Multi-objective artificial neural network-genetic algorithm optimization of transient thermal storage device in a hexagonal phase change material system
International Communications in Heat and Mass Transfer, cilt.178, sa.P5, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 178 Sayı: P5
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.icheatmasstransfer.2026.111842
- Dergi Adı: International Communications in Heat and Mass Transfer
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Artificial neural network, Genetic algorithm, Multi-objective optimization, Pareto front analysis, Phase change material, Thermal energy storage
- İstanbul Gelişim Üniversitesi Adresli: Evet
Özet
Latent thermal energy storage systems based on phase change materials (PCMs) play a vital role in improving energy efficiency, mitigating peak thermal loads, and facilitating the integration of renewable energy sources. Their ability to store large amounts of heat within a narrow temperature range makes them particularly attractive for applications in solar energy systems, waste heat recovery, and thermal management technologies. Nevertheless, the inherently low heat-conduction of most PCMs significantly limits charging rates, thereby restricting their practical performance. In response to these challenges, this work introduces a novel hexagonal thermal energy storage device inspired by honeycomb architectures and conventional shell-and-tube heat exchangers. The design integrates two concentric hexagonal frameworks within a unified outer shell, interconnected by structural bases rather than conventional fins. This monolithic configuration not only increases the effective heat-transfer surface but also creates extended conductive pathways that accelerate melting during charging cycles. The segmented internal domain further enables the use of multiple PCMs to meet varying thermal demands, while improving mechanical integrity and reducing leakage risks. To identify the optimal configuration for maximum energy storage, an artificial neural network predictive model is coupled with a multi-objective genetic algorithm, with the first and second hexagon lengths and the system inclination angle as key design variables. Two distinct optimal cases were identified by single-objective optimization (Optimal Cases 1 and 2). Another optimal case was derived from multi-objective optimization and the TOPSIS method, referred to as Optimal Case 3. By 5 h, all three optimized cases had fully melted (liquid fraction = 1). In contrast, the Core Case exhibited significantly slower melting behavior with the liquid fraction of 0.505. Besides, the optimized cases stored about 13,500 kJ of thermal energy, while the Core Case reached only 7621 kJ. Compared to the Core Case, the improvements remained significant, at approximately 77.5%.