无网格LRBF法预测根区土壤水分分布

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED
Mohamed Boujoudar , Abdelaziz Beljadid , Ahmed Taik
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引用次数: 0

摘要

本研究的主要目的是建立一个有植物根系吸水的土壤非饱和流动数值模型。数值模型采用理查兹方程和不同的汇项公式来描述根区土壤水分的分布。其中使用了基尔霍夫变换理查兹方程,并考虑了毛细管压力的加德纳模型。在拟议的数值方法中,我们采用了空间局部径向基函数(LRBF)无网格技术和时间离散化的后向欧拉方案来求解系统。LRBF 方法是一种精确且计算效率高的方法,无需生成网格,可灵活处理高维问题。此外,这种方法还能得到稀疏矩阵系统,从而避免了条件不良问题。我们为一维、二维和三维土壤建立了渗透和植物根系吸水的数值模型。我们利用非微观分析解和现有实验数据进行了数值实验,以验证所提数值技术的性能。结果表明,所提出的数值模型能够预测根区的土壤水分动态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LRBF meshless methods for predicting soil moisture distribution in root zone
The main purpose of this study is to develop a numerical model of unsaturated flow in soils with plant root water uptake. The Richards equation and different sink term formulations are used in the numerical model to describe the distribution of soil moisture in the root zone. The Kirchhoff transformed Richards equation is used and the Gardner model is considered for capillary pressure. In the proposed numerical approach, we used localized radial basis function (LRBF) meshless techniques in space and the backward Euler scheme for temporal discretization to solve the system. The LRBF approach is an accurate and computationally efficient method that does not require mesh generation and is flexible in addressing high-dimensional problems. Furthermore, this method leads to a sparse matrix system, which avoids ill-conditioning issues. We implement the numerical model of infiltration and plant root water uptake for one, two, and three-dimensional soils. Numerical experiments are performed using nontrivial analytical solutions and available experimental data to validate the performance of the proposed numerical techniques. The results demonstrate the capability of the proposed numerical model to predict soil moisture dynamics in root zone.
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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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