Effective Parallel Optimization of Complex Computer Simulations
Evin J. Cramer, Joerg M. Gablonsky
September 1, 2004
Abstract
Computer simulations are an integral part of many of today's design processes. This paper describes a method for design exploration and optimization that is firstly mathematically suitable for functions calculated with complex computer simulations and secondly made practical with parallel computing. These assertions are confirmed with numerical results. As shown with several examples, using the method also has side benefits in data consistency, use of engineering time, and in explaining results in context.
Introduction
Computer simulations are an integral part of many of today's design processes. They can be used to explore design space to discover what is possible, what are critical features of a design, and what are limiting constraints. Furthermore computer simulations can be used as augmentation for the traditional build and test cycle. In this capacity they can substitute for early tests and as a guide to decide what to build and test, thereby reducing costs, and design time.
This paper presents an approach for exploring design objectives and ultimately optimizing these objectives based on complex computer simulations. We will be focusing on deterministic physics based simulations. For example, to calculate the aerodynamic properties of a wing at a given light condition, a computational fluid dynamics (CFD) code can be used. It can take anywhere from a few minutes to two hours or more tosimulate such a light condition. To determine the shape of a wing with desired properties many runs of the CFD code are needed. Clearly, the complexity and expense of this problem grows rapidly when we expand from a single discipline to a system level trade involving many disciplines and computer simulations, both in the run time, and in the memory requirements. Other common properties of computer simulations are inherent noise, and failure to find a solution at unexpected design conditions. These failures might be the result of physical properties, i.e. a wing design that does not provide enough lift to fly, or might be caused by the simulation software directly. In the following discussions we will treat both types of failures the same way, since we cannot easily distinguish between them.
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