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Miniaturization and high heat flux of power electronic devices have posed a colossal challenge for adequate thermal management. Conventional air-cooling solutions are inadequate for high-performance electronics. Liquid cooling is an alternative solution thanks to the higher specific heat and latent heat associated with the coolants. Liquid-cooled cold plates are typically manufactured by different approaches such as: skived, forged, extrusion, electrical discharge machining. When researchers are facing challenges at creating complex geometries in small spaces, 3D-printing can be a solution. In this paper, a 3D-printed cold plate was designed and characterized with water coolant. The printed metal fin structures were strong enough to undergo pressure from the fluid flow even at high flow rates and small fin structures. A copper block with top surface area of 1 inch by 1 inch was used to mimic a computer chip. Experimental data has good match with a simulation model which was built using commercial software 6SigmaET. Effects of geometry parameters and operating parameters were investigated. Fin diameter was varied from 0.3 mm to 0.5 mm and fin height was maintained at 2 mm. A special manifold was designed to maximize the surface contact area between coolant and metal surface and therefore minimize thermal resistance. The flow rate was varied from 0.75 L/min to 2 L/min and coolant inlet temperature was varied from 25 to 48 oC. It was observed that for the coolant inlet temperature 25 oC and aluminum cold plate, the junction temperature was kept below 63.2 oC at input power 350 W and pressure drop did not exceed 23 Kpa. Effects of metal materials used in 3D-printing on the thermal performance of the cold plate were also studied in detail.
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Experimental characterization and geometrical optimization of a commercial two-phase designed cold plate
The increasing prevalence of high-performance computing data centers necessitates the adoption of cutting-edge cooling technologies to ensure the safe and reliable operation of their powerful microprocessors. Two-phase cooling schemes are well-suited for high heat flux scenarios because of their high heat transfer coefficients and their ability to enhance chip temperature uniformity. In this study, we perform experimental characterization and deep learning driven optimization of a commercial two-phase cold plate. The initial working design of the cold plate comprises a fin height of 3mm, fin thickness of 0.1 mm, and a channel width of 0.1 mm.A dielectric coolant, Novec /HFE 7000, was impinged into microchannel fins through impinging jets. A copper block simulated an electronic chip with a surface area of 1˝ × 1˝. The experiment was conducted with three different coolant inlet temperatures of 25◦ C, 36◦ C, and 48◦ C with varying heat flux levels ranging from 7.5 to 73.5 W cm2. The effects of coolant inlet temperatures and flow rate on the thermo-hydraulic performance of the cold plate were explored. In two-phase flow, increasing coolant inlet temperature results in more nucleation sites and improved thermal performance consequently. Thermal resistance drops with flow rate in single-phase flow while it is not affected by flow rate in nucleate boiling region. An improvement in the design of the cold plate was carried out, with the goal of increasing the number of bubble sites and flow velocity at the root fins, by cutting the original fins and creating channels perpendicular to the original channels. Three design parameters, fin height, width of machined channels, and height of short fins preserved through machined channels, were defined. It was observed that widening the machined channels and cutting fins to some point can improve the thermal performance of the cold plate. However, removing fins excessively adversely affects the thermal performance of the cold plate because of loss of heat transfer surface area. Moreover, preserving the short fins through the machined channels decreases thermal resistance as they increase heat transfer surface area and nucleation sites. Furthermore, a deep learning-based compact model is demonstrated for the two-phase cold plate design in the specific range of geometry and flow conditions. The developed compact model is utilized to drive the single and multi-objective optimization to arrive at global optimal results.
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- Award ID(s):
- 2209776
- PAR ID:
- 10529789
- Publisher / Repository:
- Elsevier - International Communications in Heat and Mass Transfer
- Date Published:
- Journal Name:
- International Communications in Heat and Mass Transfer
- Volume:
- 155
- Issue:
- C
- ISSN:
- 0735-1933
- Page Range / eLocation ID:
- 107457
- Subject(s) / Keyword(s):
- Two-phase Heat transfer surface area Bubble sites Thermal resistance Optimization Nucleate boiling Chip temperature uniformity
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.icheatmasstransfer.2024.107457
