Analytical Modeling of Forced-Air Cooling Heatsink by Considering Air Temperature Rise in Channel and Coupling of Multiple Heat Sources
With the rapid development of renewable energy, data centers, and new energy vehicles, the power density of power electronic converters is continuously increasing. Higher power density requires more advanced thermal dissipation system designs. Consequently, designing an efficient cooling system is c...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11080414/ |
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| Summary: | With the rapid development of renewable energy, data centers, and new energy vehicles, the power density of power electronic converters is continuously increasing. Higher power density requires more advanced thermal dissipation system designs. Consequently, designing an efficient cooling system is crucial for improving the power density of converters. Due to its simplicity and efficiency, the rectangular extruded-fin air cooling heat sink is the most common cooling system design for converters. With increasing power density, multiple power devices are often arranged along the airflow path in forced-air cooling systems. However, existing analytical thermal models for forced-air cooling systems typically focus only on the system’s maximum or average temperature, neglecting the temperature prediction of each individual device. In response, this paper proposes a novel distributed thermal resistance network model for rectangular extruded-fin air cooling heat sink. The model accounts for air temperature rise along the flow path and includes thermal coupling between heat sources, enabling accurate temperature estimation of individual devices. Furthermore, the proposed model is primarily based on analytical formulations, which ensures fast computation and makes it well-suited for rapid thermal evaluation and efficient optimization of cooling system designs. The accuracy of this thermal resistance network is verified through computational fluid dynamics (CFD) simulations and experiments, with an average prediction error of less than 6%, maximum error less than 14%. These results demonstrate that the model can accurately predicted temperature of multiple devices and assist in efficient thermal system design. |
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| ISSN: | 2169-3536 |