Preparing the path for the efficient simulation of turbulent compressible industrial flows with robust collocated DG-RK solvers

R. Jahdali, M. Parsani
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Abstract

. We present an analysis of the performance of some standard and optimized explicitly Runge– Kutta schemes that are equipped with CFL-based and error-based time step adaptivity when they are coupled with the relaxation procedure to achieve fully-discrete entropy stability for complex compressible flow simulations. We investigate the performance of the temporal integration algorithms by simulating the flow past the NASA juncture flow model using the in-house KAUST SSDC hp-adaptive collocated entropy stable discontinuous Galerkin solver. In addition, we present a preliminary analysis of the performance of the SSDC framework on the Amazon web service cloud computing. The results indicate that SSDC scales well on the most recent and exotic computing architectures available on the Amazon cloud platform. Our findings might help select a more robust and efficient temporal integration algorithm and guide the choice of the EC2 AWS instances that give the best price and wall-clock-time performance to simulate industrially relevant turbulent flow problems.
用鲁棒配置DG-RK求解器为湍流可压缩工业流的有效模拟准备路径
. 我们分析了一些标准的和明确优化的Runge - Kutta格式的性能,这些格式具有基于cfl和基于误差的时间步长自适应,当它们与松弛过程耦合时,可以实现复杂可压缩流动模拟的完全离散熵稳定性。我们利用KAUST内部的SSDC hp-自适应共配熵稳定不连续伽辽金解算器模拟了经过NASA结合部流模型的流动,研究了时间积分算法的性能。此外,我们还对SSDC框架在Amazon web服务云计算上的性能进行了初步分析。结果表明,SSDC在Amazon云平台上可用的最新和外来计算架构上都可以很好地扩展。我们的研究结果可能有助于选择一个更强大、更有效的时间积分算法,并指导选择具有最佳价格和时钟性能的EC2 AWS实例来模拟工业相关的湍流问题。
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