Robust topology optimization of a shallow arch using stochastic perturbation

In this paper, design optimization of a shallow arch is performed by means of density-based topology optimization. The design obtained by classical topology optimization is highly sensitive to geometric imperfections when geometric nonlinearity is taken into account. A robust approach to topology optimization is therefore developed in order to find a design that is both efficient and robust. A Total Lagrangian formulation for large displacements is adopted in order to incorporate geometric nonlinear effects in the topology optimization problem. Robust optimization is considered within a probabilistic framework: geometric imperfections in the design domain are modeled by a vector-valued Gaussian random field, and the variability of the performance is accounted for by minimizing a weighted sum of the mean and standard deviation in the robust optimization problem. The robust objective function is approximated by a second-order perturbation method during the optimization in order to avoid multiple calls to the nonlinear finite element solver per design iteration.

ISBN:9789609999465

Publication year:2014