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Abstract In today’s world, most data centers have multiple racks with numerous servers in each of them. The high amount of heat dissipation has become the largest server-level cooling problem for the data centers. The higher dissipation required, the higher is the total energy required to run the data center. Although still the most widely used cooling methodology, air cooling has reached its cooling capabilities especially for High-Performance Computing data centers. Liquid-cooled servers have several advantages over their air-cooled counterparts, primarily of which are high thermal mass, lower maintenance. Nano-fluids have been used in the past for improving the thermal efficiency of traditional dielectric coolants in the power electronics and automotive industry. Nanofluids have shown great promise in improving the convective heat transfer properties of the coolants due to a proven increase in thermal conductivity and specific heat capacity. The present research investigates the thermal enhancement of the performance of de-ionized water-based dielectric coolant with Copper nanoparticles for a higher heat transfer from the server cold plates. Detailed 3-D modeling of a commercial cold plate is completed and the CFD analysis is done in a commercially available CFD code ANSYS CFX. The obtained results compare the improvement in heat transfer due to improvement in coolant properties with data available in the literature. 

Abstract Increased demand for computer applications has manifested a rise in data generation, resulting in high Power Density and Heat Generation of servers and their components, requiring efficient thermal management. Due to the low heat carrying capacity of air, air cooling is not an efficient method of data center cooling. Hence, the liquid immersion cooling method has emerged as a prominent method, where the server is directly immersed in a dielectric liquid. The thermal conductivity of the dielectric liquids is drastically increased with the introduction of non-metallic nanoparticles of size between 1 to 150 nm, which has proven to be the best method. To maintain the dielectric feature of the liquid, non-metallic nanoparticles can be added. Alumina nanoparticles with a mean size of 80 nm and a mass concentration of 0 to 5% with mineral oil are used in the present study. The properties of the mixture were calculated based on the theoretical formula and it was a function of temperature. Heat transfer and effect of the nanoparticle concentration on the junction temperature of the processors using CFD techniques were simulated on an open commute server with two processors in a row. The junction temperature was studied for different flow rates of 0.5, 1, 2, and 3 LPM, at inlet temperatures of 25, 35, and 45 degrees Celsius. The chosen heatsink geometries were: Parallel plate, Pin fin, and Plate fin heatsinks. 

Abstract With more development in electronics system capable of having larger functional densities, power density is increasing. Immersion cooling demonstrates the highest power usage efficiency (PUE) among all cooling techniques for data centers and there is still interest in optimizing immersion cooling to use it to its full potential. The aim of this paper is to present the effect of inclination and thermal shadowing on two-phase immersion cooling using FC-72. For simulation of boiling, the RPI (Rensselaer Polytechnic Institute) wall boiling model has been used. Also, two empirical models were used for calculation of bubble departure diameter and nucleate site density. The boundary condition was assumed to be constant heat flux and the bath temperature was kept at boiling temperature of FC-72 and the container pressure is assumed to be atmospheric. this study showed that due to the thermal shadowing, boiling boundary layer can lay over the top chipset and increases vapor volume fraction over top chipsets. This ultimately causes increase in maximum temperature of second chip. The other main observation is with higher inclination angle of chip, maximum temperature on the chip decreases up to 3°C. 

Abstract Modern-day data center administrators are finding it increasingly difficult to lower the costs incurred in mechanical cooling of their IT equipment. This is especially true for high-performance computing facilities like Artificial Intelligence, Bitcoin Mining, and Deep Learning, etc. Airside Economization or free air cooling has been out there as a technology for a long time now to reduce the mechanical cooling costs. In free air cooling, under favorable ambient conditions of temperature and humidity, outside air can be used for cooling the IT equipment. In doing so, the IT equipment is exposed to sub-micron particulate/gaseous contaminants that might enter the data center facility with the cooling airflow. The present investigation uses a computational approach to model the airflow paths of particulate contaminants entering inside the IT equipment using a commercially available CFD code. A Discrete Phase Particle modeling approach is chosen to calculate trajectories of the dispersed contaminants. Standard RANS approach is used to model the airflow in the airflow and the particles are superimposed on the flow field by the CFD solver using Lagrangian particle tracking. The server geometry was modeled in 2-D with a combination of rectangular and cylindrical obstructions. This was done to comprehend the effect of change in the obstruction type and aspect ratio on particle distribution. Identifying such discrete areas of contaminant proliferation based on concentration fields due to changing geometries will help with the mitigation of particulate contamination related failures in data centers.