Date of Award


Document Type


Degree Name

Master of Science


Department of Operational Sciences

First Advisor

James W. Chrissis, PhD


Large-scale structural optimization problems are often difficult to solve with reasonable efficiency and accuracy. Such problems are often characterized by constraint functions which are not explicitly defined. Constraint and gradient functions are usually expensive to evaluate. An optimization approach which uses the NLPQL sequential quadratic programming algorithm of Schittkowski, integrated with the Automated Structural Optimization System ASTROS is tested. The traditional solution approach involves the formulation and solution of an explicitly defined approximate problem during each iteration. This approach is replaced by a simpler approach in which the approximate problem is eliminated. In the simpler approach, each finite element analysis is followed by one iteration of the optimizer. To compensate for the cost of additional analyses incurred by the elimination of the approximate problem, a much more restrictive active set strategy is used. The approach is applied to three large structures problems, including one with constraints from multiple disciplines. Results and algorithm performance comparisons are given. Although not much computational efficiency is gained, the alternative approach gives accurate solutions. The largest of the three problems, which had 1527 design variables and 6124 constraints was solved with ASTROS for the first time using a direct method. The resulting design represents the lowest weight feasible design recorded to date.

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The author's Vita page is omitted.