Revolutionizing gas turbine performance analysis with deep learning powered digital twin

Gas turbine technology is essential for resolving the energy trilemma while facilitating renewable energy integration, yet existing physics-based models lack adaptability to real-world variations and purely data-driven approaches frequently violate fundamental thermodynamic laws. This study presents a novel physics-constrained deep learning framework that uniquely integrates first-principles thermodynamics with operational data assimilation to create a high-fidelity digital twin of a GE 9FA heavy-duty gas turbine, distinguishing itself from conventional approaches by ensuring thermodynamic consistency while adapting to measured performance. The digital twin employs neural network architectures regularized by conservation laws and thermodynamic cycle constraints to forecast and interactively visualize T–S and P–V trajectories, translating subtle efficiency variations into actionable operational insights. Comprehensive validation across six key operating regimes demonstrates low predictive error and robust performance, confirming that physics constraints enhance generalization compared to standard machine learning baselines. These capabilities support proactive maintenance strategies and long-term efficiency optimization, representing a significant advancement toward intelligent, self-optimizing turbine systems for reliable energy management.

Text 127164.pdf - Published Version Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (16MB) Official URL or Download Paper: https://linkinghub.elsevier.com/retrieve/pii/S3117...

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