Artificial neural network based enhanced thermal energy storage system for renewable energy using nano-particles
Thermal Energy Storage (TES) has appeared to be a viable answer to the worlds energy concerns. Combining Latent Heat Storage Systems (LHSS) with renewable (solar) energy sources has improved the sustainability and efficiency of energy systems worldwide. Phase Change Material (PCM) stores thermal ene...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Case Studies in Thermal Engineering |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25008469 |
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| Summary: | Thermal Energy Storage (TES) has appeared to be a viable answer to the worlds energy concerns. Combining Latent Heat Storage Systems (LHSS) with renewable (solar) energy sources has improved the sustainability and efficiency of energy systems worldwide. Phase Change Material (PCM) stores thermal energy during phase transition, making them ideal for thermal control applications. This study offers a comprehensive analytical framework for improving TES systems using advanced materials and innovative configurations, thereby enhancing energy storage efficiency due to the rotational effects of fins. We have integrated V-shaped fins and incorporated nanoparticles (Al2O3 and Cu) into PCM to improve the thermal conductivity and storage capacity of LHSS. Enthalpy-porosity model is employed to represent the melting process of PCM using ANSYS Fluent. The consequences of distinct rotational speeds (0.1 rpm, 0.2 rpm and 0.3 rpm) of the V-shaped fins on the thermal performance of PCM are investigated. The temperature distribution with enhanced PCM is more even and effective. Improved thermal performance is achieved by amalgamating rotating V-shaped fins with PCM augmented with nanoparticles. Results demonstrate that increasing the rotational speed leads to improved energy storage, with a 5.43% increase at 0.3 rpm, accompanied by a reduction in the Nusselt number. This performance enhancement is attributed to improved thermal mixing and more effective utilization of the PCM, highlighting the potential of rotational fins as a thermal optimization strategy. After obtaining the results from the solver, TES performance is predicted using artificial neural networks (ANNs), which offer a powerful analytical tool for comprehending the intricate relationships within the system. |
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| ISSN: | 2214-157X |