Abstract For the last 30 years, experimental and modeling studies have been carried out on fluidized bed reactors from laboratory up to industrial scales. The application of developed models for predictive simulations has however been strongly limited by the available computational power and the capability of computational fluid dynamics software to handle large enough simulations. In recent years, both aspects have made significant advances and we thus now demonstrate the feasibility of a massively parallel simulation, on whole supercomputers using NEPTUNE_CFD, of an industrial-scale polydispersed fluidized-bed reactor. This simulation of an olefin polymerization reactor makes use of an unsteady Eulerian multi-fluid approach and relies on a billion cells meshing. This is a worldwide premiere as the obtained accuracy is yet unmatched for such a large-scale system. The interest of this work is two-fold. In terms of High Performance Computation (HPC), all steps of setting-up the simulation, running it with NEPTUNE_CFD, and post-processing results induce multiple challenges due to the scale of the simulation. The simulation ran using 1260 up to 36,000 cores on supercomputers, used 15 millions of CPU hours and generated 200 TB of raw data for a simulated physical time of 25 s. This article details the methodology applied to handle this simulation, and also focuses on computation performances in terms of profiling, code efficiency and partitioning method suitability. Though being by itself interesting, the HPC challenge is not the only goal of this work as performing this highly-resolved simulation will benefit chemical engineering and CFD communities. Indeed, this computation enables the possibility to account, in a realistic way, for complex flows in an industrial-scale reactor. The predicted behavior is described, and results are post-processed to develop sub-grid models. These will allow for lower-cost simulations with coarser meshes while still encompassing local phenomenon.

中文翻译：

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使用多达 36,000 个 CPU 内核的工业规模多分散反应加压流化床大规模并行数值模拟，网格为 10 亿个单元

摘要 在过去的 30 年中，从实验室到工业规模的流化床反应器都进行了实验和建模研究。然而，用于预测模拟的开发模型的应用受到可用计算能力和计算流体动力学软件处理足够大模拟的能力的强烈限制。近年来，这两个方面都取得了重大进展，因此我们现在证明了在使用 NEPTUNE_CFD 的整个超级计算机上对工业规模多分散流化床反应器进行大规模并行模拟的可行性。这种烯烃聚合反应器的模拟使用了不稳定的欧拉多流体方法，并依赖于十亿个单元格网格。这是全球首演，因为获得的精度对于如此大规模的系统来说是无与伦比的。这项工作的兴趣是双重的。在高性能计算 (HPC) 方面，由于模拟的规模，设置模拟、使用 NEPTUNE_CFD 运行它以及后处理结果的所有步骤都会带来多重挑战。模拟运行在超级计算机上使用 1260 到 36,000 个内核，使用了 1500 万个 CPU 小时，并在 25 秒的模拟物理时间中生成了 200 TB 的原始数据。本文详细介绍了用于处理此模拟的方法，还重点介绍了分析、代码效率和分区方法适用性方面的计算性能。虽然本身很有趣，HPC 挑战并不是这项工作的唯一目标，因为执行这种高分辨率的模拟将使化学工程和 CFD 社区受益。事实上，这种计算使得能够以现实的方式解释工业规模反应器中的复杂流动。描述预测的行为，并对结果进行后处理以开发子网格模型。这些将允许使用较粗的网格进行低成本模拟，同时仍包含局部现象。