نویسندگان | احمدرضا قاسمی,محمدهادی حاج محمد,احسان غفاری جونقانی |
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همایش | International Conference on Experimental Solid Mechanics (X-Mech 2016) |
تاریخ برگزاری همایش | 2016-02-16 - 2016-02-17 |
محل برگزاری همایش | 1 - تهران |
ارائه به نام دانشگاه | دانشگاه علم و صنعت ایران |
نوع ارائه | سخنرانی |
سطح همایش | بین المللی |
چکیده مقاله
In the present research, buckling optimization of grid stiffened composite shell under external hydrostatic pressure has been investigated. At first, buckling analysis of grid stiffened composite shell has been done to determine fitness function of optimization. So energy method has been used for determining the global buckling pressure of a stiffened composite cylindrical shell. This is done by considering the moment effect of the stiffeners in addition to force analysis performed on a unit cell. The stiffness of the stiffeners was superimposed with the stiffness of the shell to obtain the equivalent stiffness parameters of the whole shell. The nonlinear strain–displacement equations and the Ritz energy method are used to obtain the buckling pressure of the cylinder. The total potential energy of the shell is composed of the strain energy and the work done by the external pressure. For optimization process, some parameters can be used to increase mechanical performance of composite structures. Fiber orientations, stacking sequence of laminate, the height of the rib and the angle of the rib toward longitudinal direction are the variables which have been used to maximize critical buckling pressure. By considering the variables and buckling as the fitness function, genetic algorithm has been used to reach optimal design of the grid stiffened composite shell. The results show that optimization problem has been converged in the 100 generations and optimization parameters such as cross over and mutation have proper value. Finally, the best model which has the maximum critical buckling pressure has been presented.
کلید واژه ها: Optimization, buckling pressure, grid stiffened composite shell, genetic algorithm.