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Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux over a Large Parameter Space
Uppsala University, Sweden. Echo State AB, S-11432 Stockholm, Sweden.
Uppsala University, Sweden. Westinghouse Elect Sweden AB, S-72163 Västerås, Sweden.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. ABB, S-72171 Västerås, Sweden.
Westinghouse Electric Sweden AB, Sweden.
2024 (English)In: Nuclear Technology, ISSN 0029-5450, E-ISSN 1943-7471Article in journal (Refereed) Published
Abstract [en]

A unifying and accurate model to predict Critical Heat Flux (CHF) over a wide range of conditions has been elusive since wall boiling research emerged. With the release of the data utilized in the development of the 2006 Groeneveld CHF lookup table (LUT), by far the most extensive public CHF database available to date (nearly 25000 data points), development of data-driven predictions models over a large parameter space in simple geometry (vertical, uniformly heated round tubes) can be performed. Furthermore, the popularization of machine learning techniques to solve regression problems has led to more advanced tools for analyzing large and complex databases. This work compares three machine learning algorithms to predict the entire LUT CHF test database. For each selected regression algorithm (ν-Support vector, Gaussian process, and neural network), an optimized hyperparameter set is fitted. The best-performing algorithm is the neural network, which can achieve a standard deviation of the predicted/measured factor of 12.3%, three times lower than the LUT. In comparison, the Gaussian process regression and the ν-Support vector regression achieve a standard deviation of 17.7%, about two times lower than the LUT. All considered algorithms hence significantly outperform the LUT prediction performance. The neural network model and training methodology are designed to prevent overfitting, which is confirmed by data analysis of the predictions. Finally, a feasibility study of transfer learning is presented and future development directions (including uncertainty quantification) are discussed. 

Place, publisher, year, edition, pages
Taylor & Francis Inc , 2024.
Keywords [en]
Critical heat flux, machine learning, neural networks, Gaussian processes
National Category
Engineering and Technology Energy Engineering
Research subject
Energy- and Environmental Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-64896DOI: 10.1080/00295450.2024.2380580ISI: 001284099100001Scopus ID: 2-s2.0-85200339475OAI: oai:DiVA.org:mdh-64896DiVA, id: diva2:1815753
Available from: 2023-11-29 Created: 2023-11-29 Last updated: 2024-08-14Bibliographically 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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Publisher's full textScopushttps://www.ans.org/pubs/proceedings/article-54429/

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Soibam, Jerol

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