Aero-Structural Design Optimization of a Transonic Fan Rotor Using an Adaptive POD-Based Hybrid Surrogate Model

Abstract

In this study, an optimization framework for turbomachinery blades using a hybrid surrogate model assisted by proper orthogonal decomposition (POD) is introduced and then applied to the aero-structural multidisciplinary design optimization of a transonic fan rotor, NASA Rotor 67. The rotor blade is optimized through blade sweeping controlled by Gaussian radial basis functions. Calculations of aerodynamic and structural performance are achieved through computational fluid dynamics and computational structural mechanics. With a number of performance snapshots, singular value decomposition is employed to extract the basis modes, which are then used as the kernel functions in training the POD-based hybrid model. The inverse multi-quadratic radial basis function is adopted to construct the response surfaces for the coefficients of kernel functions. Aerodynamic design optimization is first investigated to preliminarily explore the impact of blade sweeping. In the aero-structural optimization, the aerodynamic performance, and von Mises stress are considered equally important and incorporated into one single objective function with different weight coefficients. The results are given and compared in detail, demonstrating that the average stress is dependent on the aerodynamic loading, and the configuration with forward sweeping on inner spans and backward sweeping on outer spans is the most effective for increasing the adiabatic efficiency while decreasing the average stress when the total pressure ratio is constrained. Through this study, the optimization framework is validated and a practical configuration for reducing the stress in a transonic fan rotor is provided.