Étude comparative des plateformes parallèles pour systèmes multi-agents

International audienceLa simulation est devenue un outil indispensable à la recherche pour explorer les systèmes sans avoir recours à l'expérience. En fonction des caractéristiques du système la méthode de modélisation utilisée pour représenter le système varie. Les systèmes multi-agents sont ainsi souvent utilisés pour modéliser et simuler les systèmes complexes. Quel que soit le type de modélisation utilisée, l'augmentation de la taille et de la précision du modèle fait croître le nombre des calculs, rendant nécessaire l'utilisation de systèmes parallèles. Dans cet article, nous nous intéressons aux plateformes de simulation multi-agent parallèles. Notre contribution est une étude comparative de ces différentes plateformes, dans un contexte de calcul intensif. Nous présentons une analyse qualitative, à partir de critères que nous avons définis, puis un comparatif de performance, sur la base d'un modèle agent que nous avons implémenté sur chaque plateforme

A co-located partitions strategy for parallel CFD-DEM couplings

In this work, a new partition-collocation strategy for the parallel execution of CFD--DEM couplings is investigated. Having a good parallel performance is a key issue for an Eulerian-Lagrangian software that aims to be applied to solve industrially significant problems, as the computational cost of these couplings is one of their main drawback. The approach presented here consists in co-locating the overlapping parts of the simulation domain of each software on the same MPI process, in order to reduce the cost of the data exchanges. It is shown how this strategy allows reducing memory consumption and inter-process communication between CFD and DEM to a minimum and therefore to overcome an important parallelization bottleneck identified in the literature. Three benchmarks are proposed to assess the consistency and scalability of this approach. A coupled execution on 280 cores shows that less than 0.1% of the time is used to perform inter-physics data exchange

Parallelizing XDEM: Load-balancing policies and efficiency, a study

In XDEM, the simulation domain is geometrically decomposed in regular fixed-size cells that are used to distribute the workload between the processes. The role of the partitioning algorithm is to distribute the cells among all the processes in order to balance the workload. To accomplish this task, the partitioning algorithm relies on a computing/communication cost that has been estimated for each cell. A proper estimation of these costs is fundamental to obtain pertinent results during this phase. The study in the work is twofold. First, we integrate five partitioning algorithms (ORB, RCB, RIB, kway and PhG) in the XDEM framework [1]. Most of these algorithms are implemented within the Zoltan library [2], a parallel framework for partitioning and ordering problems. Secondly, we propose different policies to estimate the computing cost and communication cost of the different cells composing the simulation domain. Then, we present an experimental evaluation and a performance comparison of these partitioning algorithms and cost-estimation policies on a large scale parallel execution of XDEM running on the HPC platform of the University of Luxembourg. Finally, after explaining the pros and cons of each partitioning algorithms and cost-estimation policies, we discuss on the best choices to adopt depending on the simulation case

Parallel Coupling of CFD-DEM simulations

Eulerian-Lagrangian couplings are nowadays widely used to address engineering and technical problems. In particular, CFD-DEM couplings have been successfully applied to study several configurations ranging from mechanical, to chemical and environmental engineering. However, such simulations are normally very computationally intensive, and the execution time represents a major issue for the applicability of this numerical approach to complex scenarios. With this work, we introduce a novel coupling approach aiming at improving the performance of the parallel CFD-DEM simulations. This strategy relies on two points. First, we propose a new partition-collocation strategy for the parallel execution of CFD–DEM couplings, which can considerably reduce the amount of inter-process communication between the CFD and DEM parts. However, this strategy imposes some alignment constraints on the CFD mesh. Secondly, we adopt a dual-grid multiscale scheme for the CFD-DEM coupling, that is known to offer better numerical properties, and that allows us to obtain more flexibility on the domain partitioning overcoming the alignment constraints. We assess the correctness and performance of our approach on elementary benchmarks and at a large scale with a realistic test-case. The results show a significant performance improvement compared to other state-of-art CFD-DEM couplings presented in the literature

Determination of Kinetic Parameters for Heterogeneous Reaction System Employing Discrete Element Methods under HPC Platforms

The complex processes of heterogeneous reactions of granular materials such as occurring during metals-ore reduction or biomass gasification involve numerous physical phenomena. The combination of elevated temperature, complex flow, aggressive atmosphere and heterogeneous chemistry make it difficult to study these industrial processes. One of the most important aspects f heterogeneous reactions is to understand and quantify the evolution of the different transformations. For instance, during metal-oxides reduction processes, it is of high importance to quantify the rate at which the pure metal is formed. Nevertheless, it is almost impossible, by experimental means only, to separately observe, accurately quantify and gain insight into these mingled nonlinear physical and chemical processes. In the last decade, numerical simulation tools for particulate processes, such as the eXtended Discrete Element Method (XDEM), have become indispensable to study complex systems without the need of costly experimental practices. In the past, the XDEM has been employed to predict the reduction of tungsten trioxide (WO 3) in dry hydrogen (H2) atmospheres [1] and reduction of iron ores [2]. In the before-mentioned research works, it was employed kinetic data extracted from literature. On one hand, in these processes the kinetic data differ from each other. This is due to the fact that the experimental data in the literature is interpreted with lumped models and empirical models bonded to the specific experimental conditions. On the other hand, advanced simulation tools, such as XDEM, account for all the influencing phenomena (e.g. species and energy distribution, flow conditions, particles shape, rheological properties) constantly interacting in time and space. In these advanced simulation tools, each particle is treated and solved as individual entities and an accurate prediction of the species formation and transport in time and space is provided. Thus, in such advanced numerical tools, the reaction rate parameters representative of the kinetics alone of the involved chemical reactions must be employed. In this contribution, two XDEM simulation case studies accounting for the industrial reduction of WO 3 are presented. The first case study is employed to determine the reaction rate parameters of the four prevalent reduction steps (WO 3↔WO2.9↔WO2.72↔WO2↔W) upon the H 2 reduction of O3. Where the reaction rates are modeled following an Arrhenius law with two parameters per step i.e. pre-exponential factor and activation energy). The constituted optimization problem of minimization of error of the XDEM simulations vs experimental data, implemented and solved in a High Performance Computing (HPC) cluster, is presented and discussed. The determined parameters are later assessed by comparison to a secondly presented case study

Eulerian-Lagrangian momentum coupling between XDEM and OpenFOAM using preCICE

Eulerian-Lagrangian couplings consider problems with a discrete phase as a particulate material that is in contact with a fluid phase. These applications are as diverse as engineering, additive manufacturing, biomass conversion, thermal processing or pharmaceutical industry, among many others. A typical approach for this type of simulations is the coupling between Computation Fluid Dynamics (CFD) and Discrete Element Method (DEM), which is challenging in many ways. Such CFD--DEM couplings are usually implemented using an ad-hoc coupling layer, specific to the both DEM and CFD software, which considerably reduces the flexibility and applicability of the proposed implementation. In this work, we present the coupling of eXtended Discrete Element Method (XDEM), with the CFD library OpenFOAM, using the preCICE coupling library~\cite{preCICE} on volumetric meshes. Such momentum coupling requires the CFD side to account for the change of porosity due to the particulate phase and the particle momentum, while the particles of the DEM will be affected by the buoyancy and drag force of the fluid. While preCICE significantly simplifies the coupling between standalone libraries, each solver and, its respective adapter, have to be made aware of the new data involved in the physic model. For that, a new adapter has been implemented for XDEM and the existing adapter for OpenFOAM has been extended to include the additional data field exchange required for the momentum coupling, e.g porosity, particle momentum, fluid velocity and density. Our solution is tested and validated using simple benchmarks and advanced testcases such as a dam break, and shows consistent results

Numerical Analysis of Interaction between a Reacting Fluid and a Moving Bed with Spatially and Temporally Fluctuating Porosity

The purpose of this study is to propose a numerical approach that combines low computational costs through the use of high computing efficiency, allowing the realistic use of the design with a sufficient result's accuracy for industrial applications to investigate biomass combustion in a large-scale reciprocating grate. In the present contribution, a Biomass combustion chamber of a 16 MW geothermal steam super-heater, which is part of the Enel Green Power "Cornia 2" power plant,is being investigated with high-performance computing methods. For this purpose, the extended discrete element method (XDEM) developed at the University of Luxembourg is used in an HPC environment, which includes both the moving wooden bed and the combustion chamber above it. The XDEM simulation platform is based on a hybrid four-way coupling between the Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD). In this approach, particles are treated as discrete elements that are coupled by heat, mass, and momentum transfer to the surrounding gas as a continuous phase. For individual wood particles, besides the equations of motion, the differential conservation equations for mass, heat, and momentum are solved, which describe the thermodynamic state during thermal conversion. The grate system has three different moving sections to ensure good mixing of the biomass parts and appropriate residence time. The primary air enters from below the grate and is split into four different zones. Furthermore, a secondary air is injected at high velocity straight over the fuel bed through nozzles. A Flue Gas Recirculation is present and partly injected through two jets along the vertical channel and partly from below the grate. The numerical 3D model presented is based on a multi-phase approach. The biomass particles are taken into consideration via the XDEM Method, while the gaseous phase is described by CFD with OpenFOAM. Thus, the combustion of the particles on the moving beds in the furnace is processed by XDEM through conduction, radiation and conversion along with the interaction with the surrounding gas phase accounted for by CFD. The coupling of CFD-XDEM as an Euler-Lagrange model is used. The fluid phase is a continuous phase handled with an Eulerian approach and each particle is tracked with a Lagrangian approach. Energy, mass and momentum conservation is applied for every single particle and the interaction of particles with each other in the bed and with the surrounding gas phase are taken into account. An individual particle can have a solid, liquid, gas or inert material phases (immobile species) at the same time. The different phases can undergo a series of conversion through various reactions that can be homogeneous, heterogeneous or intrinsic (drying, pyrolysis, gasification and oxidation). Our first results are consistent with actual data obtained from the sampling of the residual solid in the industrial plant. Our model is also able to predict gas flux behaviour inside the furnace, particularly the flue gas recirculation on the combustion process injection