Compressor airfoil optimization method driven by data-mechanism integration based on evolutionary multi-tasking algorithm

Abstract

To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 × D aerodynamic performance evaluations, where D denotes the number of design variables.