ArXiv Preprint
We demonstrate a Physics-informed Neural Network (PINN) based model for
real-time health monitoring of a heat exchanger, that plays a critical role in
improving energy efficiency of thermal power plants. A hypernetwork based
approach is used to enable the domain-decomposed PINN learn the thermal
behavior of the heat exchanger in response to dynamic boundary conditions,
eliminating the need to re-train. As a result, we achieve orders of magnitude
reduction in inference time in comparison to existing PINNs, while maintaining
the accuracy on par with the physics-based simulations. This makes the approach
very attractive for predictive maintenance of the heat exchanger in digital
twin environments.
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana
2022-12-20
