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

TL;DR

This paper reviews recent developments in Physics-Informed Neural Networks (PINNs) applied to industrial gas turbines, emphasizing their potential, current challenges, and future research directions for enhanced physical modeling and computational efficiency.

Contribution

It provides a comprehensive survey of PINNs in gas turbine research, highlighting recent advancements, challenges, and future prospects for broader adoption.

Findings

PINNs improve flow field reconstruction accuracy.

Hybrid modeling strategies enhance computational efficiency.

PINNs face challenges in robustness and scalability.

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.