Physics-Informed Machine Learning for Intelligent Gas Turbine Digital Twins: A Review

Hiyam Farhat () and Amani Altarawneh
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Hiyam Farhat: Department of Mechanical and Nuclear Engineering, Tennessee Technological University, P.O. Box 5014, Cookeville, TN 38505, USA
Amani Altarawneh: Department of Computer Science, Tennessee Technological University, P.O. Box 5101, Cookeville, TN 38505, USA

Energies, 2025, vol. 18, issue 20, 1-27

Abstract: This review surveys recent progress in hybrid artificial intelligence (AI) approaches for gas turbine intelligent digital twins, with an emphasis on models that integrate physics-based simulations and machine learning. The main contribution is the introduction of a structured classification of hybrid AI methods tailored to gas turbine applications, the development of a novel comparative maturity framework, and the proposal of a layered roadmap for integration. The classification organizes hybrid AI approaches into four categories: (1) artificial neural network (ANN)-augmented thermodynamic models, (2) physics-integrated operational architectures, (3) physics-constrained neural networks (PcNNs) with computational fluid dynamics (CFD) surrogates, and (4) generative and model discovery approaches. The maturity framework evaluates these categories across five criteria: data dependency, interpretability, deployment complexity, workflow integration, and real-time capability. Industrial case studies—including General Electric (GE) Vernova’s SmartSignal, Siemens’ Autonomous Turbine Operation and Maintenance (ATOM), and the Electric Power Research Institute (EPRI) turbine digital twin—illustrate applications in real-time diagnostics, predictive maintenance, and performance optimization. Together, the classification and maturity framework provide the means for systematic assessment of hybrid AI methods in gas turbine intelligent digital twins. The review concludes by identifying key challenges and outlining a roadmap for the future development of scalable, interpretable, and operationally robust intelligent digital twins for gas turbines.

Keywords: physics-informed machine learning; hybrid modeling; gas turbine diagnostics; artificial neural networks; intelligent digital twin; virtual sensing; remaining useful life; transfer learning; generative models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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