Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs

• URL: http://arxiv.org/abs/2212.10032v1
• Date: Tue, 20 Dec 2022 07:07:44 GMT
• Title: Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
• Authors: Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar, Shirish Karande, Lovekesh Vig, Venkataramana Runkana
• Abstract summary: A hypernetwork based approach is used to enable the domain-decomposed PINN learn the thermal behavior of the heat exchanger. 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.
• Score: 12.23889788846524
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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