Poroseva Svetlana V, Colmenares F Juan D, Murman Scott M
Mechanical Engineering Department, University of New Mexico, Albuquerque, NM, 87131, U.S.A.
NASA Ames Research Center, Moffett Field, CA 94035, U.S.A.
Phys Fluids (1994). 2016 Nov;28(11). doi: 10.1063/1.4966639. Epub 2016 Nov 22.
Simulation results conducted for incompressible planar wall-bounded turbulent flows with the Reynolds-Averaged Navier-Stokes (RANS) equations with no modeling involved are presented. Instead, all terms but the molecular diffusion are represented by the data from direct numerical simulation (DNS). In simulations, the transport equations for velocity moments through the second order (and the fourth order where the data are available) are solved in a zero-pressure gradient boundary layer over a flat plate and in a fully-developed channel flow in a wide range of Reynolds numbers using DNS data from Sillero et al. (2013), Lee & Moser (2015), and Jeyapaul et al. (2015). The results obtained demonstrate that DNS data are the significant and dominant source of uncertainty in such simulations (hereafter, RANS-DNS simulations). Effects of the Reynolds number, flow geometry, and the velocity moment order as well as an uncertainty quantification technique used to collect the DNS data on the results of RANS-DNS simulations are analyzed. New criteria for uncertainty quantification in statistical data collected from DNS are proposed to guarantee the data accuracy sufficient for their use in RANS equations and for the turbulence model validation.
本文给出了对不可压缩平面壁面湍流进行模拟的结果,该模拟采用雷诺平均纳维 - 斯托克斯(RANS)方程,不涉及任何模型。相反,除分子扩散项外的所有项均由直接数值模拟(DNS)数据表示。在模拟中,利用Sillero等人(2013年)、Lee和Moser(2015年)以及Jeyapaul等人(2015年)的DNS数据,在零压力梯度平板边界层和不同雷诺数下充分发展的通道流中求解二阶(以及有数据时的四阶)速度矩的输运方程。所得结果表明,DNS数据是此类模拟(以下简称RANS - DNS模拟)中不确定性的重要且主要来源。分析了雷诺数、流动几何形状、速度矩阶数以及用于收集DNS数据的不确定性量化技术对RANS - DNS模拟结果的影响。提出了从DNS收集的统计数据中进行不确定性量化的新标准,以确保数据精度足以用于RANS方程和湍流模型验证。