Global design optimization for aerodynamics and rocket propulsion components

Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial in sight into engineering processes involving fluid flows, and can be Fruitful ly utilized to help improve the design of practical devices. In particular. rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performa nce, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utili ze the information collected from various sources and by different tools. T hese methods offer multi-criterion optimization, handle the existence of mu ltiple design points and trade-offs via insight into the entire design spac e, can easily perform tasks in parallel, and are often effective in filteri ng the noise intrinsic to numerical and experimental data. However, a succe ssful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design vari ables, and methods for predicting the model performance. In this article, w e review recent progress made in establishing suitable global optimization techniques employing neural-network- and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level technique s, and assessment of relative performance between polynomials and neural ne tworks. Examples drawn From wing aerodynamics, turbulent diffuser flows, ga s-gas injectors, and supersonic turbines are employed to help demonstrate t he issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design practices and the need for fu ture research are identified. (C) 2001 Published by Elsevier Science Ltd. A ll rights reserved.