Machine learning-driven multi-objective optimization of fin geometry for enhanced charging and discharging performance in LTES system-Reference-Cited by-同舟云学术

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Machine learning-driven multi-objective optimization of fin geometry for enhanced charging and discharging performance in LTES system

Published:2024-09 Issue: Volume:98 Page:113210
ISSN:2352-152X
Container-title:Journal of Energy Storage
language:en
Short-container-title:Journal of Energy Storage

Author:

İzgi Burak

Publisher

Elsevier BV

Reference58 articles.

1. Applications and technological challenges for heat recovery, storage and utilisation with latent thermal energy storage;Li;Appl. Energy,2021

2. A critical review on phase change materials (PCM) based heat exchanger: different hybrid techniques for the enhancement;Togun;J. Energy Storage,2024

3. Global prospects and challenges of latent heat thermal energy storage: a review;Saha;Clean Techn. Environ. Policy,2021

4. A comprehensive review on solar to thermal energy conversion and storage using phase change materials;Tripathi;J. Energy Storage,2023

5. A review of recent improvements, developments, and effects of using phase-change materials in buildings to store thermal energy;Rashid;Designs,2023

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