CERFACS CERFACS Scientific Activity Report Jan. 2010 â Dec. 2011

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ADVANCED METHODS AND MULTIPHYSICS expertise and with predefined mesh shapes choices following canonical CADs. Structured grids are suitable to maintain mesh lines aligned with the flow anisotropy, especially in the boundary layer where the variables are mainly varying in the direction normal to the wall. Moreover, structured numerical techniques help to improve the global performance and robustness of the structured approach. The structured mesh generation process must be performed by experts and the solution can be computed very accurately and quickly when the mesh lines are aligned with the flow. Nowadays, an industrial tendency is to increase the CAD complexity in order to compute flows with a higher accuracy. For planes, one can for instance consider thermal computation between engine and nacelle or flows around landing gears. For turbomachinery, passive wall treatments are added to increase the stability area of the propeller system and cannot be meshed easily. For those examples, the global structured mesh strategy fails at a reasonable human cost and it is clear that complex CAD must be computed with unstructured capabilities. The unstructured mesh generation process is very efficient but the prize to pay lays in the global accuracy of the computation : it is very difficult to impose mesh lines which are aligned with the flow and the accuracy can finally be lower than for structured grids. This point has been highlighted as a conclusion of the 4th Drag Prediction Workshop held in June 2009 : “More scatter from unstructured methods than from structured grid methods. Suspect this is more due to grid than to code.” A way to overcome this drawback is to authorize unstructured grids composed of different element shapes with hexahedra and prisms in the anisotropy flow region, tetrahedra elements where the flow is isotropic and pyramids in the buffer region from four-nodes element faces to three-nodes element faces. This is what is called a hybrid mesh approach in the literature but we prefer to call them mixed-elements grids. Compared with a structured grid, an unstructured equivalent induces a memory overhead due to connectivity and indirect memory addressing and since the global mesh structure is left away, it is difficult to implement efficient algorithms such as implicit schemes. An interesting approach seems finally to associate both techniques. Therefore, we consider meshes composed at the same time of structured blocks and unstructured mixed-elements zones : this is what we call a hybrid approach. This approach corresponds to the current Airbus needs (and strategy) who would like to benefit from the two worlds at the same time. In that context and in collaboration with ONERA, CERFACS actively participates to the development of unstructured capabilities in elsA. We are now able to simulate RANS three-dimensional unstructured grids on parallel platforms. Advanced validations on industrial configurations are on-going. The next step consists in handling hybrid grids. CERFACS is involved in the numerical treatment of interfaces between structured zones and unstructured ones. This is a consequence of the impossibility to implement identical numerical schemes for structured and unstructured zones. This activity can be related to coupling algorithms. 4.3.2 High Performance Computing (M. Montagnac) The emergence of large-scale HPC computing platforms based on multicore and manycore technologies, processor-based CPU and hybrid CPU / GPU, involves the modification of the legacy software to ensure optimal use of these capabilities in the future. CERFACS is involved in these two aspects. The first point is on hardware accelerators and especially GPU. elsA has been ported on GPU platforms after rewriting some parts of the code in CUDA-C. Results show an acceleration factor of about ten between one GPU card and one core of an Intel Westmere processor. Many GPU cards can be addressed for parallelism but more validations are required to make conclusions. The second point focuses on multicore hardware architecture. A simplified model of elsA has been developed in order to assess many different techniques that aim to maximize both the individual CPU performance and the shared-memory multicore performance. This effort will continue in the project SONATE+. 164 Jan. 2010 – Dec. 2011
COMPUTATIONAL FLUID DYNAMICS 4.3.3 Flow Simulation Data Manager (M. Montagnac) In an industrial context, experts in numerical calculations deliver computational frameworks to their endusers to design products. Obviously, these multidisciplinary frameworks include many third-party tools and legacy codes, which exchange data. Airbus France has developed a proprietary software architecture to enable code coupling and multi-physics simulations. Since these simulations often include an aerodynamics solver, elsA has been introduced as a python module in this framework. The purpose of the main module, called the Data Manager, is to contain all information about the simulation. Each code that has to be included in this framework requires a proxy that converts the data in the Data Manager to the native format of the code and conversely. Since the CERFACS CFD team sometimes uses the Airbus tools to conduct numerical simulations, a proxy module was developed and successfully applied to simple scenarios. The steering application is written in python : the data manager module contains in particular all meshes and initial solutions read from a database, the elsA proxy module makes all these data comprehensible by the elsA module (aerodynamic solver). 4.4 References [1] T. Arts, M. Lambert de Rouvroit, and A. W. Rutherford, (1990), Aero-thermal investigation of a highly loaded transonic linear turbine guide vane cascade, technical note 174, Von Karman Institute. [2] L. Casarsa, (2003), Aerodynamic performance investigation of a fixed rib-roughened internal cooling passage, PhD thesis, Universita degli Studi di Udine, Von Karman Institute for Fluid Dynamics. [3] Y. Collin, (2007), Simulation numérique de la distorsion générée par une entrée d’air de moteur civil par vent de travers, PhD thesis, ENSAE Toulouse. [4] M. Costes, (2008), Analysis of the second vorticity confinement scheme, Aerospace Science and Technology, 12, 203–213. [5] N. A. Cumpsty and F. E. Marble, (1977), The interaction of entropy fluctuations with turbine blade rows ; a mechanism of turbojet engine noise, Proc. R. Soc. Lond. A , 357, 323–344. [6] M. D. Domenico, P. Gerlinger, and M. Aigner, (2010), Development and validation of a new soot formation model for gas turbine combustor simulations, Combust. Flame , 157, 246–258. [7] J. I. Erdos, E. Alzner, and W. McNally, (1977), Numerical solution of periodic transonic flow through a fan stage, AIAA J., 15. [8] P. Février, O. Simonin, and K. Squires, (2005), Partitioning of Particle Velocities in Gas-Solid Turbulent Flows into a Continuous Field and a Spatially Uncorrelated Random Distribution : Theoretical Formalism and Numerical Study, J. Fluid Mech. , 533, 1–46. [9] J. Hardin, J. Ristorcelli, and C. Tam, (1994), In ICASE Workshop on Benchmark problems in Computational Aeroacoustics, Hampton, Virginia. [10] J. Y. Hwang and S. H. Chung, (2001), Growth of soot particles in counter ow di usion ames of ethylene, Combustion and Flame, 752–762. [11] M. Jacob, J. Boudet, D. Casalino, and M. Michard, (2005), A rod-airfoil experiment as benchmark for broadband noise modelling, J. Theoret. Comput. Fluid Dyn., 19, 171–196. [12] A. Kaufmann, M. Moreau, O. Simonin, and J. Hélie, (2008), Comparison between Lagrangian and mesoscopic Eulerian modelling approaches for inertial particles suspended in decaying isotropic turbulence, J. Comput. Phys. , 227, 6448–6472. CERFACS ACTIVITY REPORT 165
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CERFACS Scientific Activity Report
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Table des matières 1 Foreword xiii
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9 Publications 40 9.1 Books . . . .
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3 The OpenPALM coupler 104 3.1 Intr
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Table des figures CERFACS chart as
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TABLE DES FIGURES 6 Computational F
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Foreword Welcome to the 2010-2011 S
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CERFACS STRUCTURE CERFACS organizat
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CERFACS STAFF NAME POSITION PERIOD
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CERFACS STAFF SILVA GARZON Ph.D stu
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CERFACS STAFF NAME POSITION PERIOD
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CERFACS Wide-Interest Seminars Pete
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1 Introduction 1.1 Introduction The
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PARALLEL ALGORITHMS PROJECT success
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PARALLEL ALGORITHMS PROJECT Visitor
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3 List of Members of HiePACS HiePAC
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4 Dense and Sparse Matrix Computati
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PARALLEL ALGORITHMS PROJECT Working
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5 Iterative Methods and Preconditio
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PARALLEL ALGORITHMS PROJECT 5.5 Pre
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PARALLEL ALGORITHMS PROJECT We are
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PARALLEL ALGORITHMS PROJECT is to e
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PARALLEL ALGORITHMS PROJECT automor
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PARALLEL ALGORITHMS PROJECT 7.4 Sol
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PARALLEL ALGORITHMS PROJECT precond
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PARALLEL ALGORITHMS PROJECT 7.16 An
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PARALLEL ALGORITHMS PROJECT solutio
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8 Conferences and Seminars 8.1 Conf
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PARALLEL ALGORITHMS PROJECT Decembe
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PARALLEL ALGORITHMS PROJECT July WC
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PARALLEL ALGORITHMS PROJECT Septemb
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PARALLEL ALGORITHMS PROJECT [ALG15]
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PARALLEL ALGORITHMS PROJECT [ALG54]
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1 Overview presentation The main ex
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ELECTROMAGNETISM AND ACOUSTICS TEAM
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1 Introduction Data assimilation is
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DATA ASSIMILATION 2.3 Calibrating o
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3 Data assimilation for atmospheric
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DATA ASSIMILATION BECM using or not
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DATA ASSIMILATION the potential vor
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DATA ASSIMILATION A two years simul
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DATA ASSIMILATION FIG. 4.1: Reducti
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DATA ASSIMILATION FIG. 4.3: April 2
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DATA ASSIMILATION by a surface mode
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6 Data assimilation with EDF neutro
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DATA ASSIMILATION demonstrate that
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DATA ASSIMILATION [DA13] S. Massart
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4 Coupling tools and HPC Climate Mo
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2 The OASIS coupler The OASIS coupl
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THE OASIS COUPLER 2.3 OASIS4 Since
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3 The OpenPALM coupler 3.1 Introduc
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THE OPENPALM COUPLER In the NitroEu
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4 HPC Climate modelling 4.1 General
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5 Publications 5.1 Conference Proce
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CERFACS CERFACS Scientific Activity Report Jan. 2010 â Dec. 2011