A hybrid data-driven framework for loss prediction of MCA airfoils

Abstract

The Multi Circular Arc airfoils family has gained significant popularity in axial compressor design due to its capability to achieve higher pressure ratios with lower losses compared to conventional NACA airfoils. However, modeling their aerodynamic performance for transonic axial compressors remains a challenging task. This research addresses this issue by developing a data driven model for Multi Circular Arc airfoils loss correlation utilizing an automated blade-to-blade flow solver, StarCCM, under specific flow boundary conditions. Since the data built in this research includes high dimensionality, it is subjected to dimensionality reduction using Principal Component Analysis while retaining as much of its important information as possible. What sets our research apart is the comparative analysis of hybrid machine learning approaches. We specifically combine classification techniques based on shock wave formation on the airfoil’s suction side with Multi-Layer Perceptron neural network methods. This unique combination allows for a comprehensive prediction of MCA loss as the main objective of the research. The results demonstrate that this surrogate-based approach provides accurate loss model with 97% of precision error for the classification which results in 0.99 and 0.94 of R2 for loss model related to the transonic and subsonic airfoils loss prediction.