Year: 2025
Type: article
Abstract: The integration of artificial intelligence in thermal management for wearable technology represents a significant advancement in addressing heat dissipation challenges while maintaining optimal device performance. Through multi-tiered thermal control systems and advanced sensing capabilities, modern wearable devices can dynamically adapt to changing thermal conditions. The implementation of machine learning algorithms enables predictive thermal management, reducing response times and improving overall system efficiency. By combining software-based controls with innovative hardware solutions, including phase change materials and advanced thermal interfaces, these systems maintain device temperatures within comfort thresholds while preserving performance capabilities. Real-world performance metrics demonstrate substantial improvements in thermal stability, user comfort, and device longevity. Future developments point toward enhanced integration of edge computing, multi-device thermal coordination, and automated thermal design systems, promising further advancements in wearable device thermal management.
Author: Krishna Chaitanya Saride -
Institution: Andhra University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409218768