ArticleScientific reports2025
Optimization of nano-finned enclosure-shaped latent heat thermal energy storage units using CFD, RSM, and enhanced hill climbing algorithm.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Computational assessment of cold thermal energy storage improvement using hybrid nanofluid cooling and metal foam media.Scientific reports · 2026Article
- Development of a novel solar system incorporating a heat pipe within a porous enclosure for simulating paraffin melting.Scientific reports · 2025Article
- MPGH-FS: A Hybrid Feature Selection Framework for Robust Multi-Temporal OBIA Classification.Sensors (Basel, Switzerland) · 2025Article
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10 authors.
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Abstract
Thermal energy storage plays a critical role in improving energy efficiency and sustainability, particularly in solar energy systems, industrial waste heat recovery, and building temperature regulation. However, traditional latent heat thermal energy storage (LHTES) systems face significant challenges due to the low thermal conductivity of phase change materials (PCMs), leading to prolonged charging/discharging times and reduced efficiency. To address these limitations, this study presents a framework for optimizing nano-finned enclosure-shaped LHTES units that incorporate nano-enhanced phase change materials (NePCMs) and fins. The research employs a novel hybrid approach that integrates computational fluid dynamics (CFD) simulations, response surface methodology (RSM), and an enhanced hill climbing (EHC) optimization technique to explore the complex interplay between fin geometry and nanomaterial characteristics. The influence of key design variables-including three fin geometry parameters (number, length, volume), nanomaterial concentration, and eight nanomaterials (metal, oxide, and carbon-based)-is analyzed to optimize phase change time and total stored energy. Results demonstrate that reduced sixth-degree and reduced quartic polynomial models, developed through RSM, provide high accuracy in predicting total stored energy and melting time, respectively. While the incorporation of nanomaterials generally reduces total stored energy due to their lower latent heat, carbon-based nanomaterials (GNPs, MWCNTs) offer an optimal trade-off, achieving faster melting times with minimal energy storage loss. Among the studied parameters, fin volume fraction plays a more dominant role in determining energy storage capacity compared to nanomaterial volume fraction. The optimal design configuration varies based on the priority assigned to melting time or stored energy. In a melting-time-focused scenario, the optimized unit achieves 63.03 kJ of stored energy with a melting time of 91.76 s. When prioritizing energy storage, the stored energy increases to 66.15 kJ, but the melting time extends to 222.3 s. A balanced optimization scenario yields 64.67 kJ of stored energy and a melting time of 137.4 s. These findings provide valuable insights into the design and optimization of advanced LHTES units for enhanced thermal energy management.
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