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Publication Title | UTILIZING OBJECT-ORIENTED DESIGN TO BUILD ADVANCED OPTIMIZATION STRATEGIES WITH GENERIC IMPLEMENTATION

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UTILIZING OBJECT-ORIENTED DESIGN TO BUILD ADVANCED OPTIMIZATION STRATEGIES WITH GENERIC IMPLEMENTATION

M.S. Eldred*, W.E. Hart†, W.J. Bohnhoff‡, V.J. Romero§, S.A. Hutchinson¶, and A.G. Salinger#

Abstract

The benefits of applying optimization to computational models are well known, but their range of widespread application to date has been limited. This effort attempts to extend the disciplinary areas to which optimization algorithms may be readily applied through the development and application of advanced optimization strategies capable of handling the computational difficulties associated with complex simulation codes. Towards this goal, a flexible software framework is under continued development for the application of optimization techniques to broad classes of engineering applications, including those with high computational expense and nonsmooth, nonconvex design space features. Object-oriented software design with C++ has been employed as a tool in providing a flexible, extensible, and robust multidisciplinary toolkit that establishes the protocol for interfacing optimization with computationally-intensive simulations. In this paper, demonstrations of advanced optimization strategies using the software are presented in the hybridization and parallel processing research areas. Performance of the advanced strategies is compared with a benchmark nonlinear programming optimization.

*Senior Member of Technical Staff (SMTS), Structural Dynamics Dept., Mail Stop 0439, AIAA member.

†SMTS, Algorithms and Discrete Math Dept., Mail Stop 1110.

‡SMTS, Intelligent Systems Dept., Mail Stop 1177.

§SMTS, Thermal Sciences Dept., Mail Stop 0835.

¶SMTS, Parallel Computational Sciences Dept., Mail Stop 1111.

#Research Fellow, Parallel Computational Sciences Dept., Mail Stop 1111.

**

P.O. Box 5800, Albuquerque, NM 87185, USA. This work performed at Sandia National Laboratories supported by the U.S. Department of Energy under contract DE-AC04-94AL85000. This paper is declared a work of the U.S. Government and is not subject to copyright protection in the United States.

Introduction

Computational methods developed in fluid mechanics, structural dynamics, heat transfer, nonlinear large-deformation mechanics, manufacturing and material processes, and many other fields of engineering can be an enormous aid to understanding the complex physical systems they simulate. Often, it is desired to utilize these simulations as virtual prototypes to improve or optimize the design of a particular system. The optimization effort at Sandia National Laboratories seeks to enhance the utility of this broad class of computational methods by enabling their use as design tools, so that simulations may be used not just for single-point predictions, but also for improving system performance in an automated fashion. System performance objectives can be formulated to minimize weight or defects or to maximize performance, reliability, throughput, reconfigurability, agility, or design robustness (insensitivity to off-nominal parameter values). A systematic, rapid method of determining these optimal solutions will lead to better designs and improved system performance and will reduce dependence on hardware and testing, which will shorten the design cycle and reduce development costs.

Towards these ends, this optimization effort has targeted the needs of a broad class of computational methods in order to provide a general optimization capability. Much work to date in the optimization community has focused on applying either gradient- based techniques to smooth, convex, potentially expensive problems1 or global techniques to nonconvex but inexpensive problems2. When the difficulties of high computational expense and nonsmooth, nonconvex design spaces are coupled together, standard techniques may be ineffective and advanced strategies may be required. Moreover, since the challenges of each application are frequently very different, generality and flexibility of the advanced strategies are key concerns.

The coupling of optimization with complex computational methods is difficult, and optimization algorithms often fail to converge efficiently, if at all. The difficulties arise from the following traits, shared by many computational methods:

1. The time required to complete a single function eval-

Sandia National Laboratories** Albuquerque, NM 87185

1

American Institute of Aeronautics and Astronautics

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