The contribution of artificial intelligence to phase change materials in thermal energy storage: From prediction to optimization

Abstract

Artificial Intelligence (AI) is leading the charge in revolutionizing research methodologies within the field of latent heat storage (LHS) by using phase change materials (PCMs) and elevating their overall efficiency. This comprehensive review delves into AI applications within the domain of PCM for TES systems, mainly including prediction and optimization. The review article emphasizes the crucial role of AI in predicting physical properties of composite PCM and its performance in LHS systems. Also, the review article highlights the significance of AI in optimizing the structure and layout, as well as the operation and control strategies of latent heat storage systems using PCMs across various research fields. The study at hand discusses literature encompassing both experimental and theoretical articles that detail the integration of AI techniques within TES systems by using PCM, and compares the advantages and limitations of AI prediction models and optimization algorithms with existing typical technologies in the field of LHS. The summarization of the limitations in prior research has been presented, along with the proposal of potential avenues for performance enhancement of AI applied in LHS system. Additionally, the primary directions and challenges for future investigations have been emphasized, accompanied by suggested strategies.