AI-Driven Thermal Management: Optimizing Performance and Comfort in Wearable Technology

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.

Authors

Publication Details

Journal
International Journal on Science and Technology
Published
2025-04-02
DOI
https://doi.org/10.71097/ijsat.v16.i2.3061
Citations
1
Primary Topic
Refrigeration and Air Conditioning Technologies
Type
article
Field-Weighted Citation Impact
0.71
Controls
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article

AI-Driven Thermal Management: Optimizing Performance and Comfort in Wearable Technology

Krishna Chaitanya Saride -
1 citations
International Journal on Science and Technology
Refrigeration and Air Conditioning Technologies
0.71
article

AI-Driven Thermal Management: Optimizing Performance and Comfort in Wearable Technology

Krishna Chaitanya Saride -
article en
1 citations

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.

International Journal on Science and TechnologyVol. 16(2)
Openalex Percentile: Top 34%
Refrigeration and Air Conditioning Technologies
0.71
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AI-Driven Thermal Management: Optimizing Performance and Comfort in Wearable Technology — Krishna Chaitanya Saride - · International Journal on Science and Technology (2025) | TGRS Research Map | TGRS