Sequential Approximate Optimization (SAO) is a class of methods available for the multidisciplinary design optimization (MDO) of complex systems that are composed of several disciplines coupled together. One of the approaches used for SAO, is based on a quadratic response surface approximation, where zero and first order information are required. In these methods, designers must generate and query a database of order O(n²) in order to compute the second order terms of the quadratic response surface approximation. As the number of design variables grows, the computational cost of generating the required database becomes a concern. In this paper, we present an new approach in which we require just O(n) parameters for constructing a second order approximation. This is accomplished by transforming the matrix of second order terms into the canonical form. The method periodically requires an order O(n²) update of the second order approximation to maintain accuracy. Results show

The use of optimization in a simulation based design environment has become a common trend in industry today. Computer simulation tools are common place in many engineering disciplines, providing the designers with tools to evaluate a design's performance without building a physical prototype. This has triggered the development of optimization techniques suitable for dealing with such simulations. One of these approaches is known as sequential approximate optimization. In sequential approximate minimization a sequence of optimizations are performed over local response surface approximations of the system. This paper details the development of an interior point approach for trust region managed sequential approximate optimization. The interior point approach will insure that approximate feasibility is maintained throughout the optimization process. This facilitates the delivery of a usable design at each iteration when subject to reduced design cycle time constraints. In order to deal with infeasible starting points, homotopy methods are used to relax constraints and push designs toward feasibility. Results of application studies are presented, illustrating the applicability of the proposed algorithm.

Simulation-based design has become a major tool in the design of automotive, aerospace and consumer products. Designers are faced with the continuous challenge of reducing manufacturing costs and design cycle times while improving the system's performance and reliability. Simulation-based design plays an increasingly prominent role in facilitating the conceptualization and realization of products under these competitive conditions. Single discipline simulations used for analysis are being coupled together to create complex coupled simulation systems. This investigation focuses on the development of methodologies that help designers reduce the cost of using optimization to manage the simulation based design process. The computational cost of executing a single complex coupled simulation and the total number of simulations required per iteration are the two factors which most influence an optimization framework's design. Original contributions