Assessing and improving the accuracy of synthetic turbulence generation
Assessing and improving the accuracy of synthetic turbulence generation
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DOI：
10.1017/jfm.2020.859
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发表时间：
2020-11
影响因子：
3.7
通讯作者：
J. W. Patterson;R. Balin;K. Jansen
中科院分区：
文献类型：
--
作者：
J. W. Patterson;R. Balin;K. Jansen
摘要：随着人们对空间演化边界层的尺度解析模拟越来越感兴趣，合成湍流生成（STG）已成为一种有价值的工具，它通过对有限数量的时空傅里叶模式（其幅度、方向和相位由随机数集确定）求和来提供非稳态湍流边界条件。 STG 方法的最新发展旨在匹配各向异性和不均匀雷诺应力的目标轮廓。本文表明，对于模态数量的实际值，一组给定的随机数可能会产生偏离目标 30% 的雷诺应力分布。为了弥补这种情况，STG应力预测中的误差被分解为稳态偏差和影响时间收敛的纯非稳态部分。随机数向量与两种类型的误差之间的直接关系被开发出来，允许快速扫描大量随机数集，并选择最佳执行者，以大大提高与目标的一致性。该过程经过验证，可在 $Re_\theta = 1000$ 时流入平板的直接数值模拟。本文证明了在几次流通时间内的足够时间收敛以及该方法偏差的修正。
Abstract With the growing interest in scale-resolving simulations of spatially evolving boundary layers, synthetic turbulence generation (STG) has become a valuable tool for providing unsteady turbulent boundary conditions through a sum over a finite number of spatio-temporal Fourier modes with amplitude, direction and phase determined by a random number set. Recent developments of STG methods are designed to match target profiles for anisotropic and inhomogeneous Reynolds stresses. In this paper, it is shown that, for practical values of the number of modes, a given set of random numbers may produce Reynolds stress profiles that are 30 % off their target. To remedy this situation, the error in the STG stress prediction is decomposed into a steady-state bias and a purely unsteady part affecting the time convergence. Direct relationships between the random number vectors and both types of error are developed, allowing large collections of random number sets to be rapidly scanned and the best performers selected for a much improved agreement with the target. The process is verified for the inflow to a direct numerical simulation of a flat plate at $Re_\theta = 1000$. This paper demonstrates sufficient time convergence over a few flow-through times as well as a correction of the method's biases.