Artificial Neural Network Based Enhanced Thermal Energy Storage System for Renewable Energy

Strategic Insights -

Research conducted at Wuhan University of Technology has significantly improved the efficiency and sustainability of thermal energy storage systems. The study, which was funded by Imam Mohammad Ibn Saud Islamic University, combined latent heat storage systems with renewable energy sources and employed advanced materials and innovative configurations. The research team, led by Hassan Waqas, utilized phase change materials (PCMs) to store thermal energy during phase transition, resulting in a 5.43% increase in energy storage at a rotational speed of 0.3 rpm. The integration of V-shaped fins and nanoparticles into the PCM further enhanced the thermal conductivity and storage capacity of the system.

Key Takeaways:

  • Thermal Energy Storage (TES) has emerged as a viable solution to the world's energy concerns by combining latent heat storage systems with renewable energy sources.
  • The use of Phase Change Materials (PCMs) has improved the efficiency of TES systems, making them ideal for thermal control applications.
  • The incorporation of V-shaped fins and nanoparticles into PCMs has enhanced the thermal conductivity and storage capacity of LHSS.
  • The enthalpy-porosity model was employed to represent the melting process of PCMs using ANSYS Fluent, demonstrating improved thermal performance with rotational speeds (0.1 rpm, 0.2 rpm, and 0.3 rpm).
  • Results showed a 5.43% increase in energy storage at 0.3 rpm, attributed to improved thermal mixing and more effective utilization of the PCM.
  • Artificial Neural Networks (ANNs) were used to predict TES performance, offering a powerful analytical tool for comprehending the intricate relationships within the system.
  • Meraj Ali Khan, Saima Zainab, and Sharmeen were additional authors on the research, which has significant implications for the development of renewable energy systems.

Statistics:

  • A 5.43% increase in energy storage was achieved at 0.3 rpm by integrating V-shaped fins and nanoparticles into PCMs.
  • The Nusselt number was reduced in the presence of rotating V-shaped fins, indicating improved thermal mixing.
  • The enthalpy-porosity model was used to represent the melting process of PCMs, demonstrating a more even and effective temperature distribution.
  • The research concluded that increasing the rotational speed leads to improved energy storage, with a 5.43% increase at 0.3 rpm.

Sources:

  • NewsRx. Research Conducted at Wuhan University of Technology Has Updated Our Knowledge about Renewable Energy (Artificial Neural Network Based Enhanced Thermal Energy Storage System for Renewable Energy Using Nano-particles). Ecology, Environment & Conservation. September 5, 2025; p 670.
  • Artificial Neural Network Based Enhanced Thermal Energy Storage System for Renewable Energy Using Nano-particles. Case Studies in Thermal Engineering, 2025;73.
  • Elsevier. Case Studies in Thermal Engineering. Radarweg 29, 1043 Nx Amsterdam, Netherlands.
  • Wuhan University of Technology. Sch Naval Architecture Ocean & Energy Power Engn, Wuhan 430063, Hubei, People's Republic of China.