AI-Enabled Optimization and Strategic Deployment of Phase Change Materials for Smart Thermal Regulation

Abstract

With increased demand in global energy and the necessity of climate change, intelligent thermal management has been a crucial topic in every industry. Phase Change Materials (PCMs), with their achievement in latent heats energy storage, are progressively used in structures frameworks insightful buildings, portable electronic devices, and warmth battery. The nonlinear dynamics, environmental variants and stochastic uncertainty of thermal loads however pose a challenge in their deployment and integration into real time systems. The proposed paper suggests an AI-assisted system that can optimize the strategic implementation of PCMs with the help of machine learning algorithms and stochastic differential equations to depict a thermal process and forecast it. Here we provide a hybridization approach of bifurcation theory of noise-induced transitions in PCM systems and a large deviation principle, a tool of noise analysis and control. The use case study examples of HVAC real-life optimization alongside energy-effective microelectronics are examined accompanied by the simulations, which were performed on climate-change-adapting data. Findings indicate that AI-driven deployment can enhance the performance of thermal regulation to a large extent (within 27 percent) compared to a fixed deployment. Moreover, the architecture would allow adaptive control with changing thermal demands and provide an excellent platform of future intelligent energy systems. The results emphasize the potential of the AI-PCM combination to transform how to sustain thermal environments.