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http://dx.doi.org/10.12989/gae.2016.10.6.709

Probabilistic multi-objective optimization of a corrugated-core sandwich structure  

Khalkhali, Abolfazl (Automotive Simulation and Optimal Design Research Laboratory, School of Automotive Engineering, Iran University of Science and Technology)
Sarmadi, Morteza (Automotive Simulation and Optimal Design Research Laboratory, School of Automotive Engineering, Iran University of Science and Technology)
Khakshournia, Sharif (Automotive Simulation and Optimal Design Research Laboratory, School of Automotive Engineering, Iran University of Science and Technology)
Jafari, Nariman (Automotive Simulation and Optimal Design Research Laboratory, School of Automotive Engineering, Iran University of Science and Technology)
Publication Information
Geomechanics and Engineering / v.10, no.6, 2016 , pp. 709-726 More about this Journal
Abstract
Corrugated-core sandwich panels are prevalent for many applications in industries. The researches performed with the aim of optimization of such structures in the literature have considered a deterministic approach. However, it is believed that deterministic optimum points may lead to high-risk designs instead of optimum ones. In this paper, an effort has been made to provide a reliable and robust design of corrugated-core sandwich structures through stochastic and probabilistic multi-objective optimization approach. The optimization is performed using a coupling between genetic algorithm (GA), Monte Carlo simulation (MCS) and finite element method (FEM). To this aim, Prob. Design module in ANSYS is employed and using a coupling between optimization codes in MATLAB and ANSYS, a connection has been made between numerical results and optimization process. Results in both cases of deterministic and probabilistic multi-objective optimizations are illustrated and compared together to gain a better understanding of the best sandwich panel design by taking into account reliability and robustness. Comparison of results with a similar deterministic optimization study demonstrated better reliability and robustness of optimum point of this study.
Keywords
sandwich structure; corrugated core; optimization; probabilistic; genetic algorithm; finite element method;
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