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Abstract Data centers have started to adopt immersion cooling for more than just mainframes and supercomputers. Due to the inability of air cooling to cool down recent high-configured servers with higher Thermal Design Power, current thermal requirements in machine learning, AI, blockchain, 5G, edge computing, and high-frequency trading have resulted in a larger deployment of immersion cooling. Dielectric fluids are far more efficient at transferring heat than air. Immersion cooling promises to help address many of the challenges that come with air cooling systems, especially as computing densities increase. Immersion-cooled data centers are more expandable, quicker installation, more energy-efficient, allows for the cooling of almost all server components, save more money for enterprises, and are more robust overall. By eliminating active cooling components such as fans, immersion cooling enables a significantly higher density of computing capabilities. When utilizing immersion cooling for server hardware that is intended to be air-cooled, immersion-specific optimized heat sinks should be used. A heat sink is an important component for server cooling efficacy. This research conducts an optimization of heatsink for immersion-cooled servers to achieve the minimum case temperature possible utilizing multi-objective and multidesign variable optimization with pumping power as the constraint. A high-density server of 3.76 kW was modeled on Ansys Icepak that consists of 2 CPUs and 8 GPUs with heatsink assemblies at their Thermal Design Power along with 32 Dual In-line Memory Modules. The optimization is conducted for Aluminum heat sinks by minimizing the pressure drop and thermal resistance as the objective functions whereas fin count, fin thickness, and heat sink height are chosen as the design variables in all CPUs, and GPUs heatsink assemblies. Optimization for the CPU and the GPU heatsink was done separately and then the optimized heatsinks were tested in an actual test setup of the server in ANSYS Icepak. The dielectric fluid for this numerical study is EC-110 and the cooling is carried out using forced convection. A Design of Experiment (DOE) is created based on the input range of design variables using a full-factorial approach to generate multiple design points. The effect of the design variables is analyzed on the objective functions to establish the parameters that have a greater impact on the performance of the optimized heatsink. The optimization study is done using Ansys OptiSLang where AMOP (Adaptive Metamodel of Optimal Prognosis) as the sampling method for design exploration. The results show total effect values of heat sinks geometric parameters to choose the best design point with the help of a Response Surface 2D and 3D plot for the individual heat sink assembly.
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Pairing factorial design with finite element analysis to model and optimize heat transfer in finned heatsinks
Effective thermal management is critical to many engineering applications, yet identifying optimal heat-transfer designs remains challenging due to complex interactions among material, geometry, and structural parameters. Here, we use a full-factorial design combined with thermal physics finite element simulations to systematically evaluate the effects of five factors—material, fin configuration, geometry, spacing, and thickness—on the time to boil water (τb) in a heatsink-assisted system. Using data from just 32 treatment simulations and a statistically reduced categorical model, we resolve all main effects and interactions, revealing that sparse fin spacing, aluminum material, and thin fins significantly reduce τb. While radial configurations generally outperform linear ones, interaction effects demonstrate that optimum performance depends on specific factor combinations; for example, linear designs can outperform radial ones when paired with certain geometries and materials. Contrary to intuition, neither surface area nor surface-area-to-mass ratio reliably predicts performance due to confounding effects of mass. The best-performing design—an Al-linear-trapezoidal-sparse-thin heatsink—achieved τ^b=618±2s, while other optimal designs emerged under constraints such as reduced mass or manufacturing simplicity. This study underscores the value of factorial design in navigating complex design spaces and optimizing thermal performance, offering a powerful framework for the development of next-generation heat transfer systems.
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- Award ID(s):
- 2128671
- PAR ID:
- 10649670
- Publisher / Repository:
- American Institute of Physics
- Date Published:
- Journal Name:
- Journal of Applied Physics
- Volume:
- 138
- Issue:
- 20
- ISSN:
- 0021-8979
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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