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New approach to solving the atmospheric pollutant dispersion equation using fractional derivatives

机译:分数阶导数求解大气污染物扩散方程的新方法

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In atmospheric environments, traditional differential equations do not adequately describe the problem of turbulent diffusion because the usual derivatives are not well defined in the non-differentiable behaviour introduced by turbulence, where the fractional calculation has become a very useful tool for studying anomalous dispersion and other transportation processes. Considering a new direction, this paper presents an analytical series solution of a three-dimensional advection-diffusion equation of fractional order, in the Caputo sense, applied to the dispersion of atmospheric pollutants. The solution is obtained by applying the generalised integral transform technique (GITT), solving the transformed problem by the Laplace decomposition method (LDM), and considering the lateral and vertical turbulent diffusion dependence on the longitudinal distance from the source, as well as a fractional parameter. The fractional solution is more general than the traditional solution in the sense that consideration of the integer order of the fractional parameter yields the traditional solution. The solution considers the memory effect in eddy diffusivity and in the fractional derivative, and it is simple, easy to implement, and converges rapidly. Numerical simulations were conducted to compare the performance of the proposed fractional solution to the traditional solution using an experimental dataset and other models, which also made it possible to find a better parametrisation for use in Gaussian models. The best results are obtained with the fractional order of the derivative. (C) 2019 Elsevier Ltd. All rights reserved.
机译:在大气环境中,传统的微分方程无法充分描述湍流扩散的问题,因为在湍流引入的不可微行为中,通常的导数没有得到很好的定义,分数计算已成为研究异常色散和其他异常现象的非常有用的工具。运输过程。考虑到一个新的方向,本文提出了在Caputo意义上的分数阶三维对流扩散方程的解析级数解,适用于大气污染物的扩散。该解决方案是通过应用广义积分变换技术(GITT),通过拉普拉斯分解方法(LDM)解决变换后的问题,并考虑横向和纵向湍流扩散对距源的纵向距离以及分数的依赖关系而获得的参数。在考虑分数参数的整数阶会产生传统解的意义上,分数解比传统解更笼统。该解决方案考虑了涡流扩散率和分数导数中的记忆效应,它简单,易于实现且收敛迅速。进行了数值模拟,以使用实验数据集和其他模型将建议的分数解与传统解的性能进行比较,这也使得找到用于高斯模型的更好参数化成为可能。用导数的分数阶可获得最佳结果。 (C)2019 Elsevier Ltd.保留所有权利。

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