An accurate and stable space-time radial basis function collocation method for transient coupled thermo-mechanical analysis

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xiaohan Jing , Lin Qiu , Hong Zhao , Zeqian Zhang , Yaoming Zhang , Yan Gu
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引用次数: 0

Abstract

In this study, an accurate and stable space-time radial basis function (STRBF) collocation method is developed to solve two- and three-dimensional dynamic coupled thermo-mechanical problems. The proposed method enhances numerical precision by strategically positioning source points beyond the computational domain through space-time scaling factors. To address the challenge of selecting the optimal shape parameter, a new coupled STRBF is formulated by combining the Multiquadric function with the conical spline. Furthermore, a multiscale computational strategy is implemented to mitigate numerical instability in the resulting linear system. The effectiveness of the developed approach is demonstrated through four numerical examples involving complex geometries and different initial and boundary conditions. Numerical results show that, compared to the traditional RBF collocation method, the developed scheme not only enhances computational accuracy but also significantly reduces the dependence on the choice of shape parameter, making it a promising method for dealing with transient coupled thermo-mechanical problems.
一种精确稳定的瞬态耦合热-力分析时空径向基函数配置方法
本文提出了一种精确、稳定的时空径向基函数(STRBF)配置方法,用于求解二维和三维动态耦合热-力问题。该方法通过时空尺度因子对计算域外的源点进行战略性定位,提高了数值精度。为了解决最优形状参数的选择问题,将多重二次函数与圆锥样条函数相结合,建立了一种新的耦合STRBF。此外,采用了一种多尺度计算策略来减轻所得到的线性系统的数值不稳定性。通过四个涉及复杂几何和不同初始和边界条件的数值算例,证明了所开发方法的有效性。数值结果表明,与传统的RBF配置方法相比,该方法不仅提高了计算精度,而且显著降低了对形状参数选择的依赖,是一种很有前途的处理瞬态耦合热-力问题的方法。
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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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