期刊
JOURNAL OF COMPUTATIONAL PHYSICS
卷 334, 期 -, 页码 497-521出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2016.12.049
关键词
GPU; High-order; Flux reconstruction; Turbulent; Flows; Comparison
资金
- Engineering and Physical Sciences Research Council [EP/K027379/1, EP/M50676X/1]
- EPSRC [EP/M50676X/1, EP/K027379/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/M50676X/1, EP/K027379/1] Funding Source: researchfish
First- and second-order accurate numerical methods, implemented for CPUs, underpin the majority of industrial CFD solvers. Whilst this technology has proven very successful at solving steady-state problems via a Reynolds Averaged Navier-Stokes approach, its utility for undertaking scale-resolving simulations of unsteady flows is less clear. High order methods for unstructured grids and GPU accelerators have been proposed as an enabling technology for unsteady scale -resolving simulations of flow over complex geometries. In this study we systematically compare accuracy and cost of the high-order Flux Reconstruction solver PyFR running on GPUs and the industry-standard solver STARCCM+ running on CPUs when applied to a range of unsteady flow problems. Specifically, we perform comparisons of accuracy and cost for isentropic vortex advection (EV), decay of the Taylor-Green vortex (TGV), turbulent flow over a circular cylinder, and turbulent flow over an SD7003 aerofoil. We consider two configurations of STAR-CCM+: a second order configuration, and a third -order configuration, where the latter was recommended by CD-adapco for more effective computation of unsteady flow problems. Results from both PyFR and STAR-CCM+ demonstrate that third-order schemes can be more accurate than second -order schemes for a given cost e.g. going from second-to third-order, the PyFR simulations of the EV and TGV achieve 75x and 3x error reduction respectively for the same or reduced cost, and STAR-CCM+ simulations of the cylinder recovered wake statistics significantly more accurately for only twice the cost. Moreover, advancing to higher-order schemes on GPUs with PyFR was found to offer even further accuracy vs. cost benefits relative to industry-standard tools. (C) 2017 The Authors. Published by Elsevier Inc.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据