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. To overcome these issues for high TDP chips advanced cooling technologies, need to be investigated. Liquid cooling is becoming more mainstream to overcome the some of challenges mentioned above. 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. Highly calibrated Pressure sensors were used at the supply port of the row manifolds and the inlet-outlet of the main hose to measure the supply pressure of ports and pressure drop across the row manifold respectively. Similarly, ultrasonic flow sensors were used to measure the flow rate at each supply port and the main entrance of the row manifold. Two, six-port row manifolds’ P-Q curves were generated, and the value of supply pressure and the flow rate were measured at each port. The results obtained from the experiments were validated by a technique called Flow Network Modeling (FNM). FNM is a 1-D simulation suited for the analysis of flow distribution in liquid cooling systems. The FNM technique uses the overall flow and thermal characteristics to represent the behavior of individual components. Therefore, the solution of conservation equations over the network enables efficient prediction of the flow rates, pressures, and temperatures in a complete liquid-cooling system.

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