An Efficient Scheme for Coupling OpenMC and FLUENT with Adaptive Load Balancing

This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been...

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Vydáno v:Science and technology of nuclear installations Ročník 2021; s. 1 - 16
Hlavní autoři: Zhang, Qingyang, Peng, Tianji, Zhang, Guangchun, Liu, Jie, Guo, Xiaowei, Gong, Chunye, Yang, Bo, Fan, Xukai
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Hindawi 24.09.2021
John Wiley & Sons, Inc
Wiley
Témata:
Accuracy
Adaptive algorithms
Algorithms
CAD
Codes
Computational fluid dynamics
Computer aided design
Computers
Data exchange
Efficiency
Fluid dynamics
Fluid flow
Hydraulics
Interfaces
Iterative algorithms
Iterative methods
Load balancing
Nodes
Nuclear energy
Performance enhancement
Physics
Sustainable development
Thermal simulation
ISSN:1687-6075, 1687-6083
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Shrnutí:This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.
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ISSN:1687-6075
1687-6083
DOI:10.1155/2021/5549602