A comprehensive review of optimizing phase change materials in thermal energy storage: The role of nanoparticles, fin configurations, and data-driven approaches

Abstract

Thermal energy storage (TES) systems, particularly those utilizing phase change materials (PCMs), play a crucial role in enhancing the efficiency and sustainability of renewable energy systems. PCMs store and release energy during phase transitions, effectively addressing the intermittency of solar, wind, and hydro power sources and providing a reliable and efficient storage solution. However, several challenges hinder their optimal performance, including low thermal conductivity (TC), slow heat transfer rates, and supercooling effects. The review examines recent innovations that have focused on enhancing the performance of these systems by incorporating nanoparticles (NPs), such as copper, graphene oxide, and bio-based materials, to improve TC, accelerate phase transitions, and enhance heat transfer efficiency. It also highlights the importance of balancing the improvement in TC with the latent heat storage capacity. Additionally, ensuring long-term stability is a concern, as NP agglomeration and phase separation can degrade system performance over time. To address these issues, optimizing NP concentration and achieving uniform dispersion are crucial. Moreover, the design of TES systems, including advanced fin configurations, plays a vital role in maximizing heat transfer and improving system efficiency. Additionally, this review investigates optimization strategies, the application of machine learning (ML), deep learning (DL), and multi-objective optimization methods to boost the effectiveness of TES systems, with a specific emphasis on the utilization of NPs to enhance PCMs. This review critically evaluates various optimization techniques, including metaheuristic algorithms and ML models, to assess their effectiveness in predicting and optimizing the thermophysical properties of PCMs and NP-enhanced systems. It also explores the optimal combination of NP additives, their concentrations, and suitable PCMs to maximize storage capacity and extend the load discharge period for both open and closed systems. The study highlights significant advancements, identifies current limitations, and outlines future research directions in applying data-driven approaches to the design and operation of TES systems.