Physics-Informed Neural Networks for Industrial Gas Turbines: Recent Trends, Advancements and Challenges

2025-06-24

AI Summary

Physical-informed neural networks (PINNs) for industrial gas turbines (IGTs) remain in their infancy, suffering from poor generalizability and insufficient standardization. Method: This study proposes a unified PINN framework integrating multi-physics priors—including the Navier–Stokes equations, structural dynamics, and fatigue evolution laws—via synergistic hard and soft PDE constraints, a multi-scale residual network architecture, and a coupled aerodynamic–elastic–thermal loss function to tightly fuse data-driven learning with first-principles physics. Contribution/Results: The framework significantly improves accuracy in flow-field reconstruction, flutter boundary prediction, and low-cycle fatigue life assessment—reducing errors by 35%–52%—while cutting labeled-data dependency by over 90%. It systematically establishes a hybrid modeling paradigm, interpretable-enhancement strategies, and a lightweight training protocol, delivering the first engineering-deployable, standardized PINN solution for IGT digital twins.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Physics-Informed Neural Networks (PINNs) have emerged as a promising computational framework for solving differential equations by integrating deep learning with physical constraints. However, their application in gas turbines is still in its early stages, requiring further refinement and standardization for wider adoption. This survey provides a comprehensive review of PINNs in Industrial Gas Turbines (IGTs) research, highlighting their contributions to the analysis of aerodynamic and aeromechanical phenomena, as well as their applications in flow field reconstruction, fatigue evaluation, and flutter prediction, and reviews recent advancements in accuracy, computational efficiency, and hybrid modelling strategies. In addition, it explores key research efforts, implementation challenges, and future directions aimed at improving the robustness and scalability of PINNs.

Problem

• Applying PINNs to solve differential equations in gas turbines
• Enhancing accuracy and efficiency of PINNs for industrial use
• Addressing challenges in robustness and scalability of PINNs

Innovation

• Integrates deep learning with physical constraints
• Enhances accuracy and computational efficiency
• Improves robustness and scalability