Design Optimization of Turbomachinery by Artificial Neural Networks as a Meta-model

Abstract

Artificial Neural Networks (ANN) has a potential to be used for making decisions instead of human designers. Well-trained ANN is helpful tools to eliminate the work for making decisions and can be effective tools for putting order the complicated parametric study. The genetic algorithms are one of global design search systems. It is important to provide enough generations for the design search, however the evaluation of each individual costs a lot. ANN is one of the candidates of an alternative model (meta-model) which drastically reduce the number of actual CFD calculations, and a highly reliable database can be constructed by CFD validations. Each time the database is reconstructed, the prediction accuracy of the meta-model increases, enabling efficient design search for the optimal one. This paper introduces the multi-objective optimization system using the ANN assisted genetic algorithms, and optimization of turbomachinery design is performed with reducing the simulation costs.