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An improved meshless method based on strong-weak coupling algorithm for electric field calculation

机译:一种改进的基于强弱耦合算法的电场计算的无网纤维方法

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The traditional numerical calculation methods of electrostatic field include finite element method, finite difference method and so on. Due to the time-consuming meshing, the meshless method is gradually used by scholars for physical field analysis. At present, the meshless local Petrov-Galerkin method and the meshfree weak-strong method have been reported in various fields with their better accuracy and wide applicability. This paper presents an improved meshless strong-weak coupling method based on test function. The scattered nodes are used to represent the problem domain and boundary, and the shape function is constructed by the moving least square method. For the nodes where the local integration domain does not intersect the global derivative boundary, the meshless strong form method is used to construct the basic equation. For other nodes, the meshless local weak formula method is used. The exponential function is used as a test function for weak formula expressions. By changing the coefficient of the exponential function, the shape of the test function is controlled, and the influence of the change in the size of the influence domain on the calculation accuracy is reduced. The case analysis shows that this method has higher accuracy and better convergence effect than the traditional meshless method. The proposed method has high precision and computational efficiency in processing electrical numerical calculations, and has strong astringency. This method has better application prospects than traditional methods.
机译:传统的静电场数值计算方法包括有限元方法,有限差分法等。由于耗时的啮合,学者逐步使用了无网格方法进行物理场分析。目前,在各种领域报告了无比的本地Petrov-Galerkin方法和网上弱弱方法,具有更好的准确性和广泛的适用性。本文提出了一种基于测试功能的改进的无丝肌强弱耦合方法。散射节点用于表示问题域和边界,并且通过移动最小二乘法构造形状函数。对于本地集成域不与全局衍生边界相交的节点,用于构造基本方程的无网格强大的方法。对于其他节点,使用无网格局部弱公式方法。指数函数用作弱公式表达式的测试功能。通过改变指数函数的系数,控制测试函数的形状,并且减少了对计算精度的影响域大小的变化的影响。案例分析表明,该方法具有更高的准确性和更好的收敛效果,而不是传统的无网格方法。该方法具有高精度和计算效率,在加工电气数值计算中,具有强烈的涩味。该方法具有比传统方法更好的应用前景。

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