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Inverse flow prediction using ensemble PINNs and uncertainty quantification
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
Mälardalen University, School of Innovation, Design and Engineering, Innovation and Product Realisation.ORCID iD: 0000-0002-2978-6217
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-8466-356X
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Hitachi Energy Research, Västerås, Sweden..ORCID iD: 0000-0001-8849-7661
2024 (English)In: International Journal of Heat and Mass Transfer, ISSN 0017-9310, E-ISSN 1879-2189, Vol. 226Article in journal (Refereed) Published
Abstract [en]

The thermal boundary conditions in a numerical simulation for heat transfer are often imprecise. This leads to poorly defined boundary conditions for the energy equation. The lack of accurate thermal boundary conditions in real-world applications makes it impossible to effectively solve the problem, regardless of the advancement of conventional numerical methods. 

This study utilises a physics-informed neural network to tackle ill-posed problems for unknown thermal boundaries with limited sensor data. The network approximates velocity and temperature fields while complying with the Navier-Stokes and energy equations, thereby revealing unknown thermal boundaries and reconstructing the flow field around a square cylinder. The method relies on optimal sensor placement determined by the QR pivoting technique, which ensures the effective capture of the dynamics, leading to enhanced model accuracy. In an effort to increase the robustness and generalisability, an ensemble physics-informed neural network is implemented. This approach mitigates the risks of overfitting and underfitting while providing a measure of model confidence. As a result, the ensemble model can identify regions of reliable prediction and potential inaccuracies. Therefore, broadening its applicability in tackling complex heat transfer problems with unknown boundary conditions.

Place, publisher, year, edition, pages
2024. Vol. 226
Keywords [en]
Heat transfer, mixed convection, physics informed neural network, optimal sensor placement, transient simulation, inverse method
National Category
Engineering and Technology Computational Mathematics
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64897DOI: 10.1016/j.ijheatmasstransfer.2024.125480ISI: 001226062100001Scopus ID: 2-s2.0-85189514108OAI: oai:DiVA.org:mdh-64897DiVA, id: diva2:1815754
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-05-29Bibliographically approved
In thesis
1. Machine Learning Techniques for Enhanced Heat Transfer Modelling
Open this publication in new window or tab >>Machine Learning Techniques for Enhanced Heat Transfer Modelling
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With the continuous growth of global energy demand, processes from power generation to electronics cooling become vitally important. The role of heat transfer in these processes is crucial, facilitating effective monitoring, control, and optimisation. Therefore, advancements and understanding of heat transfer directly correlate to system performance, lifespan, safety, and cost-effectiveness, and they serve as key components in addressing the world's increasing energy needs.

The field of heat transfer faces the challenge of needing intensive studies while retaining fast computational speeds to allow for system optimisation. While advancements in computational power are significant, current numerical models lack in handling complex physical problems such as ill-posed. The domain of heat transfer is rapidly evolving, driven by a wealth of data from experimental measurements and numerical simulations. This data influx presents an opportunity for machine learning techniques, which can be used to harness meaningful insights about the underlying physics.

Therefore, the current thesis aims to the explore machine learning methods concerning heat transfer problems. More precisely, the study looks into advanced algorithms such as deep, convolutional, and physics-informed neural networks to tackle two types of heat transfer: subcooled boiling and convective heat transfer. The thesis further addresses the effective use of data through transfer learning and optimal sensor placement when available data is sparse, to learn the system behaviour. This technique reduces the need for extensive datasets and allows models to be trained more efficiently. An additional aspect of this thesis revolves around developing robust machine learning models. Therefore, significant efforts have been directed towards accounting for the uncertainty present in the model, which can further illuminate the model’s behaviour. This thesis shows the machine learning model's ability for accurate prediction. It offers insights into various parameters and handles uncertainties and ill-posed problems. The study emphasises machine learning's role in optimising heat transfer processes. The findings highlight the potential of synergistic application between traditional methodologies and machine learning models. These synergies can significantly enhance the design of systems, leading to greater efficiency.

Place, publisher, year, edition, pages
Västerås: Mälardalens universitet, 2024
Series
Mälardalen University Press Dissertations, ISSN 1651-4238 ; 399
National Category
Engineering and Technology Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
urn:nbn:se:mdh:diva-64898 (URN)978-91-7485-625-5 (ISBN)
Public defence
2024-02-13, Delta, Mälardalens universitet, Västerås, 09:00 (English)
Opponent
Supervisors
Available from: 2023-12-01 Created: 2023-11-29 Last updated: 2024-01-23Bibliographically approved

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Soibam, JerolAslanidou, IoannaKyprianidis, KonstantinosBel Fdhila, Rebei

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