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  1. ; ; ; ; ; ; ; ; ( , Journal of Electronic Packaging)
    Abstract The practice of commissioning data centers (DCs) is necessary to confirm the compliance of the cooling system to the information technology equipment (ITE) load (design capacity). In a typical DC, there are different types of ITE, each having its physical characteristics. Considering these geometrical and internal differences among ITE, it is infeasible to use the actual ITE as a self-simulator. Hence, a separate device called load bank is employed for that purpose. Load banks create a dummy thermal load to analyze, test, and stress the cooling infrastructure. Available commercial load banks do not accurately replicate a server's airflow patternsmore »and transient heat signatures which are governed by thermal inertia, energy dissipation, flow resistance, and fan system behavior. In this study, a novel prototype of the server called server simulator was designed and built with different components to be used as a server mockup. The server simulator accurately captured air resistance, heat dissipation, and the functionality of actual server behavior. Experimental data showed up to 93% improvement in ITE passive and active flow curves using the designed server simulator compared to the commercial load bank. Furthermore, the experimental results demonstrated a below 5% discrepancy on the critical back pressure and free delivery point between the actual ITE and the designed server simulator. In addition, experimental data indicated that the developed server simulator improved the actual ITE thermal mass by 27% compared to the commercial load bank.« less
    Free, publicly-accessible full text available December 1, 2023
  2. ; ; ; ; ; ; ( , Journal of Electronic Packaging)
    Abstract An increasingly common power saving practice in data center thermal management is to swap out air cooling unit blower fans with electronically commutated plug fans, Although, both are centrifugal blowers. The blade design changes: forward versus backward curved with peak static efficiencies of 60% and 75%, respectively, which results in operation power savings. The side effects of which are not fully understood. Therefore, it has become necessary to develop an overall understanding of backward curved blowers and compare the resulting flow, pressure, and temperature fields with forwarding curved ones in which the induced fields are characterized, compared, and visualizedmore »in a reference data center which may aid data center planning and operation when making the decisions of which computer room air handler (CRAH) technology to be used. In this study, experimental and numerical characterization of backward curved blowers is introduced. Then, a physics-based computational fluid dynamics model is built using the 6sigmaroom tool to predict/simulate the measured fields. Five different scenarios were applied at the room level for the experimental characterization of the cooling units and another two scenarios were applied for comparison and illustration of the interaction between different CRAH technologies. Four scenarios were used to characterize a CRAH with backward curved blowers, during which a CRAH with forwarding curved was powered off. An alternate arrangement was examined to quantify the effect of possible flow constraints on the backward curved blower's performance. Then parametric and sensitivity of the baseline modeling are investigated and considered. Different operating conditions are applied at the room level for experimental characterization, comparison, and illustration of the interaction between different CRAH technologies. The measured data is plotted and compared with the computational fluid dynamics (CFD) model assessment to visualize the fields of interest. The results show that the fields are highly dependent on CRAH technology. The tile to CRAH airflow ratios for the flow constraints of scenarios 1, 2, 3, and 4 are 85.5%, 83.9%, 61%, and 59%, respectively. The corresponding leakage ratios are 14.5%, 16%, 38.9%, and 41%, respectively. Furthermore, the validated CFD model was used to investigate and compare the airflow pattern and plenum pressure distribution. Lastly, it is notable that a potential side effect of backward curved technology is the creation of an airflow dead zone.« less
    Free, publicly-accessible full text available September 1, 2023
  3. ; ; ; ; ; ; ( , Applied Energy)
    Free, publicly-accessible full text available January 1, 2023
  4. ; ; ; ; ; ; ( , International Journal of Heat and Mass Transfer)
    Full Text Available 
  5. ; ; ; ; ; ; ; ; ; ( , Control Engineering Practice)
    Full Text Available 
  6. ; ; ; ; ; ( , 2021 37th Semiconductor Thermal Measurement, Modeling & Management Symposium (SEMI-THERM))
    To reproduce a Digital Twin (DT) of a data center (DC), input data is required which is collected through site surveys. Data collection is an important step since accurate representation of a DC depends on capturing the necessary detail for various model fidelity levels of each DC component. However, guidance is lacking in this regard as to which components within the DC are crucial to achieve the level of accuracy desired for the computational model. And determining the input values of the component object parameters is an exercise in engineering judgement during site survey. Sensitivity analysis can be an effectivemore »methodology to determine how the level of simplification in component models can affect the model accuracy.In this study, a calibrated raised-floor DC model is used to study the sensitivity of a DC component's representation to the DC model accuracy. Commercial CFD tool, 6SigmaDC Room is used for modeling and simulation. A total of 8 DC components are considered and eventually ranked on the basis of time and effort required to collect model input data. For parametrized component object, the object's full range of input parameter values are considered, and simulations run. The results are compared with the baseline calibrated model to understand the trade-off between survey effort/cost and model accuracy. For the calibrated DC model and of the 8 components considered, it was observed that the chilled water piping branches, data cables and the cable penetration seal (found within cabinets) have considerable influence on the tile flow rate prediction accuracy.« less
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ( , International Journal of Thermal Sciences)
    Full Text Available 
  8. ; ; ; ; ; ; ( , Journal of Electronic Packaging)
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  9. ; ; ; ( , ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems)
    Given the vital rule of data center availability and since the inlet temperature of the IT equipment increase rapidly until reaching a certain threshold value after which IT starts throttling or shut down because of overheat during cooling system failure. Hence, it is especially important to understand failures and their effects. This study presented experimental investigation and analysis of a facility-level cooling system failure scenario in which chilled water interruption introduced to the data center. Quantitative instrumentation tools including wireless technology such as wireless temperature and pressure sensors were used to measure the discrete air inlet temperature and pressure differentialmore »though cold aisle enclosure, respectively. In addition, Intelligent Platform Management Interface (IPMI) and cooling system data during failure/recovery were reported. Furthermore, the IT equipment performance and response for opened and contained environments were simulated and compared. Finally, an experiment based analysis of the Ride Through Time (RTT) of servers during chilled water interruption of the cooling infrastructure presented as well. The results showed that for all three classes of servers tested during the cooling failure, CAC helped keep the server’s cooler for longer. The containment provided a barrier between the hot and cold air streams and caused slight negative pressure to build up, which allowed the servers to pull cold air from the underfloor plenum. In addition, the results show that the effect of CAC in containment solutions on the IT equipment performance and response could vary and depend on the server’s airflow, generation and hence types of servers deployed in cold aisle enclosure. Moreover, it was shown that when compared to the discrete sensors, the IPMI inlet temperature sensors underestimate the Ride Through Time (RTT) by 42% and 12% for the CAC and opened cases, respectively.« less
    Full Text Available 
  10. ; ; ; ; ; ; ( , ASME Proceedings: Servers of the Future, IoT, and Edge to Cloud)
    There are various designs for segregating hot and cold air in data centers such as cold aisle containment (CAC), hot aisle containment (HAC), and chimney exhaust rack. These containment systems have different characteristics and impose various conditions on the information technology equipment (ITE). One common issue in HAC systems is the pressure buildup inside the HAC (known as backpressure). Backpressure also can be present in CAC systems in case of airflow imbalances. Hot air recirculation, limited cooling airflow rate in servers, and reversed flow through ITE with weaker fan systems (e.g. network switches) are some known consequences of backpressure. Currentlymore »there is a lack of experimental data on the interdependency between overall performance of ITE and its internal design when a backpressure is imposed on ITE. In this paper, three commercial 2-rack unit (RU) servers with different internal designs from various generations and performance levels are tested and analyzed under various environmental conditions. Smoke tests and thermal imaging are implemented to study the airflow patterns inside the tested equipment. In addition, the impact leak of hot air into ITE on the fan speed and the power consumption of ITE is studied. Furthermore, the cause of the discrepancy between measured inlet temperatures by internal intelligent platform management interface (IPMI) and external sensors is investigated. It is found that arrangement of fans, segregation of space upstream and downstream of fans, leakage paths, location of sensors of baseboard management controller (BMC) and presence of backpressure can have a significant impact on ITE power and cooling efficiency.« less
    Full Text Available