Saurabh K. Shrivastava

Comparative Analysis of Different Data Center Airflow Management Configurations

Increase in computing power resulting from high performance microprocessors, packages, and modules and the deployment of high heat load computer rack units in high density configurations, has escalated the thermal challenges in today’s data center systems. One of the key issues is the location of hot recirculation regions in the room and the mixing of hot rack exhaust air with the cold supply air. Along with many factors such as the rack heat load and the cooling capacity of the supply air, the data center thermal management architecture plays an important role in determining the reliability of the electronic equipment and the general thermal performance of the data center. There are several candidate configurations available for the air ducting designs for data centers. The overall energy efficiency of the system is highly dependant upon the selection of the specific configuration. This paper will summarize the results of a broad numerical study carried out to assess the effectiveness of different data center configurations. The numerical modeling is performed using a commercial computational fluid dynamics (CFD) code based on finite volume approach. The configurations studied include different combinations of raised floor and ceiling supply and return vent location subject to specific constraints. The performance of the data center has been characterized on the basis of average and maximum mean region rack inlet air temperature. Among the seven different configurations compared, the raised floor/ceiling return type configuration is found to be the most effective configuration for the given set of constraints and assumptions.Copyright

Experimental-Numerical Comparison for a High-Density Data Center: Hot Spot Heat Fluxes in Excess of 500 W/FT2

The current trend of using denser server environments is continuously increasing to satisfy the growing needs of e-commerce and other emerging technologies. The resulting high room-level heat fluxes present significant challenges with respect to maintaining acceptable computer rack inlet temperatures and minimizing total data center energy consumption. Numerical methods are widely used to model existing and new facilities. Validation of existing numerical techniques is an important step in facilitating good thermal design of data centers. This paper uses previously published experimental data to present a comparison between test results and numerical simulations. The example considered is a large 7400 ft2 data canter that houses over 130 heat-producing racks (1.2 MW) and 12 air conditioning units. Localized hot spot heat fluxes were measured to be as high as 512 W/ft2 (5.5 kW/m2) for a 400 ft (37 m) region. A numerical model based on computational fluid dynamics (CFD) was constructed using inputs from the measurements. The rack inlet air temperature was considered to be the basis for experimental vs. numerical comparison. The overall mean rack inlet temperature predicted numerically at a height of 1.75 m is within 4degC of the test data with a rack-by-rack standard deviation of 3.3 degC

Quantitative comparison of air containment systems

Data center cooling energy efficiency is critical to successful operation of modern large data centers. The current trend of deploying high heat load density cabinets in data centers necessitates the use of air containment systems. According to a recent poll (by Gartner), about 80% of the data centers already have or are planning to deploy a containment system. There are primarily two types of air containment systems: hot air containment and cold air containment. This paper compares the performance and cooling energy costs of different types of air containment systems. It also includes the set of guidelines that should be considered when selecting one containment type over the other. In order to compare the performance of standard Hot Aisle/Cold Aisle (HA/CA) arrangement vs. Containment Systems we have selected a comprehensive data center layout. This layout contains a mix of equipment (Server, Networking, and Storage) which is characteristic of a typical data center facility. In this paper we will first determine the cooling needs of the equipment and establish the associated set point conditions for the CRAH (Computer Room Air Handlers) units, and then later consider different types of chilled water systems to do the comparative analysis.

Thermal Characterization of Non-Raised Floor Air Cooled Data Centers Using Numerical Modeling

Data center equipment almost always represents a high expenditure capital investment to the customer, and is often operated without any down time. Data com equipment is typically designed to operate at a rack air inlet temperature of between 10 and 35°C, and a violation of this specification can diminish electronic device reliability and even lead to failure in the field. Thus, it is of paramount importance, from a reliability perspective, to sufficiently understand these systems. A representative non-raised floor data center system was numerically modeled and the data generated from a parametric study was analyzed. The model constitutes a half symmetry section of a 40 rack data center that is arranged in a cold aisle-hot aisle fashion. The effect of several input variables, namely, rack heat load, rack flow rate, rack temperature rise, diffuser flow rate, diffuser location, diffuser height, diffuser pitch, ceiling height, hot exhaust air return vent location, and non-uniformity in rack heat load, was studied. Temperature data was collected at several locations at the inlet to the racks. Statistical analysis was carried out to describe trends in the data.Copyright

Thermal mass characterization of a server at different fan speeds

Dynamic cooling has been proposed as one approach for enhancing the energy efficiency of data center facilities. It involves using sensors to closely monitor the data center environment with time and making real time decisions on how to allocate the cooling resources based on the location of hotspots and concentration of workloads. In order to effectively implement this approach, it is good to know the transient thermal response of the various systems comprising the data center must be determined, which is a function of thermal mass. Not only is thermal mass important in dynamic cooling, it also plays a major role in the temperature rise of a data center during power failure. A previous study concentrated on characterizing the thermal mass of a 2 RU server by running the server at different powers and a fixed fan speed. The fixed fan speed corresponds to one specific heat transfer coefficient value. This study is a continuation to the previous work, where the server fan speed is varied to deduce the change in heat transfer coefficient at different airflow rates. As expected, the heat transfer coefficient increases as the server airflow rate increases. The average thermal mass value obtained for the 2 RU server in this study was 12 kJ/K. A method of adopting the compact model developed in this study into a Computational Fluid Dynamics (CFD) code is proposed to cut down on the computational time of transient analysis.

Prediction of Cold Aisle End Airflow Boundary Conditions Using Regression Modeling

This paper is a continuation of the development of software tools that estimate, in real time or in near-real time, the cooling performance of a cluster of racks bounding a common cold aisle in a raised-floor data center environment. A fundamental assumption within the algorithm of these tools is that the computation of airflow patterns inside the cold aisle can be decoupled from the room environment. The effect of the room environment impacts the solution in the estimation of the cooling performance primarily through the airflow boundary conditions prescribed at the ends of the cold aisle. Consequently, the accuracy of the cooling-performance tool is directly linked to the accuracy of the end airflow prediction for any room environment. The end airflow is a complex function of many factors including the location and airflow rate of each rack, the perforated-tile airflow rate, and room environment conditions. As shown here, the dominant room-environment parameter is the difference between ambient and supply air temperatures. This paper describes the model developed to estimate the end airflow rate. End airflow values are calculated from several hundred computational fluid dynamics (CFD) scenarios covering a broad range of rack airflow (and power) distributions, tile flow rates, and room environments. An end airflow model is developed based on a regression analysis from the CFD data, which facilitates the real-time prediction of the end airflow for any practical cluster layout and room environment. The difference between accepted and predicted end airflow values is typically less than 25% of the accepted value or per-tile airflow rate.

Benefits of properly sealing a cold aisle containment system

Data centers are mission-critical facilities and the nerve center of successful business operations. Surging demand for processing power, work load virtualization and consolidation is increasing data center heat loads, making the thermal management of data centers challenging. Containing the air in a data center is an important energy savings strategy towards data center optimization. Most of the modern, energy efficient, data centers deploy some kind of containment system. This paper discusses test data for a Cold Aisle Containment (CAC) system and compares it with a standard Hot Aisle/Cold Aisle (HA/CA) configuration. The HA/CA configuration is shown to support a heat load of 14.6kW/cabinet while the CAC system was tested up to 25.2 kW/cabinet. In addition, the test data demonstrated the cooling energy savings with the CAC system. Furthermore, we quantified the importance and effectiveness of sealing accessories (i.e. grommets for cable openings and blanking panels) when deployed in a CAC environment.

Partially Decoupled Aisle Method for Estimating Rack-Cooling Performance in Near-Real Time

The Partially Decoupled Aisle (PDA) method facilitates a near-real-time cooling-performance analysis of a single cluster of racks and, potentially, coolers, bounding a common hot or cold aisle in a data center. With the PDA method, the airflow patterns and related variables need be computed only within an isolated cold or hot aisle “on the fly” through CFD analysis or other means. The analysis is fast because the much larger surrounding room environment is not directly modeled; its effect enters the model through the boundary conditions applied to the top and ends of the isolated aisle. The proper boundary conditions in turn may be estimated from an empirical model determined in advance (“offline”) from the study of a large number of CFD simulations of varying equipment layouts and room environments. A software tool based on the PDA method, which uses a full CFD engine to solve the aisle airflow within the isolated aisle, can analyze a typical cluster of racks and coolers in 10–30 seconds and requires no special user skills. This paper formally introduces the general PDA method and shows several examples of its application with comparisons to corresponding whole-room CFD analyses.Copyright

Data Center Cooling Prediction Using Artificial Neural Network

An analytical approach using artificial intelligence has been developed for assessing the cooling performance of data centers. This paper discusses the use of a Neural Network (NN) model in the real-time prediction of the cooling performance of a cluster of equipment in a data center environment. The NN model is used to predict the Capture Index (CI) [1] as a function of rack power, cooler airflow and physical/geometric arrangement for a cluster located in a simple room environment. The Neural Network is “trained” on thousands of hypothetical but realistic cluster variations for which CI values have been computed using either PDA [2] or full Computational Fluid Dynamics (CFD). The great value of the NN approach lies in its ability to capture the non-linear relationships between input parameters and corresponding capture indices. The accuracy of the NN approach is 3.8% (Root Mean Square Error) for a set of example scenarios discussed here. Because of the real-time nature of the calculations, the NN approach readily facilitates optimization studies. Example cases are discussed which show the integration of the NN approach and a genetic algorithm used for optimization.© 2007 ASME

Open and Contained Cold Aisle Experimentally Validated CFD Model Implementing CRAC and Server Fan Curves for a Data Center Test Laboratory

This paper presents the results of an experimentally validated Computational Fluid Dynamics (CFD) model for a data center with fully implemented fan curves on both the servers and the Computer Room Air Conditioner (CRAC). Open and contained cold aisle systems are considered experimentally and numerically. This work is divided into open (uncontained) cold aisle system calibration and validation, and fully contained cold aisle system calibration and leakage characterization.In the open system, the CRAC unit is calibrated using the manufacturer fan curve. Tiles flow measurements are used to calibrate the floor leakage. The fan curves of the load banks are generated experimentally. A full physics based model of the system is validated with two different CRAC fan speeds. The results showed a very good agreement with the tile flow measurements, with an approximate average error of 5%, indicating that the average model prediction of the tile flow is five percent lower that the measured values.In the fully contained cold aisle system, a detailed containment CFD model based on experimental measurements is developed. The model is validated by comparing the flow rate through the perforated floor tiles with the experimental measurements. The CFD results are in a good agreement with the experimental data. The average error is about 6.7%. Temperature measurements are used to calibrate other sources of containment and racks leaks including mounting rails and clearance between racks. The temperature measurements and the CFD results agree well with average error less than 2%.Detailed and equivalent modeling methods for the floor and containment system are investigated. It is found that the simple equivalent models are able to predict the flow rates however they did not succeed in providing detailed fluid flow information. While the detailed models succeeded in explaining the physical phenomena and predicting the flow rates with noticeable tradeoffs regarding the computational time.Important conclusions can be drawn from this study. In order to accurately model the containment system, both the CRAC and the load banks fan curves should be simulated in the numerical model. Unavoidable racks and containment leaks could cause inlet temperature increase even if the cold aisle is overprovisioned with cold air. It is also noted that heat conduction through the floor tiles causes a slight increase the inlet temperature of the cold aisles. Finally, it is noteworthy that using detailed modeling is necessary to understand the details of the thermal systems, however simpler and faster to compute equivalent models can be used in extended optimization studies that show relative rankings of different designs.Copyright