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Laminate optimization of a composite stiffened panel based on surrogate model |
| LIU Zhe1, JIN Dafeng1, FAN Zhirui2 |
1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;
2. School of Mechanical and Power Engineering, North University of China, Taiyuan 030051, China |
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Abstract This study optimized the stacking sequence of stiffeners in a composite stiffened panel to maximize the buckling load of the panel assuming a constant mass panel. The number of finite element models was reduced by using a radial basis function neural network (RBF) as the surragate model with the lamination parameters as inputs to estimate the buckling load. The lamination input parameters reduced the nonlinearities of the objective function. Due to the irregular shape of the design space, the D-optimal method was used to determine the sample points for training the RBF. The model errors were reduced by constructing a zoomed RBF to enhance the RBF accuracy near the provisional optimal laminate. A numerical example shows the accuracy and efficiency of the RBF with the lamination parameters as inputs and how the model accuracy is increased by the zoomed RBF near the optimal region.
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| Keywords
composite
stiffened panel
optimization
genetic algorithm
surrogate model
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Issue Date: 15 July 2015
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[1] Irisarri F X, Laurin F, Leroy F H, et al. Computational strategy for multiobjective optimization of composite stiffened panels [J]. Composite Structures, 2011, 93(3): 1158-1167.
[2] Marín L, Trias D, Badalló P, et al. Optimization of composite stiffened panels under mechanical and hygrothermal loads using neural networks and genetic algorithms [J]. Composite Structures, 2012, 94(11): 3321-3326.
[3] Faggiani A, Falzon B G. Optimization strategy for minimizing damage in postbuckling stiffened panels [J]. AIAA Journal, 2007, 45(10): 2520-2528.
[4] Rikards R, Abramovich H, Kalnins K, et al. Surrogate modeling in design optimization of stiffened composite shells [J]. Composite Structures, 2006, 73(2): 244-251.
[5] Blom A W, Setoodeh S, Hol J M A M, et al. Design of variable-stiffness conical shells for maximum fundamental eigenfrequency [J]. Computers & Structures, 2008, 86(9): 870-878.
[6] Wang G G, Dong Zuoming, Aitchison P. Adaptive response surface method: A global optimization scheme for approximation-based design problems [J]. Engineering Optimization, 2001, 33(6): 707-734.
[7] Abouhamze M, Shakeri M. Multi-objective stacking sequence optimization of laminated cylindrical panels using a genetic algorithm and neural networks [J]. Composite Structures, 2007, 81(2): 253-263.
[8] Apalak M K, Yildirim M, Ekici R. Layer optimisation for maximum fundamental frequency of laminated composite plates for different edge conditions [J]. Composites Science and Technology, 2008, 68(2): 537-550.
[9] Todoroki A, Ishikawa T. Design of experiments for stacking sequence optimizations with genetic algorithm using response surface approximation [J]. Composite Structures, 2004, 64(3): 349-357.
[10] Todoroki A, Sasai M. Stacking sequence optimizations using GA with zoomed response surface on lamination parameters [J]. Advanced Composite Materials, 2002, 11(3): 299-318.
[11] De Aguiar P F, Bourguignon B, Khots M S, et al. D-optimal designs [J]. Chemometrics and Intelligent Laboratory Systems, 1995, 30(2): 199-210.
[12] Falzon B G, Stevens K A, Davies G O. Postbuckling behaviour of a blade-stiffened composite panel loaded in uniaxial compression [J]. Composites Part A: Applied Science and Manufacturing, 2000, 31(5): 459-468. |
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2015,
Vol. 55
