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Reduction of the random variables of the turbulent wind field

机译：减少湍动风场的随机变量

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Applicability of the Probability Density Evolution Method (PDEM) for realizing evolution of the probability density for the wind turbines has rather strict bounds on the basic number of the random variables involved in the model. The efficiency of most of the Advanced Monte Carlo (AMC) methods, i.e. Importance Sampling (IS) or Subset Simulation (SS), will be deteriorated on problems with many random variables. The problem with PDEM is that a multidimensional integral has to be carried out over the space defined by the random variables of the system. The numerical procedure requires discretization of the integral domain; this becomes increasingly difficult as the dimensions of the integral domain increase. On the other hand efficiency of the AMC methods is closely dependent on the design points of the problem. Presence of many random variables may increase the number of the design points, hence affects the efficiency of the AMC methods. The idea of the paper is to propose new schemes which allow reduction of the basic random variables of the turbulence such that PDEM and Advanced Monte Carlo (AMC) methods, i.e. subset simulation, are applicable on it.
机译：概率密度演化方法（PDEM）用于实现风力涡轮机概率密度演化的适用性对模型中涉及的随机变量的基本数量有严格的限制。大多数高级蒙特卡洛（AMC）方法（即重要采样（IS）或子集模拟（SS））的效率都会因存在许多随机变量的问题而降低。 PDEM的问题是必须在系统随机变量定义的空间上执行多维积分。数值过程要求对积分域进行离散化。随着积分域的尺寸增加，这变得越来越困难。另一方面，AMC方法的效率密切取决于问题的设计要点。许多随机变量的存在可能会增加设计点的数量，从而影响AMC方法的效率。本文的思想是提出允许减小湍流的基本随机变量的新方案，使得PDEM和高级蒙特卡洛（AMC）方法，即子集仿真，可应用于其上。

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