Global design optimization for fluid machinery applications

摘要

Recent experiences in utilizing the gobal optimization methodology, based on polynomial and neural network techniques, for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numercial and experimental data. Another adantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. Examples of applications, selected from rocket propulsion components, including a supersonic turbine and an injector element, and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.