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A physics-driven and machine learning-based digital twinning approach to transient thermal systems

Armando Di Meglio, Nicola Massarotti, Perumal Nithiarasu Orcid Logo

International Journal of Numerical Methods for Heat and Fluid Flow, Volume: 34, Issue: 6, Pages: 2229 - 2256

Swansea University Author: Perumal Nithiarasu Orcid Logo

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DOI (Published version): 10.1108/hff-10-2023-0616

Abstract

Purpose - In this study, we propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The primary objective is to harness the combined power of Deep Learning (DL) and Physics-Based Methods (PBM) to create an active virtual replica of the physical syst...

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Published in: International Journal of Numerical Methods for Heat and Fluid Flow
ISSN: 0961-5539 0961-5539
Published: Emerald 2024
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65598
Abstract: Purpose - In this study, we propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The primary objective is to harness the combined power of Deep Learning (DL) and Physics-Based Methods (PBM) to create an active virtual replica of the physical system. Design/methodology/approach - To achieve this goal, we introduce a Deep Neural Network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is employed to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop. Findings - The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D, and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems. Originality/Value - The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.
Keywords: Digital Twinning; Digital Twin (DT); Transient Heat Transfer; FEM; Deep Learning; Machine Learning; Data Exchange
College: Faculty of Science and Engineering
Issue: 6
Start Page: 2229
End Page: 2256

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