five

MicroAnalytix

收藏
Mendeley Data2024-06-26 更新2024-06-26 收录
下载链接:
https://data.mendeley.com/datasets/gwnt75p22n
下载链接
链接失效反馈
官方服务:
资源简介:
The increasing demand for efficient cooling solutions in diverse industries has boosted extensive research into microchannel cooling technologies. This paper explores the validation and utilization of an analytical tool, the Thermal-Hydrodynamic Model (THM), specifically designed to expedite microchannel cooling. Findings underscore the effectiveness of the THM in accurately estimating thermal resistances and pressure drops for manifold, straight, and serpentine configurations within acceptable error margins. The THM predicts critical parameters, including electronic package temperatures, temperature differences across packages, thermal resistances, and pressure drops across microchannel sections, enabling rapid design iterations. Moreover, influential factors are identified to assess the validity of the obtained results. Pressure drop estimates for straight channels consistently remain within a 10% error margin compared to numerical simulations, while serpentine microchannels met this criterion for Dean numbers below 40. Manifold configurations, however, do not meet the 10% criterion. For predictions within a 15% error margin, an Inlet Ratio below 0.13 and an assumed Velocity Ratio of unity and low Reynolds numbers are necessary. Additionally, for thermal resistance estimations across all configurations, a number of grooves below 23 is required to maintain 10% validity. Additionally, a case study demonstrates the potential application of the THM to identify the optimal configuration of a cold plate design, resulting in cooling power requirements at least two times lower than other configurations in which the mass flow is minimized. These findings highlight the THM’s potential as an alternative to simulation-based approaches for fast estimation of pressure drop and thermal properties of microchannel cold plate design
创建时间:
2024-06-26
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作