Machine Learning (ML) Based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging That Leverages Phase Change Materials (PCM)
Miniaturization of electronics devices is often limited by the concomitant high heat fluxes (cooling load) and maldistribution of temperature profiles (hot spots). Thermal energy storage (TES) platforms providing supplemental cooling can be a cost-effective solution, that often leverages phase chang...
Guardado en:
Autores principales: | Aditya Chuttar, Debjyoti Banerjee |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/de7dd1e229db4f9d8cf2b222d93981b6 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Under-FET Thermal Sensor Enabling Smart Full-Chip Run-Time Thermal Management
por: Cheng Li, et al.
Publicado: (2020) -
Thermal Comfort and Energy Analysis of a Hybrid Cooling System by Coupling Natural Ventilation with Radiant and Indirect Evaporative Cooling
por: Pradeep Shakya, et al.
Publicado: (2021) -
Increasing the energy efficiency of a building by thermal insulation to reduce the thermal load of the micro-combined cooling, heating and power system
por: Spiru Paraschiv, et al.
Publicado: (2021) -
Thermal Storage for District Cooling—Implications for Renewable Energy Transition
por: Efstathios E. Michaelides
Publicado: (2021) -
Investigation and optimization of PCM melting with nanoparticle in a multi-tube thermal energy storage system
por: Hadi Bashirpour-Bonab
Publicado: (2021)