Fiber Orientation optimization and topology optimization for thermal buckling of laminated plates

IF 2.2 3区 工程技术 Q2 MECHANICS
Changfu Hu
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引用次数: 0

Abstract

This work aims to optimize the topology and fiber orientations of symmetric laminated composite plates. The goal is to maximize their stability under thermal loading while meeting volume fraction constraints. First, lamination parameters and densities are set as design variables to determine fiber orientations and topological shapes. A composite optimization model is established based on penalty theory. The method of moving asymptotes (MMA) algorithm is used to obtain the optimum lamination parameters and topological shapes. Next, leveraging the relationship between lamination parameters and fiber orientations, the solution of nonlinear equations is reformulated as a least-squares optimization problem. The Levenberg–Marquardt algorithm is then applied to determine the fiber orientations. Finally, the effectiveness of the proposed method is verified through optimization examples of four-sided and opposite-sided clamped laminate plates.

层压板热屈曲的纤维方向优化和拓扑优化
本工作旨在优化对称层合复合材料板的拓扑结构和纤维取向。目标是在满足体积分数限制的同时,最大限度地提高它们在热载荷下的稳定性。首先,层压参数和密度设置为设计变量,以确定纤维的取向和拓扑形状。基于惩罚理论建立了复合优化模型。采用移动渐近线法(MMA)算法获得了最优的层合参数和拓扑形状。其次,利用层压参数与纤维取向之间的关系,将非线性方程的解重新表述为最小二乘优化问题。然后应用Levenberg-Marquardt算法来确定光纤的方向。最后,通过四边和对边夹紧层压板的优化算例验证了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.40
自引率
10.70%
发文量
234
审稿时长
4-8 weeks
期刊介绍: Archive of Applied Mechanics serves as a platform to communicate original research of scholarly value in all branches of theoretical and applied mechanics, i.e., in solid and fluid mechanics, dynamics and vibrations. It focuses on continuum mechanics in general, structural mechanics, biomechanics, micro- and nano-mechanics as well as hydrodynamics. In particular, the following topics are emphasised: thermodynamics of materials, material modeling, multi-physics, mechanical properties of materials, homogenisation, phase transitions, fracture and damage mechanics, vibration, wave propagation experimental mechanics as well as machine learning techniques in the context of applied mechanics.
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