Recent advancements on thermal management and evaluation for data centers

A review on airflow management in data centers

A survey on data center cooling systems: Technology, power consumption modeling and control strategy optimization

Effects of airflow on the thermal environment and energy efficiency in raised-floor data centers: A review.

Recently, the rapid growth in both data center power density and scale poses great challenges to the cooling system. On one hand, the data center operators try to over provision cooling resources for fear of server failures induced by accumulated heat. On the other hand, they also want to reduce the energy cost as the cooling system takes up a significant portion of overall energy consumption. Among all available cooling solutions, air cooling dominates the data center industry due to its simpleness. However, its cooling efficiency has been questioned due to the low air density and specific heat. In this paper, we provide an overview for current endeavors to improve the air cooling efficiency. We group existing researches according to the locations where they can be applied from the perspective of air flow cycle. We also discuss the thermal measurement issues. We hope this paper can help researchers and engineers to design and control their data center air cooling systems.

Air Flow Measurement and Management for Improving Cooling and Energy Efficiency in Raised-Floor Data Centers: A Survey

We developed a generalized testing methodology in which IT fans’ internal and external impedance effects are collapsed into a single curve (i.e., flow curve). Such curves can give accurate flow rate predictions of the IT equipment based on RPM logging. Flow curves cover the three possible airflow regions for IT in a contained environment. The experimental procedure is introduced to rank IT equipment based on its air systems, and the flow regions are correlated with the CPU performance. The strength of the IT air system is characterized by the free delivery and the critical pressure points. In addition, the experimentally obtained active flow curves can be used as a numerical boundary condition in various containment designs. Accordingly, for the first time in the literature, the aerodynamic interaction of IT with different air systems in the containment (i.e., BladeCenters, servers, and switches) can numerically be investigated. It is found that stacking IT in a contained environment with no physical consideration can lead to radical reliability problems. The region of reverse flow is discovered to be very possible upon events in the data center. In such a case, the weak IT (e.g., 1U switch) behaves as an open leakage into the sealed containment. On the other hand, at the critical pressure point, minimum forced convection occurs in the weak IT, leading to CPU overheating.

Chip to Facility Ramifications of Containment Solution on IT Airflow and Uptime

As the number of data centers is exponentially growing globally, pragmatic characterization schemes are considered to be a necessity for measuring and modeling the load capacity and flow pattern of the facility. This paper contains experimental and numerical characterization of a new data center laboratory using practical measurements methods, including tiles and CRAH flow measurements. Then a full physics based CFD model is built to simulate/predict the measured data. A rapid flow curve method is used showing high accuracy and low computational expense.Detailed descriptions of the data center structure, dimensions, layout (Appendix, A-1) and flow devices are given. Also, modeling parameters are mentioned in details to provide a baseline for any investigative parametric or sensitivity studies. Four experimental room level flow constraint scenarios are applied at which measurements were taken, (Appendix, A-2). The model is then built and calibrated then used to predict measurements.Measurements of the cooling unit were performed using hot wire anemometry with a traverse duct installed at the top of the CRAH. The tiles measurements were carried out using a flow hood with back pressure compensation. A detailed CFD model is constructed to predict the four experimental cases. For modeling the interdependency between the flow and pressure in flow devices flow curve approach is used. This is a rapid modeling technique that relies on experimentally measured (for IT) or approximated (for CRAH) flow curves. Applying the operational flow curves boundary conditions at the vents of the flow device results in a very accurate simulation model. It is also shown that the flow curves can be used to predict the real-time flow rate of servers at known RPM. This greatly simplifies flow rate measurements of IT in the data center.Copyright © 2015 by ASME

Experimental and Numerical Characterization of a Raised Floor Data Center Using Rapid Operational Flow Curves Model

Cooling failure in data centers (DCs) is a complex phenomenon due to the many interactions between the cooling infrastructure and the information technology equipment (IT). To fully understand it, a system integration philosophy is vital to the testing and design of experiment. In this paper, a facility-level DC cooling failure experiment is run and analyzed. An airside cooling failure is introduced to the facility during two different cooling set points as well as in open and contained environments. Quantitative instrumentation includes pressure differentials, tile airflow, external contour and discrete air inlet temperature, intelligent platform management interface (IPMI), and cooling system data during failure recovery. Qualitative measurements include infrared imaging and airflow visualization via smoke trace. To our knowledge of current literature, this is the first experimental study in which an actual multi-aisle facility cooling failure is run with real IT (compute, network, and storage) load in the white space. This will establish a link between variations from the facility to the central processing unit (CPU). The results show that using the external IT inlet temperature sensors, the containment configuration shows a longer available uptime (AU) during failure. However, the IPMI data show the opposite. In fact, the available uptime is reduced significantly when the external sensors are compared to internal IT analytics. The response of the IT power, CPU temperature, and fan speed shows higher values during the containment failure. This occurs because of the instantaneous formation of external impedances in the containment during failure, which renders the contained aisle to be less resilient than the open aisle. The tradeoffs between PUE, OPEX, and AU are also explained.

Chip to Chiller Experimental Cooling Failure Analysis of Data Centers: The Interaction Between IT and Facility

This study illustrates basic guidelines for integrating empirical measurements to state of the art modeling approaches; that can be used to improve the accuracy of physics based data center CFD models. Experimental measurements gathered in a new research dedicated data center laboratory are used to guide an advanced approach to CFD modeling, the intent is to come up with a validated predictive model of the data center. One of the goals of this study is to provide scientists and engineers with a clear set of design guidelines; that allow for the introduction of important physical details into the model which have previously been largely overlooked. This approach will establish a valid baseline for future designs of data centers as well as future research projects in the data center laboratory. The current CFD model incorporates important physical parameters that affect the accuracy of the results. These include the plenum floor jacks' hydraulic resistance, the impact of cutouts, exact locations of supply vents, CRAH (computer room air handler) unit blower jet effects and momentum transfer through the perforated tiles. Other details of the CRAH unit such as the detailed supply vents, blower specifications, turning vent and angle of flow are included; also rack detailed structure including the mounting rails, cable penetration and PDUs (power distribution units). CFD modeling results are compared to the measured tiles flow rates from several different experimental conditions. The models were refined based on the experimental data and comparisons of the CFD versus experimental tiles flow data showed an overall average error of –3%.

Mark Seymour

Papers

Chip to Facility Ramifications of Containment Solution on IT Airflow and Uptime

Experimental and Numerical Characterization of a Raised Floor Data Center Using Rapid Operational Flow Curves Model

Chip to Chiller Experimental Cooling Failure Analysis of Data Centers: The Interaction Between IT and Facility

Innovative approaches of experimentally guided CFD modeling for data centers

Impact of Tile Design on the Thermal Performance of Open and Enclosed Aisles