جستجوی مقالات مرتبط با کلیدواژه « evolutionary structural optimization » در نشریات گروه « مکانیک »

Recently, some researchers proposed a new algorithm for the bidirectional evolutionary structural optimization (BESO) in which adding and removing of elements is controlled by a signal parameter of removal ratio of volume (or weight). Nevertheless, their BESO improvement suffers from some restrictions like premature stopping of the iterative process due to the closeness of the adding and removing coefficients. In this study, the BESO method is modified such that eliminates the previous restrictions and increase its efficiency significantly. Several examples are presented for structural optimization of a plate in two states of elastic and elastoplastic with stiffness and stress criteria. From the three examples, one can conclude that the modified BESO algorithm is more robust than the ESO and BESO algorithm previously proposed. It is also concluded the maximum stress and the performance index are improved in comparison with the case when the signal parameter is the same. Overall, the proposed modification prevent the premature termination of the optimization process when the optimization process is based on the hard kill method with both adding and removing the elements.

In many structural components, it is usually desirable to have a minimum deflection at specific points. To develop such stiff structures, different optimization methods have been employed by researchers. In this study, the Simulated Annealing (SA) method is used for maximizing the stiffness of two-dimensional structures. The effectiveness of the SA is then evaluated by comparing its results with that of the Evolutionary Structural Optimization (ESO). Three examples are presented including the square plate, the rectangular plate and the MBB beam. These examples are conducted for different loading and mesh refinement. Furthermore, to have a practical shape, the checkerboard pattern is eliminated in the optimization process. The results show that in the square plate with 3 thicknesses the solution obtained with the SA method is 7% better than the Evolutionary Structural Optimization solution. For rectangular plate, SA solution is 4% better than the ESO solution. For the MBB beam the ESO solution is about 3% better than the SA solution. However, the optimum shapes obtained from both methods are quite similar. It can be concluded that the SA is an effective method for optimizing the stiffness of 2D structures when a better accuracy is needed.

Topology optimization of structure seeks to achieve the best material distribution in the Pre-determined design domain. In this paper, the effect of design parameters contains length scale parameter and evolutionary volume ratio in improved bi-directional evolutionary structural optimization method with soft kill approach is discussed. The main aim of this method is searching for the stiffest structure with a given volume of material using finite element method. At each iteration of finite element analysis, sensitivity number is calculated for each individual element in design domain and then converted to the nodal sensitivity number. With Filter Scheme and using length scale, an improved sensitivity numbers is defined. This number is used as a criterion for rating each element in design domain and determining the addition and elimination (remove) of elements. To increase the convergence of the optimization process, the accuracy of the new elemental sensitivity numbers is improved by considering the sensitivity history. This method is convergent and mesh-independent and there are no checkerboard pattern and local solutions in optimal topologies. Using three design samples, a cantilever and classical beam and Michell type structure, affecting factors will be discussed on the final design of the structure. Change of length scale parameter makes various schemes in final structures in which with increasing this parameter, more iteration is needed for convergent solution. Reducing evolutionary volume ratio forms different and even asymmetric topologies. Better final topologies are obtained with higher evolutionary volume ratios.