Knowledge Based 2D Blade Design Using Multi-Objective Aerodynamic Optimization and a Neural Network

Abstract

This paper presents a method to obtain optimized 2D blade sections using expert knowledge, a multi-criteria optimization approach and a neural network in an automated process. A special focus is put on neural networks, which are used to capture the complex correlations between aerodynamic and geometric parameters. The multi-criteria optimization is used to generate optimal training data for the neural network. The main objective of this investigation is to generate 2D blade sections from scratch including loss prediction using through flow quantities and a neural network approach without any CFD computations. First results are very promising in terms of computation time, model capacities and performance prediction of the neural network.