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arXiv 2002.01515physics.flu-dyn
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粗糙壁面湍流中等效砂粒高度的数据驱动预测

Data-driven prediction of the equivalent sand-grain height in rough-wall turbulent flows

Mostafa Aghaei Jouybari, Junlin Yuan, Giles J. Brereton, Michael S. Murillo

AI总结:

针对粗糙壁面湍流等效砂粒高度预测难题,采用DNN和GPR机器学习方法,结合45种数值模拟表面与15组实验数据,实现了精度优于现有公式的高保真预测,并明确了孔隙率和展向有效斜率的关键作用。

摘要:

本文研究了表面粗糙度对湍流影响这一长期存在的问题:对于给定的粗糙度形貌,其等效粗糙度砂粒高度是多少?研究采用深度神经网络(DNN)和高斯过程回归(GPR)机器学习方法,开发了一种适用于多种不同粗糙表面湍流的高保真Nikuradse等效砂粒高度$k_s$预测方法。为此,研究生成了45种表面几何形状,并采用直接数值模拟在$\text{Re}_τ=1000$条件下模拟了其上方的流动。这些表面几何形状在表面高度波动矩、有效斜率、平均倾角、孔隙率和随机程度上存在显著差异。其中30种表面被认定为完全粗糙,研究还补充了既往研究中15种其他表面的完全粗糙流动实验数据。DNN和GPR方法预测$k_s$的平均误差小于10%,最大误差小于30%,精度显著高于现有预测公式。研究还发现,表面孔隙率和展向粗糙度有效斜率是阻力预测的重要影响因素。

英文摘要:

This paper investigates a long-standing question about the effect of surface roughness on turbulent flow: what is the equivalent roughness sand-grain height for a given roughness topography? Deep Neural Network (DNN) and Gaussian Process Regression (GPR) machine learning approaches are used to develop a high-fidelity prediction approach of the Nikuradse equivalent sand-grain height $k_s$ for turbulent flows over a wide variety of different rough surfaces. To this end, 45 surface geometries were generated and the flow over them simulated at $\hbox{Re}_τ=1000$ using direct numerical simulations. These surface geometries differed significantly in moments of surface height fluctuations, effective slope, average inclination, porosity and degree of randomness. Thirty of these surfaces were considered fully-rough and they were supplemented with experimental data for fully-rough flows over 15 more surfaces available from previous studies. The DNN and GPR methods predicted $k_s$ with an average error of less than 10% and a maximum error of less than 30%, which appears to be significantly more accurate than existing prediction formulas. They also identified the surface porosity and the effective slope of roughness in the spanwise direction as important factors in drag prediction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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