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Abstract Demand is growing for the dense and high-performing IT computing capacity to support artificial intelligence, deep learning, machine learning, autonomous cars, the Internet of Things, etc. This led to an unprecedented growth in transistor density for high-end CPUs and GPUs, creating thermal design power (TDP) of even more than 700 watts for some of the NVIDIA existing GPUs. Cooling these high TDP chips with air cooling comes with a cost of the higher form factor of servers and noise produced by server fans close to the permissible limit. Direct-to-chip cold plate-based liquid cooling is highly efficient and becoming more reliable as the advancement in technology is taking place. Several components are used in the liquid-cooled data centers for the deployment of cold plate-based direct-to-chip liquid cooling like cooling loops, rack manifolds, CDUs, row manifolds, quick disconnects, flow control valves, etc. Row manifolds used in liquid cooling are used to distribute secondary coolant to the rack manifolds. Characterizing these row manifolds to understand the pressure drops and flow distribution for better data center design and energy efficiency is important. In this paper, the methodology is developed to characterize the row manifolds. Water-based coolant Propylene glycol 25% was used as the coolant for the experiments and experiments were conducted at 21 °C coolant supply temperature. Two, six-port row manifolds' P-Q curves were generated, and the value of supply pressure and the flowrate were measured at each port. The results obtained from the experiments were validated by a technique called flow network modeling (FNM). FNM technique uses the overall flow and thermal characteristics to represent the behavior of individual components. 

Abstract The increasing demand for high-performance computing in applications such as the Internet of Things, Deep Learning, Big data for crypto-mining, virtual reality, healthcare research on genomic sequencing, cancer treatment, etc. have led to the growth of hyperscale data centers. To meet the cooling energy demands of HPC datacenters efficient cooling technologies must be adopted. Traditional air cooling, direct-to-chip liquid cooling, and immersion are some of those methods. Among all, Liquid cooling is superior compared to various air-cooling methods in terms of energy consumption. Direct on-chip cooling using cold plate technology is one such method used in removing heat from high-power electronic components such as CPUs and GPUs in a broader sense. Over the years Thermal Design Power (TDP) is rapidly increasing and will continue to increase in the coming years for not only CPUs and GPUs but also associated electronic components like DRAMs, Platform Control Hub (PCH), and other I/O chipsets on a typical server board. Therefore, unlike air hybrid cooling which uses liquid for cold plates and air as the secondary medium of cooling the associated electronics, we foresee using immersion-based fluids to cool the rest of the electronics in the server. The broader focus of this research is to study the effects of adopting immersion cooling, with integrated cold plates for high-performance systems. Although there are several other factors involved in the study, the focus of this paper will be the optimization of cold plate microchannels for immersion-based fluids in an immersion-cooled environment. Since immersion fluids are dielectric and the fluids used in cold plates are conductive, it exposes us to a major risk of leakage into the tank and short-circuiting the electronics. Therefore, we propose using the immersed fluid to pump into the cold plate. However, it leads to a suspicion of poor thermal performance and associated pumping power due to the difference in viscosity and other fluid properties. To address the thermal and flow performance, the objective is to optimize the cold plate microchannel fin parameters based on thermal and flow performance by evaluating thermal resistance and pressure drop across the cold plate. The detailed CFD model and optimization of the cold plate were done using Ansys Icepak and Ansys OptiSLang respectively. 

Abstract Due to the increasing computational demand driven by artificial intelligence, machine learning, and the Internet of Things (IoT), there has been an unprecedented growth in transistor density for high-end CPUs and GPUs. This growth has resulted in high thermal dissipation power (TDP) and high heat flux, necessitating the adoption of advanced cooling technologies to minimize thermal resistance and optimize cooling efficiency. Among these technologies, direct-to-chip cold plate-based liquid cooling has emerged as a preferred choice in electronics cooling due to its efficiency and cost-effectiveness. In this context, different types of single-phase liquid coolants, such as propylene glycol (PG), ethylene glycol (EG), DI water, treated water, and nanofluids, have been utilized in the market. These coolants, manufactured by different companies, incorporate various inhibitors and chemicals to enhance long-term performance, prevent biogrowth, and provide corrosion resistance. However, the additives used in these coolants can impact their thermal performance, even when the base coolant is the same. This paper aims to compare these coolant types and evaluate the performance of the same coolant from different vendors. The selection of coolants in this study is based on their performance, compatibility with wetted materials, reliability during extended operation, and environmental impact, following the guidelines set by ASHRAE. To conduct the experiments, a single cold plate-based benchtop setup was constructed, utilizing a thermal test vehicle (TTV), pump, reservoir, flow sensor, pressure sensors, thermocouple, data acquisition units, and heat exchanger. Each coolant was tested using a dedicated cold plate, and thorough cleaning procedures were carried out before each experiment. The experiments were conducted under consistent boundary conditions, with a TTV power of 1000 watts and varying coolant flow rates (ranging from 0.5 lpm to 2 lpm) and supply coolant temperatures (17°C, 25°C, 35°C, and 45°C), simulating warm water cooling. The thermal resistance (Rth) versus flow rate and pressure drop (ΔP) versus flow rate graphs were obtained for each coolant, and the impact of different supply coolant temperatures on pressure drop was characterized. The data collected from this study will be utilized to calculate the Total Cost of Ownership (TCO) in future research, providing insights into the impact of coolant selection at the data center level. There is limited research available on the reliability used in direct-to-chip liquid cooling, and there is currently no standardized methodology for testing their reliability. This study aims to fill this gap by focusing on the reliability of coolants, specifically propylene glycols at concentrations of 25%. To analyze the effectiveness of corrosion inhibitors in these coolants, ASTM standard D1384 apparatus, typically used for testing engine coolant corrosion inhibitors on metal samples in controlled laboratory settings, was employed. The setup involved immersing samples of wetted materials (copper, solder coated brass, brass, steel, cast iron, and cast aluminum) in separate jars containing inhibited propylene glycol solutions from different vendors. This test will determine the reliability difference between the same inhibited solutions from different vendors.