Increasing Computational Efficiency of CFD Simulations of Reactive Flows at Catalyst Surfaces through Dynamic Load Balancing

IF 4.3 Q2 ENGINEERING, CHEMICAL ACS Engineering Au Pub Date : 2024-01-19 DOI:10.1021/acsengineeringau.3c00066
Daniele Micale, Mauro Bracconi* and Matteo Maestri, 
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Abstract

We propose a numerical strategy based on dynamic load balancing (DLB) aimed at enhancing the computational efficiency of multiscale CFD simulation of reactive flows at catalyst surfaces. Our approach employs DLB combined with a hybrid parallelization technique, integrating both MPI and OpenMP protocols. This results in an optimized distribution of the computational load associated with the chemistry solution across processors, thereby minimizing computational overheads. Through assessments conducted on fixed and fluidized bed reactor simulations, we demonstrated a remarkable improvement of the parallel efficiency from 19 to 87% and from 19 to 91% for the fixed and fluidized bed, respectively. Owing to this improved parallel efficiency, we observe a significant computational speed-up of 1.9 and 2.1 in the fixed and fluidized bed reactor simulations, respectively, compared to simulations without DLB. All in all, the proposed approach is able to improve the computational efficiency of multiscale CFD simulations paving the way for a more efficient exploitation of high-performance computing resources and expanding the current boundaries of feasible simulations.

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通过动态负载平衡提高催化剂表面反应流 CFD 模拟的计算效率
我们提出了一种基于动态负载平衡(DLB)的数值策略,旨在提高催化剂表面反应流多尺度 CFD 模拟的计算效率。我们的方法将 DLB 与混合并行化技术相结合,同时整合了 MPI 和 OpenMP 协议。这使得与化学解决方案相关的计算负荷在处理器之间得到优化分配,从而将计算开销降至最低。通过对固定床和流化床反应器模拟的评估,我们证明了并行效率的显著提高,固定床和流化床的并行效率分别从 19% 提高到 87%,从 19% 提高到 91%。与不使用 DLB 的模拟相比,由于并行效率的提高,我们发现固定床和流化床反应器模拟的计算速度分别显著提高了 1.9 和 2.1。总之,所提出的方法能够提高多尺度 CFD 模拟的计算效率,为更有效地利用高性能计算资源铺平了道路,并扩展了当前可行模拟的范围。
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ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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