Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage

Luo, Ji-Wang; Yaji, Kentaro; Chen, Li; Tao, Wen-Quan

doi:https://doi.org/10.1016/j.apenergy.2024.124596

Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage

Luo, Ji-Wang; Yaji, Kentaro; Chen, Li et al.

Applied Energy, 2024, 377, 124596

アクセス数:369件（2025-07-01 10:27 集計）

固定URL: https://hdl.handle.net/11094/98219

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タイトル	Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage
著者	著者名 Luo, Ji-Wang
	NRID 1000090779373 1000090779373 ORCID 0000-0002-4309-2043 0000-0002-4309-2043 researchmap permalink yajiken0609 yajiken0609 Scopus Author ID 55787947500 55787947500 著者名 Yaji, Kentaro
	著者名 Chen, Li
	著者名 Tao, Wen-Quan
キーワード等	Topology optimization
	Data-driven approach
	Multi-fidelity design
	Phase change material
	Latent heat thermal energy storage
	Heat transfer enhancement
内容	Luo Ji-Wang, Yaji Kentaro, Chen Li, et al. Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage. Applied Energy 377, 124596 (2024); https://doi.org/10.1016/j.apenergy.2024.124596.
抄録	This work develops a data-driven multi-fidelity topology design (MFTD) method for designing fins in a latent heat thermal energy storage tube. The high-fidelity simulation resolves the actual solid-liquid phase change process using the enthalpy method, while the low-fidelity topology optimization (TO) simply considers the natural convection with Darcy flow. The above MFTD method is integrated into the framework of evolutional algorithm, and the variational autoencoder is introduced to generate new offspring. Fins for accelerating the melting and solidification processes at different Grashof number (Gr) are designed. It is found that when the fin volume fraction is low, the melt designs exhibit strong heterogeneity due to the strong convection, while the solidification designs are almost isotropic. Along with the increase of the fin volume fraction, the fins are first getting longer, then having more branches or sub-branches and finally becoming thicker. The superiority of the present data-driven MFTD method to the gradient-based direct TO method for solving optimization problems with strong multimodality has been demonstrated in this work. Results find that compared with the designs from direct TO, the MFTD melt design can further reduce the melting time by at least 27 % and 20 % at Gr = 3.3 × 103 and Gr = 3.3 × 104 respectively, and the MFTD solidification design can further shorten the solidification time by at least 9 %.
出版者	Elsevier BV
収録物名	Applied Energy
巻(号)	377
ページ	124596
刊行年月	2024-10-01
ハンドルURL	https://hdl.handle.net/11094/98219
PISSN	03062619
関連情報 (isVersionOf)	DOI (出版社版) https://doi.org/10.1016/j.apenergy.2024.124596
アクセス権	embargoed access
権利情報	© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
出版タイプ	Accepted Manuscript
カテゴリ	学術雑誌論文 Journal Article

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Applied Energy. 2024, 377, p. 124596
DC.title	Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage
DC.creator	Luo, Ji-Wang
	Yaji, Kentaro
	Chen, Li
	Tao, Wen-Quan
DC.publisher	Elsevier BV
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-10-01
DC.identifier	https://doi.org/10.1016/j.apenergy.2024.124596
DC.subject	Topology optimization
	Data-driven approach
	Multi-fidelity design
	Phase change material
	Latent heat thermal energy storage
	Heat transfer enhancement
DC.description	Luo Ji-Wang, Yaji Kentaro, Chen Li, et al. Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage. Applied Energy 377, 124596 (2024); https://doi.org/10.1016/j.apenergy.2024.124596.
DCTERMS.abstract	This work develops a data-driven multi-fidelity topology design (MFTD) method for designing fins in a latent heat thermal energy storage tube. The high-fidelity simulation resolves the actual solid-liquid phase change process using the enthalpy method, while the low-fidelity topology optimization (TO) simply considers the natural convection with Darcy flow. The above MFTD method is integrated into the framework of evolutional algorithm, and the variational autoencoder is introduced to generate new offspring. Fins for accelerating the melting and solidification processes at different Grashof number (Gr) are designed. It is found that when the fin volume fraction is low, the melt designs exhibit strong heterogeneity due to the strong convection, while the solidification designs are almost isotropic. Along with the increase of the fin volume fraction, the fins are first getting longer, then having more branches or sub-branches and finally becoming thicker. The superiority of the present data-driven MFTD method to the gradient-based direct TO method for solving optimization problems with strong multimodality has been demonstrated in this work. Results find that compared with the designs from direct TO, the MFTD melt design can further reduce the melting time by at least 27 % and 20 % at Gr = 3.3 × 103 and Gr = 3.3 × 104 respectively, and the MFTD solidification design can further shorten the solidification time by at least 9 %.
DC.rights	© 2024. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/
DC.format	98219
citation_title	Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage
citation_author	Luo, Ji-Wang
	Yaji, Kentaro
	Chen, Li
	Tao, Wen-Quan
citation_publisher	Elsevier BV
citation_date	2024-10-01
citation_journal_title	Applied Energy
citation_volume	377
citation_firstpage	124596
citation_doi	https://doi.org/10.1016/j.apenergy.2024.124596
citation_public_url	https://hdl.handle.net/11094/98219

Data-driven multi-fidelity topology design of fin structures for latent heat thermal energy storage

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