Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach
The increasing demand for cooling and refrigeration poses an urgent need in designing efficient and low-cost thermal energy storage systems for future energy systems. While multiple effects may affect the heat transfer behaviors during thermal energy storage, these effects can be lumped into one par...
| Main Authors: | , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/161871 |
| _version_ | 1826115740132966400 |
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| author | Yang, Lizhong Gil, Antoni Leong, Pammy S. H. Khor, Jun Onn Akhmetov, Bakytzhan Tan, Wooi Leong Rajoo, Srithar Cabeza, Luisa F. Romagnoli, Alessandro |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Yang, Lizhong Gil, Antoni Leong, Pammy S. H. Khor, Jun Onn Akhmetov, Bakytzhan Tan, Wooi Leong Rajoo, Srithar Cabeza, Luisa F. Romagnoli, Alessandro |
| author_sort | Yang, Lizhong |
| collection | NTU |
| description | The increasing demand for cooling and refrigeration poses an urgent need in designing efficient and low-cost thermal energy storage systems for future energy systems. While multiple effects may affect the heat transfer behaviors during thermal energy storage, these effects can be lumped into one parameter, the effective thermal conductivity. Effective thermal conductivity provides a simple and reliable solution for accurate numerical simulations in designing a thermal energy storage system. In this study, a novel experimental, numerical and Bayesian optimization-based method is developed and validated that allows for fast and accurate measurement of the effective thermal conductivities over a wide temperature range. The method can also be applied to other bulky and heterogeneous structures that cannot be considered as continuous media. An experimental setup and a 3D numerical model were developed for the plate-type thermal energy storage. After a thorough algorithm comparison, Bayesian optimization using Gaussian process was selected to search for the effective thermal conductivities with high accuracy (root mean square error < 2 K and R-squared between 0.975 and 0.992). The effective thermal conductivities measured using deionized water as the phase change material were validated by a COMSOL simulation. With the accurate effective thermal conductivity results, we revealed that neglecting the effective thermal conductivity for the solid phase while still using conduction models will lead to significant errors in the simulation. A duo arch-shaped graphite sheet-based macrofiller is designed and inserted into the plate-type thermal energy storage, which increased the effective thermal conductivities by around 20% and suppressed the subcooling effect. |
| first_indexed | 2024-10-01T04:00:06Z |
| format | Journal Article |
| id | ntu-10356/161871 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:00:06Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1618712022-09-22T06:35:11Z Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach Yang, Lizhong Gil, Antoni Leong, Pammy S. H. Khor, Jun Onn Akhmetov, Bakytzhan Tan, Wooi Leong Rajoo, Srithar Cabeza, Luisa F. Romagnoli, Alessandro School of Mechanical and Aerospace Engineering Surbana Jurong – Nanyang Technological University Corporate Lab Engineering::Mechanical engineering Thermal Energy Storage Machine Learning The increasing demand for cooling and refrigeration poses an urgent need in designing efficient and low-cost thermal energy storage systems for future energy systems. While multiple effects may affect the heat transfer behaviors during thermal energy storage, these effects can be lumped into one parameter, the effective thermal conductivity. Effective thermal conductivity provides a simple and reliable solution for accurate numerical simulations in designing a thermal energy storage system. In this study, a novel experimental, numerical and Bayesian optimization-based method is developed and validated that allows for fast and accurate measurement of the effective thermal conductivities over a wide temperature range. The method can also be applied to other bulky and heterogeneous structures that cannot be considered as continuous media. An experimental setup and a 3D numerical model were developed for the plate-type thermal energy storage. After a thorough algorithm comparison, Bayesian optimization using Gaussian process was selected to search for the effective thermal conductivities with high accuracy (root mean square error < 2 K and R-squared between 0.975 and 0.992). The effective thermal conductivities measured using deionized water as the phase change material were validated by a COMSOL simulation. With the accurate effective thermal conductivity results, we revealed that neglecting the effective thermal conductivity for the solid phase while still using conduction models will lead to significant errors in the simulation. A duo arch-shaped graphite sheet-based macrofiller is designed and inserted into the plate-type thermal energy storage, which increased the effective thermal conductivities by around 20% and suppressed the subcooling effect. The authors would like to acknowledge the funding support from SJ-NTU Corporate Lab at Nanyang Technological University. This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from Surbana Jurong Pte Ltd. This research is partially funded by the Ministerio de Ciencia Innovacion ´ y Universidades de Espana ˜ (RTI2018-093849-B-C31— MCIU/AEI/FEDER, UE), and the Ministerio de Ciencia, Innovacion ´ y Universidades—Agencia Estatal de Investigacion ´ (AEI) (RED2018- 102431-T). This work is partially supported by ICREA under the ICREA Academia programme. 2022-09-22T06:35:11Z 2022-09-22T06:35:11Z 2022 Journal Article Yang, L., Gil, A., Leong, P. S. H., Khor, J. O., Akhmetov, B., Tan, W. L., Rajoo, S., Cabeza, L. F. & Romagnoli, A. (2022). Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach. Journal of Energy Storage, 52(Part A), 104795-. https://dx.doi.org/10.1016/j.est.2022.104795 2352-152X https://hdl.handle.net/10356/161871 10.1016/j.est.2022.104795 2-s2.0-85129954548 Part A 52 104795 en Journal of Energy Storage © 2022 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Mechanical engineering Thermal Energy Storage Machine Learning Yang, Lizhong Gil, Antoni Leong, Pammy S. H. Khor, Jun Onn Akhmetov, Bakytzhan Tan, Wooi Leong Rajoo, Srithar Cabeza, Luisa F. Romagnoli, Alessandro Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach |
| title | Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach |
| title_full | Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach |
| title_fullStr | Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach |
| title_full_unstemmed | Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach |
| title_short | Bayesian optimization for effective thermal conductivity measurement of thermal energy storage: an experimental and numerical approach |
| title_sort | bayesian optimization for effective thermal conductivity measurement of thermal energy storage an experimental and numerical approach |
| topic | Engineering::Mechanical engineering Thermal Energy Storage Machine Learning |
| url | https://hdl.handle.net/10356/161871 |
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