Nuclear Engineering and Technology 49 (2017) 1310e1317

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Nuclear Engineering and Technology

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Original Article

Large eddy simulation of turbulent flow using the parallel computational fluid dynamics code GASFLOW-MPI

Han Zhang*, Yabing Li, Jianjun Xiao, Thomas Jordan Institute of Nuclear and Energy Technologies, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany

a r t i c l e i n f o

Article history: Received 2 June 2017 Received in revised form 31 July 2017 Accepted 3 August 2017 Available online 30 August 2017

Keywords: GASFLOW-MPI large eddy simulation M&C2017 scalable linear solver

  • Corresponding author. E-mail address: (H. 1738-5733/© 2017 Korean Nuclear Society, Published licenses/by-nc-nd/4.0/).

a b s t r a c t

GASFLOW-MPI is awidely used scalable computational fluid dynamics numerical tool to simulate the fluid turbulence behavior, combustion dynamics, and other related thermalehydraulic phenomena in nuclear power plant containment. An efficient scalable linear solver for the large-scale pressure equation is one of the key issues to ensure the computational efficiency of GASFLOW-MPI. Several advanced Krylov subspace methods and scalable preconditioningmethods are compared and analyzed to improve the computational performance. With the help of the powerful computational capability, the large eddy simulation turbulent model is used to resolve more detailed turbulent behaviors. A backward-facing step flow is performed to study the free shear layer, the recirculation region, and the boundary layer, which is widespread in many scientific and engineering applications. Numerical results are comparedwith the experimental data in the literature and the direct numerical simulation results by GASFLOW-MPI. Both time-averaged velocity profile and turbulent intensity are well consistent with the experimental data and direct numerical simulation result. Furthermore, the frequency spectrum is presented and a e5/3 energy decay is observed for a wide range of frequencies, satisfying the turbulent energy spectrum theory. Parallel scaling tests are also implemented on the KIT/IKET cluster and a linear scaling is realized for GASFLOW-MPI. © 2017 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the

CC BY-NC-ND license (

  1. Introduction

Hydrogen risk evaluation in nuclear power plant (NPP) containment is an important issue for severe accident analysis, because hydrogen could be ignited anddamage the integrity of NPPs during a severe accident, such as the Fukushima accident in 2011 [1]. Accurate prediction of the hydrogen turbulent transport is the first, but crucial, step to determine hydrogen distribution. Extensive experimental research [2e4] and numerical simulation [5e7] were carried out to study the turbulent transport and other related phe- nomena in NPP containment. With the help of the powerful computational capability, computational fluid dynamics (CFD) be- comes a practical numerical tool to analyze the hydrogen behavior in NPP containment [8e10]. In order to reduce the computational time or enable high-fidelity predictionwithmore detailed geometry information for large-scale containment simulations, efficient scalable parallel computation, especially the high-performance scalable parallel linear solver, is one of the key points for a


by Elsevier Korea LLC. This is an

successful hydrogen distribution prediction. In this article, a large eddysimulation (LES) of thebackward-facing stepflow is performed by using the parallel CFD code GASFLOW-MPI to study the wall- bounded turbulent flow as well as to preliminarily evaluate the parallel performance of the GASFLOW-MPI.

GASFLOW-MPI is an advanced parallel CFD numerical tool that has been developed based on the message passing interface (MPI) library and domain decomposition technique. GASFLOW-MPI [11,12] has been developed, validated, and widely used to predict the complex thermalehydraulic behavior in NPP containments. In the past decades, GASFLOW was applied to simulate the hydrogen cloud distribution and evaluate the risk mitigation strategy for different types of nuclear plants, such as the EPR [13], the Inter- national Thermonuclear Experimental Reactor (ITER) [14], the German Konvoi-type PWR [15], the VVER [16], and the APR1400 [17], as well as the well-known open tests [18] and blind tests [19].

Obviously, an efficient scalable parallel linear solver for the large- scale symmetrical sparse equations derived from the pressure equation is one of the key issues to ensure the computational effi- ciency of GASFLOW-MPI. The preconditioned Krylov subspace iter- ative method is a type of efficient linear solver. The first Krylov

open access article under the CC BY-NC-ND license (


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