Cylindrical shell panels are commonly used for the lightweight design of thin-walled structures in the aerospace engineering. In this work, the snap-through buckling of composite cylindrical shell panels subjected to ...
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Cylindrical shell panels are commonly used for the lightweight design of thin-walled structures in the aerospace engineering. In this work, the snap-through buckling of composite cylindrical shell panels subjected to lateral loads is calculated and designed. We present a novel method to extract the essential features of the snap-through response using a fast path-following scheme, and subsequently apply them as inputs to the stacking sequence optimization. In the path-following scheme, the full snap-through response can be achieved efficiently based on the finite element perturbation method, meanwhile its numerical accuracy is well guaranteed by the necessary corrections on residual forces. The essential features of the snap-through response, i.e. the load-limit point, critical deflection and jump deflection, are selected to be either the objective or the constraint in the stacking sequence optimization. The stacking sequence optimization using the genetic algorithm can be realized considering the severe geometrical nonlinearities of mechanics responses, benefiting from the high efficiency and robustness of a single function call. Composite cylindrical shell panels with various geometry/lamination configurations and load/displacement boundary conditions, are considered in the stacking sequence optimizations, to validate the good performance of the proposed method.
We propose a new proximal path-following framework for a class of constrained convex problems. We consider settings where the nonlinear-and possibly nonsmooth-objective part is endowed with a proximity operator, and t...
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We propose a new proximal path-following framework for a class of constrained convex problems. We consider settings where the nonlinear-and possibly nonsmooth-objective part is endowed with a proximity operator, and the constraint set is equipped with a self-concordant barrier. Our approach relies on the following two main ideas. First, we reparameterize the optimality condition as an auxiliary problem, such that a good initial point is available;by doing so, a family of alternative paths toward the optimum is generated. Second, we combine the proximal operator with path-following ideas to design a single-phase, proximal path-following algorithm. We prove that our algorithm has the same worst-case iteration complexity bounds as in standard path-following methods from the literature but does not require an initial phase. Our framework also allows inexactness in the evaluation of proximal Newton directions, without sacrificing the worst-case iteration complexity. We demonstrate the merits of our algorithm via three numerical examples, where proximal operators play a key role.
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