用马尔可夫链-蒙特卡罗模型研究释放到中性行星边界层的粒子弥散

R. Avila, S. Raza
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引用次数: 6

摘要

摘要:本文描述了连续释放到中性行星边界层中的粒子(流体元素)的分散和浓度。采用马尔可夫链-蒙特卡罗(MCMC)过程,以随机时间间隔产生粒子的速度波动,并具有一步记忆。采用全拉格朗日方法计算粒子的局部平均浓度,该方法进行有效的近邻搜索,并采用平滑核消除统计噪声。用Pasquill-Gifford σz和σy等经验参数对中性大气中升高的连续释放源的垂直和横向粒子偏离其平均位置[()1/2和()1/2]的预测均方根进行了比较。数值预测的颗粒浓度与高斯模型和在“绿光计划”中获得的地面浓度现场测量数据进行了比较。数值pr…
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Dispersion of Particles Released into a Neutral Planetary Boundary Layer Using a Markov Chain–Monte Carlo Model
Abstract The dispersion and concentration of particles (fluid elements) that are continuously released into a neutral planetary boundary layer is presented. The velocity fluctuations of the particles are generated using a Markov chain–Monte Carlo (MCMC) process at random time intervals with a one-step memory. The local mean concentration of the particles is calculated by using a fully Lagrangian method, which performs an efficient near-neighbor search and employs a smoothing kernel for eliminating the statistical noise. The predicted vertical and transversal root-mean-square of the particles’ deviation from their mean position [()1/2 and ()1/2] for an elevated continuous release source in a neutral atmosphere are compared with empirical parameters like the Pasquill–Gifford σz and σy. The numerical predictions of the particle concentration are compared with a Gaussian model and field measurement data on the ground concentration obtained during the Green Glow Program. The comparison between the numerical pr...
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