基于Hgstrm(1996)和Beljaars et al.(1991)的研究工作,沿用Louis et al.(1982)和Launiainen(1995)的思路,本文采用多元回归分析方法,研发了一种采用非迭代方法的湍流通量参数化方案。该方案直接用整体理查森数、空气动力学粗糙度长...基于Hgstrm(1996)和Beljaars et al.(1991)的研究工作,沿用Louis et al.(1982)和Launiainen(1995)的思路,本文采用多元回归分析方法,研发了一种采用非迭代方法的湍流通量参数化方案。该方案直接用整体理查森数、空气动力学粗糙度长度和热力学粗糙度长度对稳定度参数进行参数化,从而避免了通过循环迭代计算Monin-Obukhov长度。该方案不仅有效地节省了CPU计算时间,而且其计算结果与迭代方案(BHH方案)的计算结果非常接近。展开更多
Modeling of rough surfaces with given roughness parameters is studied,where surfaces with Gaussian height distribution and exponential auto-correlation function(ACF) are concerned.A large number of micro topography sa...Modeling of rough surfaces with given roughness parameters is studied,where surfaces with Gaussian height distribution and exponential auto-correlation function(ACF) are concerned.A large number of micro topography samples are randomly generated first using the rough surface simulation method with FFT.Then roughness parameters of the simulated roughness profiles are calculated according to parameter definition,and the relationship between roughness parameters and statistical distribution parameters is investigated.The effects of high-pass filtering with different cut-off lengths on the relationship are analyzed.Subsequently,computing formulae of roughness parameters based on standard deviation and correlation length are constructed with mathematical regression method.The constructed formulae are tested with measured data of actual topographies,and the influences of auto-correlation variations at different lag lengths on the change of roughness parameter are discussed.The constructed computing formulae provide an approach to active modeling of rough surfaces with given roughness parameters.展开更多
文摘基于Hgstrm(1996)和Beljaars et al.(1991)的研究工作,沿用Louis et al.(1982)和Launiainen(1995)的思路,本文采用多元回归分析方法,研发了一种采用非迭代方法的湍流通量参数化方案。该方案直接用整体理查森数、空气动力学粗糙度长度和热力学粗糙度长度对稳定度参数进行参数化,从而避免了通过循环迭代计算Monin-Obukhov长度。该方案不仅有效地节省了CPU计算时间,而且其计算结果与迭代方案(BHH方案)的计算结果非常接近。
基金Projects(51535012,U1604255)supported by the National Natural Science Foundation of ChinaProject(2016JC2001)supported by the Key Research and Development Project of Hunan Province,China
文摘Modeling of rough surfaces with given roughness parameters is studied,where surfaces with Gaussian height distribution and exponential auto-correlation function(ACF) are concerned.A large number of micro topography samples are randomly generated first using the rough surface simulation method with FFT.Then roughness parameters of the simulated roughness profiles are calculated according to parameter definition,and the relationship between roughness parameters and statistical distribution parameters is investigated.The effects of high-pass filtering with different cut-off lengths on the relationship are analyzed.Subsequently,computing formulae of roughness parameters based on standard deviation and correlation length are constructed with mathematical regression method.The constructed formulae are tested with measured data of actual topographies,and the influences of auto-correlation variations at different lag lengths on the change of roughness parameter are discussed.The constructed computing formulae provide an approach to active modeling of rough surfaces with given roughness parameters.
