Operational data-based adaptive improvement method of gas turbine component characteristics for performance simulation

• Published in: Journal of mechanical science and technology 2023, 37(12), , pp.6691-6709
• Main Authors: Zhang, Peng, Feng, Kun, Liu, Baoxia, Li, Yingli, Yan, Binbin
• Format: Article
• Language: English
• Subjects: Adaptation; Control; Dynamical Systems; Engineering; Fault diagnosis; Gas turbines; Industrial and Production Engineering; Mechanical Engineering; Original Article; Parameters; Simulation; Simulation models; Turbines; Vibration; 기계공학
• ISSN: 1738-494X; 1976-3824
• DOI: 10.1007/s12206-023-1040-2

Accurate component maps are crucial for gas turbine performance simulation. However, generating component maps is challenging due to limited data availability and individual characteristic differences between gas turbines. Thus, a component characteristic adaptation method is proposed here. Initially, the original component analytical formulas (OCAF) are enhanced, and the analytical normalized characteristic parameters (ANCPs) are calculated. Subsequently, the real normalized characteristic parameters (RNCPs) are calculated reversely based on field-measured data. Next, the tuning factors are optimized to obtain optimal improved component analytical formulas (ICAF). Finally, the effectiveness of the proposed method is validated using LM2500+ gas turbine field data and compared with two previous adaptive methods. The results reveal that the proposed method offers high tunability and computational efficiency during the adaptation process, significantly improving the accuracy of the gas turbine performance simulation model. This study paves the way for more reliable gas turbine performance simulations and enhanced fault diagnosis in the field.