Application of Sequential Convex Programming to Large-Scale StructuralOptimization Problems

摘要

Structural design problems are often modeled using finite element methods. Suchmodels are often characterized by constraint functions that are not explicitly defined in terms of the design variables. These functions are typically evaluated through numerical finite element analysis (FEA). Optimizing large-scale structural design models requires computationally expensive FEAs to obtain function and gradient values. An optimization approach which uses the SCP sequential convex programming algorithm of Zillober, integrated as the optimizer in the Automated Structural Optimization System (ASTROS), is tested. The traditional approach forms an explicitly defined approximate subproblem at each design iteration that is solved using the method of modified feasible directions. In an alternative approach, the SCP subroutine is called to formulate and solve the approximate subproblem. The SCP method is an implementation of the Method of Moving Asymptotes algorithm with five different asymptote determination strategies. This study investigates the effect of different asymptote determination strategies and constraint retention strategies on computational efficiency. The approach is tested on three large-scale structural design models, including one with constraints from multiple disciplines. Results and comparisons to the traditional approach are given. The largest of the three models, which had 1527 design variables and 6124 constraints, was solved to optimality with ASTROS for the first time using a mathematical programming method. The structural weight of the resulting design is 9% lower than the previously recorded minimum weight.