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KLIMEŠ, L. MAUDER, T. CHARVÁT, P. ŠTĚTINA, J.
Original Title
Front tracking in modelling of latent heat thermal energy storage: Assessment of accuracy and efficiency, benchmarking and GPU-based acceleration
Type
journal article in Web of Science
Language
English
Original Abstract
Computer simulations of phase change processes are of high importance in research and industry. The phase change of a material from solid to liquid and vice versa is commonplace in many technical applications from metal production to latent heat thermal energy storage. As for computer modelling, most investigators and engineers use well-known interface capturing methods because of their simplicity and straightforward implementation. However, these methods often suffer from lower computational accuracy. The paper investigates the use of the front tracking method which utilizes explicit tracking of the interface between the phases. The assessment of the computational accuracy shows that the front tracking method is about two orders of magnitude more accurate than interface capturing methods. The acceleration by means of the graphics processing units (GPUs) was utilized to enhance the computational efficiency of the front tracking method. The results demonstrate that the front tracking method and its GPU-based acceleration represent a powerful tool for fast and accurate modelling of phase change processes.
Keywords
Computational heat transfer; Front tracking method; GPU-based acceleration; Latent heat thermal energy storage; Phase change modelling
Authors
KLIMEŠ, L.; MAUDER, T.; CHARVÁT, P.; ŠTĚTINA, J.
Released
15. 7. 2018
Publisher
Elsevier
ISBN
0360-5442
Periodical
Energy
Year of study
155
Number
1
State
United Kingdom of Great Britain and Northern Ireland
Pages from
297
Pages to
311
Pages count
15
BibTex
@article{BUT151950, author="Lubomír {Klimeš} and Tomáš {Mauder} and Pavel {Charvát} and Josef {Štětina}", title="Front tracking in modelling of latent heat thermal energy storage: Assessment of accuracy and efficiency, benchmarking and GPU-based acceleration", journal="Energy", year="2018", volume="155", number="1", pages="297--311", doi="10.1016/j.energy.2018.05.017", issn="0360-5442" }