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Due to their lower pressure drop, impinging cold-plates are preferred over parallel flow cold-plates when there is no strict space limitation (i.e. when flow can enter perpendicular to the electronic board). Splitting the flow into two branches cuts the flow rate and path in half, which leads to lower pressure drop through the channels. A groove is used to direct the flow exiting the diffuser into the channels. The number of the geometric design parameters of the cold-plate will vary depending on the shape of the groove. In this research, the response surface method (RSM) was used to optimization the fin geometry of an impinging cold-plate with a trapezoidal cross section groove. The cold plate is used for warm water cooling of electronics. Three fin parameters (thickness, height, and gap) and three groove parameters were optimized to reach minimum values for hydraulic and thermal resistances at fixed values of coolant inlet temperature, coolant flow rate, and electronic chip power.
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This content will become publicly available on August 20, 2026
Cold Plate Optimization with Entropy Minimization Using Bayesian Optimization
The rapid advancement of 5G technology necessitates the development of efficient thermal management solutions to handle the increased heat dissipation demands of high-power electronic components. This study presents an optimization strategy for a microchannel cold plate designed for a prototype 5G front-end system, featuring four 22-Watt chips as heat sources. The cold plate, constructed from aluminum, incorporates multiple rectangular flow channels evenly spaced to facilitate uniform heat distribution, with an inlet runner. The primary objective of this study is to optimize the geometry of the flow channels and the coolant mass flow rate at the runner entrance to minimize entropy generation, thereby enhancing the heat dissipation capability of the cold plate while minimizing pressure drop. Given these challenges, this study aims to develop an optimization strategy for cold plate design. This research applies Bayesian Optimization (BO), and Response Surface Methodology (RSM) paired with Genetic Algorithm (GA), and FMINCON (sequential quadratic programming, a built-in optimizer of MATLAB). These methods are utilized to fine-tune the channel dimensions and coolant flow rate, and the data that is used to evaluate entropy of the system is obtained from conjugate heat transfer simulations solved by Ansys Fluent. By using the Gaussian Process model to build response surface and predicting function of entropy generation, the results indicate that BO outperforms RSM paired with GA and FMINCON in terms of entropy reduction with same number of samples.
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- Award ID(s):
- 2329206
- PAR ID:
- 10631181
- Publisher / Repository:
- Proceedings of the ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Date Published:
- Edition / Version:
- 1
- Format(s):
- Medium: X
- Location:
- Anaheim, CA
- Sponsoring Org:
- National Science Foundation
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