Domain decomposition with discrete element simulations using shared-memory parallel computing for railways applications

Abstract

Numerical simulation with discrete elements leads to several issues for large scale problems and long loading times, as for the granular dynamic simulations of the ballasted railway behavior. To reduce computational costs, we study the use of two strategies: domain decomposition methods and shared-memory parallelization with OpenMP. An example of a maintenance process, the tamping, on a portion of railway track with 7 sleepers, is simulated.RÉSUMÉ. La simulation numérique par éléments discrets présente des difficultés pour l'étude de problèmes de grande taille et en temps de sollicitation long, comme la dynamique des milieux granulaires pour le ballast ferroviaire. Afin de résoudre ce problème à moindre coût, on propose d'allier deux stratégies : la décomposition de domaine (DDM) et le calcul parallèle (en mémoire partagée avec OpenMP). Un exemple traitant d'un procédé de maintenance ferroviaire, le bourrage, sur une portion de voie ballastée de 7 blochets de long est étudié.

Parallel computing in railway research

“…The DDM in this case can deal with the boundary conditions and partition the dependent computing tasks into parallelisable domains as shown in Figure 5(b). DDM has been widely used in railway applications [7,19,[21][22][23][24][25][26][27][28][29]. More information regarding the DDM can be found in Reference [20].…”

“…In railway research, parallel FEA has mainly been used to study three topics: (1) wheel-rail contact issues [7,28,29]; (2) bridge structures [25,55]; and (3) interactions between trains and infrastructure [21][22][23]26]. In addition to the FEA related studies, Hoang et al [19,24] have used parallel DEA to simulate railway ballast. Parallel CFD has been used to analyse vehicle response subject to cross-wind [18], the entry flow issue of railway tunnels [27], catenary aerodynamics [78] and air brake modelling [41].…”

Stochastic heterogeneous material modeling for wave propagation in a ballast layer

“…The discrete approaches are today capable of solving only a few meters-length of ballast. The absence of an efficient scheme for parallelization over large clusters of computers [2] and the coupling between the grains and the soil [3] remains open problems. On the other hand, the methods based on continuum mechanics (like FEM) can solve very large models thanks to efficient parallelization schemes.…”

High performance computing of discrete nonsmooth contact dynamics with domain decomposition

“…Up to our knowledge, the only solution methods coupling a DDM with non‐regular implicit contact dynamics for granular models are available in . The approach described in mainly uses an asynchronous distributed‐memory non‐linear Gauss‐Seidel solver (NLGS) (close to the one used in for a synchronous solver and in for an asynchronous version, both for shared‐memory architectures. They correspond to the algebraic partitioning of the reduced dynamics, that is, a splitting of grains between processors, while herein, we rely on a splitting of interactions between processors).…”

“…The approach described in [23] mainly uses an asynchronous distributed-memory Non-Linear Gauss-Seidel solver (close to the one used in [17] for a synchronous solver and in [18] for an asynchronous version, both for shared-memory architectures. They correspond to the algebraic partitioning of the reduced dynamics, i.e.…”