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The smoothed finite element method for time-dependent mechanical responses of MEE materials and structures around Curie temperature

机译:静脉温度周围Mee材料和结构的时间依赖性机械响应的平滑有限元方法

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摘要

Investigating the mechanical behavior of composites materials under harsh environments is of great importance. A standard finite element method (FEM) is still one of the main methods to investigate properties and mechanical responses of magnetoelectro-elastic (MEE) materials and structures. However, the computational features of FEM lead to some limitations. In this paper, we proposed the cell-based smoothed finite element method (CS-FEM) for superior calculations, in which the strain smoothing technique is introduced into FEM. We showed, that CS-FEM possesses high accuracy, low mesh restriction, much less computational-cost than FEM, and stronger handling ability when encountering strong mesh distortions and large deformations. CS-FEM with modified Newmark scheme was defined to show an effect of the high-temperature environment and mechanical load on the time-dependent responses of MEE structures. The convergence, effectiveness, and efficiency of CS-FEM were validated via the numerical examples for simplified bi-layer transducer and an energy harvester as MEE intelligent structures. Additionally, the transient performance of intelligent structures around Curie temperature was comprehensively discussed. The presented CS-FEM and obtained results can be used for future studies of the coupled multi-physical problems as well as investigations of the accuracy of intelligent structure models subjected to extreme conditions. (C) 2020 Elsevier B.V. All reserved.
机译:研究在恶劣环境下复合材料材料的力学行为非常重要。标准有限元方法(FEM)仍然是研究磁电弹性(MEE)材料和结构的性质和机械响应的主要方法之一。然而,有限元的计算特征导致一些限制。在本文中,我们提出了基于细胞的平滑有限元方法(CS-FEM),用于卓越的计算,其中将应变平滑技术引入FEM中。我们显示,CS-FEM具有高精度,低网格限制,比FEM的计算成本低得多,并且在遇到强烈网眼失真和大变形时更强的处理能力。具有改进的纽马克方案的CS-FEM被定义为显示高温环境和机械负载对MEE结构的时间依赖性响应的影响。 CS-FEM的收敛,有效性和效率通过用于简化的双层换能器和能量收割机作为MEE智能结构的能量收割机进行验证。另外,综合地讨论了静脉温度周围的智能结构的瞬态性能。所呈现的CS-FEM和获得的结果可用于对耦合多身体问题的未来研究以及对受极端条件进行智能结构模型的准确性的研究。 (c)2020 Elsevier B.v.所有保留。

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