Multi-disciplinary optimization of a contra-rotating fan

Abstract

This paper presents the shape optimization of the blades of a contra-rotating fan according to aerodynamic, structural and aeroacoustic criteria. The prediction of wake-interaction and trailing-edge noise emission is integrated into the optimization process by means of RANS-based hybrid methods. These methods split the description of the flow field, which is made by computational fluid dynamics, from the quantification of the source of noise and of its propagation, obtained using analytic formulations. The optimization is performed by an evolutionary algorithm assisted by an artificial neural network. The sensitivity analysis of the design parameters and of the constraints used in the optimization process provides a new perspective on the multi-objective efficiency-noise optimization approach, which will be increasingly used in turbomachinery design in the future.