Optimal design of a honeycomb core composite sandwich panel using evolutionary optimization algorithms

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• 作者：Meghdad Gholami ; ^{gh.meghdad@yahoo.com" class="auth_mail" title="E-mail the corresponding author} ; Reza Akbari Alashti ^{raalashti@nit.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Alireza Fathi ^{fathi@nit.ac.ir" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Honeycomb ; Sandwich panel ; Particle swarm optimization ; Niching Memetic PSO ; Evolutionary optimization algorithms ; Locally Informed Particle Swarm (LIPS)
• 刊名：Composite Structures
• 出版年：2016
• 出版时间：1 April 2016
• 年：2016
• 卷：139
• 期：Complete
• 页码：254-262
• 全文大小：2025 K

文摘

The present paper deals with the optimal design of a composite sandwich panel with honeycomb core structure using particle swarm optimization (PSO) technique. The face sheets of sandwich panel are considered to be thin and the sandwich panel is subjected to a uniformly distributed normal load. The Navier type solution and the closed-form expressions for the macroscopic in-plane elastic constants of the honeycombs are used to predict the deflection of the panel. Also, the maximum stress in the composite sandwich panel is determined using the macroscopic stress–strain relation of the honeycomb core and the plate. Niching Memetic PSO (NMPSO) and Locally Informed Particle Swarm (LIPS) variants of PSO are examined to minimize the weight of the panel. Numerical results showed that the optimal geometry of the honeycomb cell has this property that its radius and thickness converge to their lower bounds while its length converges to its upper bound. This means that to have an optimal panel, each cell should have the allowable minimum cross section (highest number of cells) and maximum allowable length. Moreover the effects of panel aspect ratio, cell width and applied load were examined. Also, the numerical results confirm the efficiency and effectiveness of the NMPSO in finding optimal solution on the constrained and unconstrained objective functions.