Optimization of dynamic buckling for sandwich nanocomposite plates with sensor and actuator layer based on sinusoidal-visco-piezoelasticity theories using Grey Wolf algorithm
Restricted accessResearch articleFirst published online January, 2020
Optimization of dynamic buckling for sandwich nanocomposite plates with sensor and actuator layer based on sinusoidal-visco-piezoelasticity theories using Grey Wolf algorithm
Optimization of embedded piezoelectric sandwich nanocomposite plates for dynamic buckling analysis is presented in this work based on Grey Wolf algorithm. The Grey Wolf algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In addition, the main steps of hunting, searching for prey, encircling prey, and attacking prey are employed. The structure is composed of a laminated functionally graded-carbon nanotubes reinforced layers as core integrated with sensor and actuator layers considering structural damping effects. Two-dimensional magnetic and 3D electric fields are applied to core and piezoelectric layers, respectively. Sinusoidal shear deformation theory is utilized for obtaining the motion equations and differential quadrature method is applied for solution. Also, a proportional–derivative controller is employed to control the dynamic behavior of the structure. Finally, the optimum designs for the structure are evaluated using proposed Grey Wolf algorithm based on the geometrical parameters of plate, applied voltage, controller parameters, volume fraction of carbon nanotubes, spring, and shear constants of foundation. Numerical results indicate that by applying the positive voltage and transverse magnetic field the optimum dimensionless frequency of the system decreases.
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