MULTIDISCIPLINARY DESIGN OPTIMIZATION WITH MULTIPLE DEGREES OF FREEDOM FOR AN AXIAL COMPRESSOR BASED ON DATA-DRIVEN

Compressor design represents a multidisciplinary coupling problem encompassing aerodynamics, structure strength, vibration, fatigue, and acoustics. Multidisciplinary design optimization enables a comprehensive consideration of the interconnectedness among these disciplines, reasonably balances the conflicting requirements, and ultimately enhances the performance of products significantly while reducing the development cycle. In this paper, a multidisciplinary optimization system is established based on a data-driven multi-objective evolution algorithm. This algorithm is combined with the directly manipulated free-form deformation method to achieve multi-degree-of-freedom parameterized control. The research focuses on optimizing the aerodynamic and aeroelastic design of a 1.5-stage axial flow compressor in a gas turbine. Maximum efficiency and surge margin are set as the optimization objectives, while the constraint conditions involve flow rate, total pressure ratio, as well as the strength and resonance margin of the rotor blade. To improve the prediction accuracy of surge margin during optimization, a successive approximation method combined with efficiency-based residual convergence determination is utilized. The results show a significant reduction in the stress distribution of blades, with the maximum stress value decreased by 30.6%. Simultaneously, the surge margin of the compressor is increased by 3.36%, and the efficiency at the design point is also slightly improved. The optimization system can not only ensure optimization effectiveness, but also greatly reduce the design variables and evaluation time, which is effectively applicable to the multidisciplinary design optimization of compressors.