A thermodynamics-informed deep learning approach for lightweight modeling of gas turbine performance

Lightweight performance modeling approaches are particularly crucial in the condition monitoring system of a heavy-duty gas turbine (HDGT) to track its efficiency changes over time accurately and promptly. This ensures sufficient productivity through a well-planned predictive maintenance strategy. This paper introduces a physics-informed deep learning methodology for lightweight modeling of HDGT performance, aimed at areal- time degradation monitoring for predictive maintenance. Initially, a mechanism-based thermodynamic model is established to serve as a performance benchmark. Subsequently, corrections are applied by adjusting base-load operating conditions to reference conditions, thereby mitigating the influence of ambient conditions and power demand. A substitute model called deep operator networks (DeepONet) is then constructed by integrating actual and simulation data to rapidly obtain crucial gas turbine performance parameters, such as compressor and turbine efficiency, as well as corrected power output and heat rate of the system. A standardized procedure is developed to automate the efficient performance modeling for gas turbines. To showcase the benefits of the proposed methodology and procedure, a comparative study with three different classical models is conducted using data from two various real-world HDGT machines. The DeepONet deep learning model is utilized to rapidly generate multivariate performance results for a gas turbine at 10ms for a single-step prediction, significantly faster than the physics-based model, which takes 6s. The prediction error is less than 0.1% on average when compared to the latter. Numerical results demonstrate that the proposed methodology offers a promising tool for real-time performance prediction of a gas turbine for predictive maintenance purposes.