Abstract Computational Fluid Dynamics (CFD) is one of the most powerful simulation methods, which is used for temporally and spatially resolved solutions of fluid flow, heat transfer, mass transfer, etc. One of the challenges of Computational Fluid Dynamics is the extreme hardware demand. Nowadays super-computers (e.g. High Performance Computing, HPC) featuring multiple CPU cores are applied for solving—the simulation domain is split into partitions for each core. Some of the different methods for partitioning are investigated in this paper. As a practical example, a new open source based solver was utilized for simulating packed bed adsorption, a common separation method within the field of thermal process engineering. Adsorption can for example be applied for removal of trace gases from a gas stream or pure gases production like Hydrogen. For comparing the performance of the partitioning methods, a 60 million cell mesh for a packed bed of spherical adsorbents was created; one second of the adsorption process was simulated. Different partitioning methods available in OpenFOAM ® ( Scotch , Simple , and Hierarchical ) have been used with different numbers of sub-domains. The effect of the different methods and number of processor cores on the simulation speedup and also energy consumption were investigated for two different hardware infrastructures (Vienna Scientific Clusters VSC 2 and VSC 3). As a general recommendation an optimum number of cells per processor core was calculated. Optimized simulation speed, lower energy consumption and consequently the cost effects are reported here.
[1]
Douglas M. Ruthven,et al.
Principles of Adsorption and Adsorption Processes
,
1984
.
[2]
Michael Harasek,et al.
Numerische Simulation des Konzentrations‐ und Strömungsprofiles in einem Festbettadsorber
,
2015
.
[3]
G McKay,et al.
Adsorption isotherm models for basic dye adsorption by peat in single and binary component systems.
,
2004,
Journal of colloid and interface science.
[4]
François Pellegrini,et al.
PT-Scotch: A tool for efficient parallel graph ordering
,
2008,
Parallel Comput..
[5]
Anja R. Paschedag,et al.
CFD in der Verfahrenstechnik: Allgemeine Grundlagen und mehrphasige Anwendungen
,
2004
.
[6]
Sang Bong Lee.
Numerical discrepancy between serial and MPI parallel computations
,
2016
.
[7]
D. Do,et al.
Adsorption analysis : equilibria and kinetics
,
1998
.
[8]
Anita Plazinska,et al.
Theoretical models of sorption kinetics including a surface reaction mechanism: a review.
,
2009,
Advances in colloid and interface science.
[9]
I. E. Barton,et al.
Comparison of SIMPLE‐ and PISO‐type algorithms for transient flows
,
1998
.
[10]
Shamoon Jamshed,et al.
Using HPC for Computational Fluid Dynamics: A Guide to High Performance Computing for CFD Engineers
,
2015
.
[11]
Weeratunge Malalasekera,et al.
An introduction to computational fluid dynamics - the finite volume method
,
2007
.