Parallel implementation of large-scale structural optimization

Abstract

Advances in computer technology and performance allow researchers to pose useful optimization problems that were previously too large for consideration. For example, NASA Langley Research Center is investigating the large structural optimization problems that arise in aircraft design. The total number of design variables and constraints for these nonlinear optimization problems is now an order of magnitude larger than anything previously reported. To find solutions in a reasonable amount of time, a coarse-grained parallel-processing algorithm is recommended. This paper studies the effects of problem size on sequential and parallel versions of this algorithm.

For initial testing of this algorithm, a hub frame optimization problem is devised such that the size of the problem can be adjusted by adding members and load cases. Numerous convergence histories demonstrate that the algorithm performs correctly and in a robust manner. Timing profiles for a wide range of randomly generated problems highlight the changes in the subroutine timings that are caused by the increase in problem size. The potential benefits and drawbacks associated with the parallel approach are summarized.