Rotor Blade Design Optimization with Airfoil Consideration Using Advanced Reduced Order Models

Abstract

This paper introduces ABC2, an advanced framework for rotor blade design optimization that can effectively consider the airfoil shape variations during optimization process. A major component of this framework is an reduced-order model (ROM) that leverages deep-neural-network techniques both for airfoil parameterization and performance prediction. Utilizing the UIUC airfoil database and a two-dimensional unsteady Reynolds-averaged Navier-Stokes (URANS) solver, the ROM can effectively control the airfoil shapes and predict the resulting aerodynamic performance across the wide range of flow conditions. A comprehensive aerodynamic solver is incorporated for blade design optimization. Enhancement of the fidelity of the comprehensive solver is achieved through the integration of a three-dimensional URANS solver, which also plays a crucial role in analyzing the aerodynamics of the optimized blade and uncovering its underlying physics. The competence of the present framework is demonstrated by a hovering optimization problem, yielding satisfactorily optimized blades and identifying the underlying physics.