Author(s) :

Dr. M. Rajagopal.

Volume/Issue :

Abstract :

Artificial intelligence (AI) is increasingly being integrated into thermal management systems that use phase change materials (PCMs) to enhance energy efficiency and temperature control. AI can analyze large datasets from thermal management systems, identifying patterns and correlations that traditional methods might miss. Machine learning algorithms can predict how PCMs will behave under different conditions, optimizing their performance for applications like building energy management, thermal energy storage, and electronics cooling. AI models can simulate the thermal behavior of PCMs in real time. This allows for dynamic adjustments to thermal systems, ensuring optimal temperatures are maintained and preventing overheating or excessive cooling. By utilizing AI-driven algorithms, researchers can optimize the formulation of PCMs, enhancing their thermal properties such as melting and solidification temperatures. This can lead to improved energy efficiency in various applications. AI can be used to monitor the health of thermal management systems utilizing PCMs. By analyzing operational data, AI can predict failures or inefficiencies, allowing for timely maintenance and reducing downtime. AI can assist in energy demand forecasting, helping to manage the use of PCMs in systems like solar thermal energy storage. Predictive analytics can optimize charging and discharging cycles based on expected energy consumption patterns. AI can enhance control strategies for systems using PCMs, enabling more responsive and adaptive management based on real-time conditions and forecasts. This ensures maximum efficiency and performance of thermal management systems. AI can work alongside Internet of Things (IoT) technologies to gather real-time data from various sensors in thermal management systems. This integration allows for more sophisticated predictive analytics and decision-making.

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