An optimization method for dynamics of structures with repetitive component patterns

Abstract

The occurrence of dynamic problems during the operation of machinery may have devastating effects on a product. Therefore, design optimization of these products becomes essential in order to meet safety criteria. In this research, a hybrid design optimization method is proposed where attention is focused on structures having repeating patterns in their geometries. In the proposed method, the analysis is decomposed but the optimization problem itself is treated as a whole. The model of an entire structure is obtained without modeling all the repetitive components using the merits of the Component Mode Synthesis method. Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques: Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP). GAs are utilized to increase the chance of finding the location of the global optimum and since this optimum may not be exact, SQP is employed afterwards to improve the solution. A theoretical test problem is used to demonstrate the method.

Dynamic substructuring and reanalysis methods in a surrogate-based design optimization environment

“…The possible overfitting problems are tried to be prevented utilizing the Bayesian regularization of Mackay (1992). Details about the surrogate modeling approach can be found in (Akçay Perdahcıoglu et al 2009). …”

“…Accordingly, the number of the FE analysis calls increases significantly. In order to reduce this computational burden, an SBO method is proposed by Akçay Perdahcıoglu et al (2009) for optimizing the dynamic behavior of structures where global approximations are utilized as surrogates. In the method, CraigBampton (CB) method is used for offering solutions to one of the major difficulties in SBO, the analysis time.…”

“…Genetic Algorithms and Sequential Quadratic Programming are the numerical optimization methods employed in MLHO. In this research, the SBO method proposed by Akçay Perdahcıoglu et al (2009) is coupled with a novel reanalysis approach (Akçay Perdahcıoglu et al 2010) with which it is aimed to increase the computational efficiency of the SBO strategy further.…”

Low cost metamodel for robust design of periodic nonlinear coupled micro-systems

“…Bouazizi et al [7] associated the equivalent linearization method with a reduction basis enriched by static residual vectors accounting for parametric modifications and localized nonlinearities. In [8] the CB transformation is enriched with a set of static residual vectors calculated using a priori knowledge of the initial component designs. In [9], to take into account geometrical nonlinearities, the CMS method is enriched by adding the most important modal derivatives, describing second order nonlinear contributions of vibration modes perturbed with the shapes of some others.…”