. The Earth-Simulator is now the fastest computer in the world. Papers on flow-simulations using the Earth-Simulator are also included, as well as a thirty-two page special tutorial article on numerical optimization.

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This volume is proceedings of the international conference of the Parallel Computational Fluid Dynamics 2002. In the volume, up-to-date information about numerical simulations of flows using parallel computers is given by leading researchers in this field. Special topics are "e;Grid Computing"e; and "e;Earth Simulator"e;. Grid computing is now the most exciting topic in computer science. An invited paper on grid computing is presented in the volume. The Earth-Simulator is now the fastest computer in the world. Papers on flow-simulations using the Earth-Simulator are also included, as well as a thirty-two page special tutorial article on numerical optimization.

Inhalt

Front Cover 1
Parallel Computational Fluid Dynamics: New Frontiers and Multi-Disciplinary Applications 4
Copyright Page 5
Table of Contents 10
Preface 6
Acknowledgements 8
Part 1: Invited Papers 18
Chapter 1. Lattice Boltzmann Methods: High Performance Computing and Engineering Applications 20
Chapter 2. Multi-Disciplinary Simulations and Computational and Data Grids 30
Chapter 3. A Different Approach to Large-Eddy Simulation with Advantages for Computing Turbulence-Chemical Kinetics Interactions 40
Chapter 4. Simulation of Combustion Dynamics in Gas Turbine Engines 50
Chapter 5. A Substepping Navier-Stokes Splitting Scheme for Spectral/hp Element Discretisations 60
Part 2: Earth and Space Global Simulations and Earth Simulator 70
Chapter 6. An MHD Model for Heliospheric Studies 72
Chapter 7. Computational Performance of the Dynamical Part of a Next Generation Climate Model Using an Icosahedral Grid on the Earth Simulator 80
Chapter 8. Optimization of a MHD Dynamo Simulation Code Using the GeoFEM for the Earth Simulator 88
Chapter 9. Performance of Atmospheric General Circulation Model Using the Spectral Transform Method on the Earth Simulator 96
Chapter 10. Improving Computational Efficiency of 4D-VAR System for Global Ocean Circulation Study 104
Chapter 11. Coupling Strategy of Atmospheric-Oceanic General Circulation Model with Ultra High Resolution and its Performance on the Earth Simulator 110
Chapter 12. Parallel Architecture and its Performance of Oceanic Global Circulation Model Based on MOM3 to Be Run on the Earth Simulator 118
Chapter 13. Design and Performance Analysis of an Ocean General Circulation Model Optimized for the Earth Simulator 126
Chapter 14. Development of a Nonhydrostatic General Circulation Model Using an Icosahedral Grid 132
Chapter 15. Zonally Implicit Scheme for a Global Ocean Model 140
Chapter 16. Successful Achievement in Developing the Earth Simulator 148
Part 3: Parallel Environments 156
Chapter 17. Simulation of Combustion Problems Using Multiprocessor Computer Systems 158
Chapter 18. Autonomic System for Dynamic Load Balancing of Parallel CFD 166
Chapter 19. High-Speed Mass Storage System of Numerical Simulator III and it's Basic I/O Performance Benchmark 174
Chapter 20. Construction of a Large Scale PC-Cluster Machine and its Application to Combusting Flow Analysis in Chemical Furnace 182
Chapter 21. Aerodynamic Computation of a Scramjet Engine on Vector-Parallel Supercomputers 190
Chapter 22. A New Approach to Scientific Computing with JavaSpace 198
Chapter 23. Numerical Simulator III – Building a Terascale Distributed Parallel Computing Environment for Aerospace Science and Engineering 204
Chapter 24. Blood Flow Simulation in a Grid Environment 212
Chapter 25. Parallelization Methods for Three-Dimensional Fluid Code Using High Performance Fortran 220
Chapter 26. The Development Strategy of Super-Computer Calculations in Russia. 228
Chapter 27. Reaching Equilibrium for Non-Cooperative Dynamic Load Balancing Applications 232
Chapter 28. The Parallel Flux Module of the National Combustion Code on Dynamic Load Balancing Environment 240
Part 4: Parallel Algorithms 248
Chapter 29. Numerical Simulation of Reaction-Diffusion and Adsorption Processes in Porous Media Using Lattice Boltzmann Methods with Concurrent Visualisation 250
Chapter 30. A Parallel Multilevel Finite Element Solver for Axial Compressor CFD 258
Chapter 31. Fully Coupled Solver for Incompressible Navier-Stokes Equations Using a Domain Decomposition Method 266
Chapter 32. Parallel Direct Simulation Monte Carlo and its Application to Flows in Micro Channels 274
Chapter 33. Simulation of a 3-D Lid-Driven Cavity Flow by a Parallelised Lattice Boltzmann Method 282
Chapter 34. A Parallel Implementation of an Implicit Scheme for Underexpanded Jet Problems 290
Chapter 35. Parallel Implicit Solution of 3-D Navier-Stokes Equations 298
Chapter 36. On the Parallelization of the Lattice Boltzmann Method for Turbomachinery Applications 306
Chapter 37. Parallel Numerical Method for Compressible Flow Calculations of Hovering Rotor Flowfields 314
Chapter 38. Parallel Finite Element Method for Orographic Wind Flow and Rainfall 322
Chapter 39. Parallel Numerical Method for Incompressible Navier-Stokes Equations 330
Chapter 40. Parallel Efficiency of a Variable Order Method of Lines 338
Chapter 41. Large Eddy Simulation of a Lobed Mixer Nozzle Using a Multi-Block Method 346
Chapter 42. Parallel FEM Based on Level Set Method for Free-Surface Flow Using PC Cluster 354
Chapter 43. Parallel Algorithms Based on Two-Dimensional Splitting Schemes for Multidimensional Parabolic Equations 362
Chapter 44. Applicability of QSI Scheme to Advection-Diffusion Equations with Domain Decomposition Method 370
Chapter 45. A Finite-Element Based Navier-Stokes Solver for LES 378
Part 5: Mesh Strategies 386
Chapter 46. Parallel Adaptivity for Solution of Euler Equations Using Unstructured Meshes 388
Chapter 47. Parallel Approach of Fully Systemized Chimera Methodology for Steady/Unsteady Problems 396
Chapter 48. Evaluation of Parallelized Unstructured-Grid CFD for Aircraft Applications 404
Chapter 49. A Parallel Method for Adaptive Refinement of a Cartesian Mesh Solver 412
Chapter 50. Parallel Computation of Vortex-Induced Vibration of a Circular Cylinder Using Overset Grid 420
Chapter 51. Dynamical Computing Power Balancing for Adaptive Mesh Refinement Applications 428
Chapter 52. Study of Parallelization Enhancements for Cartesian Grid Solver 436
Chapter 53. Parallel Computation of Higher Order Gridless Type Solver 444
Chapter 54. Building-Cube Method Designed for Large Scale Flow Computations on Parallel Computers 452
Chapter 55. Parallelization of an Adaptive Cartesian Mesh Flow Solver Based on the 2N-tree Data Structure 458
Part 6: Multi-Disciplinary Simulations 466
Chapter 56. Biofluid Simulations on Linux Clusters 468
Chapter 57. Numerics in BoRiS 476
Chapter 58. CEF Model in the Industrial Application of Non-Newtonian Fluids 484
Chapter 59. Exhaust Manifold Design for a Car Engine Based on Engine Cycle Simulation 492
Chapter 60. Parallel Implementation of the Solution of the Nonlinear Schrodinger Equation 500
Chapter 61. Distributed-Memory Parallelization of Radiative Transfer Calculation in Hypersonic Flow 508
Chapter 62. Mass Simulations Based Design Approach and its Environment 516
Chapter 63. Parallel High Accuracy CFD Code for Complete Aircraft Viscous Flow Simulations 524
Chapter 64. Multiobjective GA for SST Wing-Body Shape Design 532
Chapter 65. Parallel Computation of Flows Around Flapping Airfoils in Biplane Configuration 540
Chapter 66. Numerical Prediction for Transportation of Non-Uniform Particles Accumulated under Oscillating Turbulent Flows 548
Chapter 67. Parallel Implementation of the Solver for the One-Dimensional Vlasov-Poisson Equation Based on the DA-CIP Method 556
Chapter 68. Large Eddy Simulation of Rotor-Stator Cavity Flow 564
Chapter 69. Capability of UPACS in Various Numerical Simulations 572
Chapter 70. Parallelization of a Genetic Algorithm for Curve Fitting Chaotic Dynamical Systems 580
Part 7: Tutorial Paper 588
Chapter 71. Parallel Evolutionary Computation for Solving Complex CFD Optimization Problems: A Review and Some Nozzle Applications 590

Textauszug

Lattice Boltzmann Methods: High Performance Computing and Engineering Applications

G. Brennera; Th. Zeisera; K. Beronova; P. Lammersa; J. Bernsdorfb    a Institute of Fluid Mechanics, University of Erłangen-Nuremberg, Cauerstraße 4, 91058 Erlangen, Germany
b C&C Research Laboratories, Rathausallee 10, 53757 Sankt Augustin, Germany

The development of novel numerical methods for applications in computational fluid dynamics has made rapid progress in recent years. These new techniques include the lattice gas and lattice Boltzmann methods. Compared to the traditional CFD methods, the lattice Boltzmann methods are based on a more rigorous physical modelling, the Boltzmann equation. This allows to circumvent many deficiencies inherent in existing Navier-Stokes based approaches. Thus, the lattice Boltzmann methods have attracted a lot of attention in the fluid dynamics community and emerged as an attractive alternative in many application areas. In the present paper, we discuss some perspectives of the lattice Boltzmann methods, in particular for industrial applications and present some successful examples from projects related to aerodynamics, chemical and process engineering.

1 Introduction

In the past years, the methods of lattice gas cellular automata (LGCA) and the lattice Boltzmann methods (LBM) have attained a certain maturity and subsequently challenged the traditional methods of computational fluids dynamics (CFD) in many areas. In that context, traditional methods of CFD are understood to include all numerical schemes, that aim to solve the Navier-Stokes equations by some direct discretisation. In contrast to that, the LBM is based on a more rigorous description of the transport phenomena, the Boltzmann equation. Compared to other attempts, that have been made to solve this equation in the past, the LBM makes use of several significant, physically motivated simplifications that allow to construct efficient and competitive or even superior computational codes as compared to the classical approaches.

Lattice gas cellular automata and even more lattice Boltzmann methods are relatively new. Just about 15 years ago, the field of LGCA started almost out of the blue with the now famous paper of Frisch, Hasslacher and Pomeau [1], who showed that some simplified kind of ”billiard game” representing the propagation and collision of fluid particles leads to the Navier-Stokes equations in a suitable macroscopic limit. In particular, the authors showed how the propagation and collisions of particles have to be abstracted in order to conserve mass and momentum and how the underlaying lattice has to be designed in order t,o provide sufficient symmetries to obtain Navier-Stokes like behaviour. Each month, several papers appear to present new models or to investigate existing models, to demonstrate and assess the use of LBM in application fields or to evaluate high performance computing (HPC) aspects. Summerschools, special conferences and LBM sessions in existing conferences have been organised to satisfy also the growing interest of developers and potential users in this technique. Besides that, commercial products are available with remarkable success (see e. g [2])

The goal of the present paper is to show the potential of the lattice Boltzmann method in CFD and in related areas. Besides the classical application fields, such as aerodynamics, these are in particular problems related to chemical and process engineering. Due to the complexity of the relevant transport and chemical conversion mechanisms, that have to be modelled, these areas open new challenges also for the LBM.

In the present paper, after a short summary of the basic principles of the LBM, examples related to turbulent flows, reacting flows and the respective application fields are discussed.

2 Lattice Gas and Boltzmann Method

From a gaskinetical, i.e. microscopic, point of view, the movement of a fluid may be considered as the propagation and collision of molecular particles governed by fundamental laws of physics. The modelling of this motion may be carried out on several levels, starting with the Hamilton equation of a set of discrete particles. Since this approach prohibits itself because of the large number of freedoms to be considered, several attempts have been made to simplify this picture by extracting only the essential criteria required to model e. g. the motion of a Newtonian fluid. In that context, the lattice gas automata may be seen as an abstraction of the fluid making use of the fact, that the statistics of the gas may be correctly described by a significantly reduced number of molecules and by applying simplyfied dynamics of the particles. This can be explained by the fact, that the conservation principles as well as associated symmetries are the basic building blocks for the continuum equations of fluids. Thus, in oder to simulate a continuum flow, the approximation of the computer gas has to recover only these principles to a certain extend. The FHP automata, named after [1], was a first successful attempt to construct a discrete model to compute the motion of a Newtonain fluid. Although this discrete particle approach seems promising, there are problems due to spurious invariants and random noise in the solutions. These deficiencies can be overcome by applying the idea of McNamara and Zanetti [3], who considered the discrete Boltzmann equation as a base for the numerical algorithm. This approach may be briefly explained as follows: The Boltzmann equation is an integro-đifïerential equation for the single particle distribution function tx→υ→, which describes the propability to find a particle in a volume →,x→+dx→ and with a velocity in the range. →,υ→+dυ→ Neglecting body forces one has:

tf+υ→∇f=Qf

  (1)

A suitable simplification of the complicated collision integral Q(f) is the BGK approximation,

f≈1τfeq−f

  (2)

which preserves the lower moments an satisfies an H-Theorem like the original equations 1. Here feq is the Maxwell equilibrium distribution. The discretisation of this equation requires a finite representation of the distribution function in the velocity space. One way to realise this is to introduce a finite set of velocities →i and associated distribution functions itx→υ→i, which are governed by the discrete velocity Boltzmann (BGK) equation:

tfi+υ→i∇fi=1τfieq−fi

  (3)

Next, the discretisation in space and time is accomplished by an explicit finite difference approximation. With a scaling of the lattice spacing, the time step and the discrete velocities according to →i=Δx→i/Δt, the discretised equation takes the following form:

ix+υ→iΔt,t+Δt−fix,t=1τfieq−fix,t.

  (4)

The discrete values of the equilibrium functions are chosen of Maxwell type, in the sense that their velocity moments up to fourth order are identical with the velocity moments over the Maxwell distribution. The following definition satisfies this requirement:

ieq=tpρ1+υiαuαcs2+uαuβ2cs2υiαυiβcs2−δαβ.

  (5)

The discrete equilibrium functions may be computed efficiently at each time step for each node, from the components of the local macroscopic flow velocity uα, the fluid density ρ, the ”speed of sound cs” and a direction-dependent lattice geometry weighting factor tp. The viscosity ν of the simulated fluid can be controlled by the relaxation time τ, according to

=13τ−12

  (6)

Further technical details of this method, in particular concerning the formulation of boundary conditions, may be found in [4–6].

From the computational point of view the above approach is interesting as it resembles a simple finite difference scheme applied to a first oder (in time and space) hyperbolic system of equations in diagonal form. This extremely simplifies the design of a numerical scheme. However, finally the solution of the Navier-Stokes equations with second order accuracy in the limit of low Mach numbers s2>>υ→2 is recovered, as can be shown rigorously in [7] by applying the Chapman-Enskog procedure to eq. (4).

The approach presented above leads to the basic version of LBM. Many improvements have been designed in order to broaden the methods range of applicability, see e.g. the review article of Chen and Doolen [8]. Current issues are the multi-time relaxation methods to enhance the stability of the method [9,10], implicit methods [11] or the application of nonuniform and...

Produkt-Details

• Erscheint lt. Verlag: 25.4.2003
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Theorie / Studium
• Themenwelt: Mathematik / Informatik ► Mathematik ► Algebra
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Naturwissenschaften ► Physik / Astronomie ► Strömungsmechanik
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Maschinenbau
• ISBN-10: 0-08-053842-8 / 0080538428
• ISBN-13: 978-0-08-053842-6 / 9780080538426