Parallel Algorithms for Particles–Turbulence Two-Way Interaction Direct Numerical Simulation

• Published in: Journal of parallel and distributed computing Vol. 62; no. 1; pp. 38 - 60
• Main Authors: Ling, W., Liu, J., Chung, J.N., Crowe, C.T.
• Format: Journal Article
• Language: English
• Published: San Diego, CA Elsevier Inc 01.01.2002; Elsevier
• Subjects: Applied sciences; Computational methods in fluid dynamics; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Fluid dynamics; Fundamental areas of phenomenology (including applications); Physics; Software; Turbulence; Digital simulation; Momentum; Flow pattern; Parallel algorithms; Sandwich structures; Mixing layer; Coarse particle; Large Reynolds number; Three dimensional model; Two-phase flow; Particle interactions; Large scale; Domain decomposition
• ISSN: 0743-7315; 1096-0848
• DOI: 10.1006/jpdc.2001.1781

Understanding the demixing effect on the dispersion of particles by large-scale turbulence is very important in practical applications. Using pseudo-spectral and Lagrangian approaches, we have simulated a three-dimensional particle-laden mixing layer under one-way coupling effect. However, the computer resource required to simulate such a two-phase flow with high Reynolds number and two-way momentum coupling effect exceeds the limit of the current single processor. In this paper, the computation of particles and the two-way momentum coupling terms are partitioned in the span-wise direction because particles are distributed most evenly in this direction. The computation of the tree-dimensional flow field is first partitioned into three groups of processors because of the most independence of the computation among the three spatial dimensions. In each group, the domain is then partitioned using two different schemes based on the property of the fast Fourier transformation. The first one, the master–slave scheme, is employed for Algorithm MS due to its simplicity and overlapping of communication and computation. The second one, the transpose approach, is used for Algorithm TP order to partition all of the flow field computation. An analysis shows that compared to Algorithm MS, Algorithm TP can also reduce nearly a half of the amount of communication work. Experiments show that Algorithm MS has obtained a speedup of 4.3 using 9HP workstations and a speedup of 6.4 using 15 nodes of IBM SP-2 for a problem size on the order of 643, and Algorithm TP has achieved speedups 44% higher than Algorithm MS.