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On the development of adaptive random differential quadrature method with an error recovery technique and its application in the locally high gradient problems

机译:具有误差恢复技术的自适应随机微分正交方法的发展及其在局部高梯度问题中的应用

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摘要

An adaptive numerical method called, the adaptive random differential quadrature (ARDQ) method is presented in this paper. In the ARDQ method, the random differential quadrature (RDQ) method is coupled with a posteriori error estimator based on relative error norm in the displacement field. An error recovery technique, based on the least square averaging over the local interpolation domain, is proposed which improves the solution accuracy as the spacing, h → 0. In the adaptive refinement, a novel convex hull approach with the vectors cross product is proposed to ensure that the newly created nodes are always within the computational domain. The ARDQ method numerical accuracy is successfully evaluated by solving several 1D, 2D and irregular domain problems having locally high gradients. It is concluded from the convergence values that the ARDQ method coupled with error recovery technique can be effectively used to solve the locally high gradient initial and boundary value problems. Keywords Meshless method - Adaptive random differential quadrature - Error recovery technique - Locally high gradient problems - Differential quadrature method
机译:本文提出了一种自适应数值方法,称为自适应随机微分正交(ARDQ)方法。在ARDQ方法中,随机微分正交(RDQ)方法与位移场中基于相对误差范数的后验误差估计器结合。提出了一种基于局部插值域上最小二乘平均的错误恢复技术,该技术提高了间距h→0时的求解精度。在自适应细化中,提出了一种新的带有向量叉积的凸包方法。确保新创建的节点始终在计算域内。通过解决几个具有局部高梯度的1D,2D和不规则域问题,成功评估了ARDQ方法的数值精度。从收敛值可以得出结论,结合误差恢复技术的ARDQ方法可以有效地解决局部高梯度初始值和边界值问题。无网格方法-自适应随机微分正交-误差恢复技术-局部高梯度问题-微分正交方法

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