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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