Efficient structural optimization using reanalysis and sensitivity reanalysis
Optimization of large-scale structures using conventional formulations often involves much computational effort. Repeated solutions of the analysis and sensitivity analysis equations usually require most of this effort. The computational cost may become prohibitive in large-scale structures having complex analysis models. To alleviate this difficulty, various procedures are integrated in this study into a general optimization approach. The approach is suitable for different classes of response types and optimization methods, including linear and non-linear response; static and dynamic response; direct and gradient optimization methods. Combined approximations are used for reanalysis and repeated sensitivity analysis. The advantage is that the efficiency of local approximations and the improved quality of global approximations are combined to obtain effective solution procedures. Approximate reanalysis and finite-difference sensitivity reanalysis are considered for each intermediate design during the solution process. Reductions in the computational effort may reach several orders of magnitude. Typical numerical examples show that the results achieved by the approach presented are similar to those obtained by exact reanalysis and sensitivity analysis.
structural optimization sensitivity analysis combined approximations approximate reanalysis structural reanalysis