Multi-Variable Intelligent Auto-Calibration of Transient Performance Model for Gas Turbines Using Data-Physics Fusion

Abstract

This study proposes a data-physics fusion intelligent methodology for multi-variable auto-calibration of transient performance model for gas turbines. A thermodynamics-based performance model is developed to accurately simulate the transient behavior of a heavy-duty gas turbine through automatic fitting of compressor characteristic curves, intelligent tuning of the combustion chamber under variable operating conditions, and optimal calibration of gas-specific heat properties. A generalized implementation framework is established to seamlessly integrate actual operational data with the physics-based performance model using a genetic algorithm-based calibration methodology. A comparison study, conducted using data from two real-world gas turbines across various operational phases - including startup, load ramping and steady-state transitions - demonstrates the effectiveness of the proposed approach. Quantitative validation results show that the proposed calibration methodology achieves an 86.3% average error reduction compared to the original transient model, significantly enhancing model accuracy. These findings confirm the framework's ability to balance computational efficiency with physical interpretability, ensuring robust performance across different gas turbine configurations.

Keywords: Gas Turbine, Transient performance model, Model calibration, genetic algorithm, Data-physics fusion