A physics knowledge-guided stage-wise hybrid algorithm for rapid gas turbine performance prediction

Abstract

Gas turbine (GT) rapid performance prediction is a key task in health management and operation optimization. Existing methods for degradation parameters estimation either tend to become trapped in local optima or require a substantial computational effort. To tackle these problems, a stage-wise fast solution algorithm for GT degradation parameters based on a hybrid Levenberg-Marquardt (LM) and particle swarm optimization (PSO) method was proposed in this study. Firstly, a component-level simulation model was established, and the corresponding performance degradation parameters were defined to accurately characterize the health states of primary components. Secondly, a physics-guided stage-wise hybrid LM–PSO algorithm was proposed to balance solution accuracy and computational efficiency. Finally, comparative studies with other solution algorithms were conducted using both actual operational data and simulation data to demonstrate the effectiveness of the proposed method. The comparative results demonstrate that the proposed method significantly improves the solution speed while maintaining high accuracy, making it well-suited for practical GT degradation assessment and health management applications.