scholarly journals Improving Prediction Accuracy Concerning the Thermal Environment of a Data Center by Using Design of Experiments

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4595
Author(s):  
Naoki Futawatari ◽  
Yosuke Udagawa ◽  
Taro Mori ◽  
Hirofumi Hayama
Keyword(s):  
Design Of Experiments ◽  
Data Center ◽  
Prediction Accuracy ◽  
Data Centers ◽  
Cfd Simulation ◽  
Thermal Environment ◽  
Parameter Tuning ◽  
Inlet Temperature ◽  
Cfd Model ◽  
Ventilation Efficiency

In data centers, heating, ventilation, and air-conditioning (HVAC) consumes 30–40% of total energy consumption. Of that portion, 26% is attributed to fan power, the ventilation efficiency of which should thus be improved. As an alternative method for experimentations, computational fluid dynamics (CFD) is used. In this study, “parameter tuning”—which aims to improve the prediction accuracy of CFD simulation—is implemented by using the method known as “design of experiments”. Moreover, it is attempted to improve the thermal environment by using a CFD model after parameter tuning. As a result of the parameter tuning, the difference between the result of experimental-measurement results and simulation results for average inlet temperature of information-technology equipment (ITE) installed in the ventilation room of a test data center was within 0.2 °C at maximum. After tuning, the CFD model was used to verify the effect of advanced insulation such as raised-floor fixed panels and show the possibility of reducing fan power by 26% while keeping the recirculation ratio constant. Improving heat-insulation performance is a different approach from the conventional approach (namely, segregating cold/hot airflow) to improving ventilation efficiency, and it is a possible solution to deal with excessive heat generated in data centers.

Expanded Assessment of a Practical Thermally Aware Energy-Optimized Load Placement Strategy for Open-Aisle, Air-Cooled Data Centers

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
Dustin W. Demetriou ◽  
H. Ezzat Khalifa
Keyword(s):  
Real Time ◽  
Data Center ◽  
Data Centers ◽  
Thermal Environment ◽  
Inlet Temperature ◽  
Airflow Rate ◽  
Time Data ◽  
Improve Energy Efficiency ◽  
Air Temperatures ◽  
Holistic Optimization

This paper expands on the work presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) that investigated practical IT load placement options in open-aisle, air-cooled data centers. The study found that a robust approach was to use real-time temperature measurements at the inlet of the racks to remove IT load from the servers with the warmest inlet temperature. By considering the holistic optimization of the data center load placement strategy and the cooling infrastructure optimization, for a range of data center IT utilization levels, this study investigated the effect of ambient temperatures on the data center operation, the consolidation of servers by completely shutting them off, a complementary strategy to those presented by Demetriou and Khalifa (Demetriou and Khalifa, 2013, “Thermally Aware, Energy-Based Load Placement in Open-Aisle, Air-Cooled Data Centers,” ASME J. Electron. Packag., 135(3), p. 030906) for increasing the IT load beginning with servers that have the coldest inlet temperature and finally the development of load placement rules via either static (i.e., during data center benchmarking) or dynamic (using real-time data from the current thermal environment) allocation. In all of these case studies, by using a holistic optimization of the data center and associated cooling infrastructure, a key finding has been that a significant amount of savings in the cooling infrastructure's power consumption is seen by reducing the CRAH's airflow rate. In many cases, these savings can be larger than providing higher temperature chilled water from the refrigeration units. Therefore, the path to realizing the industry's goal of higher IT equipment inlet temperatures to improve energy efficiency should be through both a reduction in air flow rate and increasing supply air temperatures and not necessarily through only higher CRAH supply air temperatures.

scholarly journals Optimal thermal sensors placement based on indoor thermal environment characterization by using CFD model

2021 ◽  
Vol 19 (3) ◽  
pp. 628-641
Author(s):  
F Faridah ◽  
Sentagi Utami ◽  
Ressy Yanti ◽  
S Sunarno ◽  
Emilya Nurjani ◽  
...  
Keyword(s):  
Euclidean Distance ◽  
Cfd Simulation ◽  
Thermal Environment ◽  
Thermal Characteristics ◽  
Data Sets ◽  
Cfd Model ◽  
Outdoor Climate ◽  
Computational Fluid Dynamics Cfd ◽  
Maximum Root ◽  
Optimum Sensor

This paper discusses an analysis to obtain the optimal thermal sensor placement based on indoor thermal characteristics. The method relies on the Computational Fluid Dynamics (CFD) simulation by manipulating the outdoor climate and indoor air conditioning (AC) system. First, the alternative sensor's position is considered the optimum installation and the occupant's safety. Utilizing the Standardized Euclidean Distance (SED) analysis, these positions are then selected for the best position using the distribution of the thermal parameters' values data at the activity zones. Onsite measurement validated the CFD model results with the maximum root means square error, RMSE, between both data sets as 0.8°C for temperature, the relative humidity of 3.5%, and an air velocity of 0.08m/s, due to the significant effect of the building location. The Standardized Euclidean Distance (SED) analysis results are the optimum sensor positions that accurately, consistently, and have the optimum % coverage representing the thermal condition at 1,1m floor level. At the optimal positions, actual sensors are installed and proven to be valid results since sensors could detect thermal variables at the height of 1.1m with SED validation values of 2.5±0.3, 2.2±0.6, 2.0±1.1, for R15, R33, and R40, respectively.

Experimentally Validated Computational Fluid Dynamics Model for Data Center With Active Tiles

2018 ◽  
Vol 140 (1) ◽  
Author(s):  
Jayati Athavale ◽  
Yogendra Joshi ◽  
Minami Yoda
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Flow Rate ◽  
Data Center ◽  
Hot Spot ◽  
Volume Flow Rate ◽  
Optimal Number ◽  
Inlet Temperature ◽  
Cfd Model ◽  
Level Model

Abstract This paper presents an experimentally validated room-level computational fluid dynamics (CFD) model for raised-floor data center configurations employing active tiles. Active tiles are perforated floor tiles with integrated fans, which increase the local volume flow rate by redistributing the cold air supplied by the computer room air conditioning (CRAC) unit to the under-floor plenum. The numerical model of the data center room consists of one cold aisle with 12 racks arranged on both sides and three CRAC units sited around the periphery of the room. The commercial CFD software package futurefacilities6sigmadcx is used to develop the model for three configurations: (a) an aisle populated with ten (i.e., all) passive tiles; (b) a single active tile and nine passive tiles in the cold aisle; and (c) an aisle populated with all active tiles. The predictions from the CFD model are found to be in good agreement with the experimental data, with an average discrepancy between the measured and computed values for total flow rate and rack inlet temperature less than 4% and 1.7 °C, respectively. The validated models were then used to simulate steady-state and transient scenarios following cooling failure. This physics-based and experimentally validated room-level model can be used for temperature and flow distributions prediction and identifying optimal number and locations of active tiles for hot spot mitigation in data centers.

Raised Floor Hybrid Cooled Data Center: Effect on Rack Inlet Air Temperatures When In Row Cooling Units are Installed Between the Racks

2015 ◽  
Author(s):  
Tianyi Gao ◽  
James Geer ◽  
Russell Tipton ◽  
Bruce Murray ◽  
Bahgat G. Sammakia ◽  
...  
Keyword(s):  
Flow Rate ◽  
Data Center ◽  
Heat Load ◽  
Data Centers ◽  
Cooling System ◽  
Air Flow ◽  
Inlet Temperature ◽  
Air Flow Rate ◽  
Air Temperatures ◽  
Cooling Unit

The heat dissipated by high performance IT equipment such as servers and switches in data centers is increasing rapidly, which makes the thermal management even more challenging. IT equipment is typically designed to operate at a rack inlet air temperature ranging between 10 °C and 35 °C. The newest published environmental standards for operating IT equipment proposed by ASHARE specify a long term recommended dry bulb IT air inlet temperature range as 18°C to 27°C. In terms of the short term specification, the largest allowable inlet temperature range to operate at is between 5°C and 45°C. Failure in maintaining these specifications will lead to significantly detrimental impacts to the performance and reliability of these electronic devices. Thus, understanding the cooling system is of paramount importance for the design and operation of data centers. In this paper, a hybrid cooling system is numerically modeled and investigated. The numerical modeling is conducted using a commercial computational fluid dynamics (CFD) code. The hybrid cooling strategy is specified by mounting the in row cooling units between the server racks to assist the raised floor air cooling. The effect of several input variables, including rack heat load and heat density, rack air flow rate, in row cooling unit operating cooling fluid flow rate and temperature, in row coil effectiveness, centralized cooling unit supply air flow rate, non-uniformity in rack heat load, and raised floor height are studied parametrically. Their detailed effects on the rack inlet air temperatures and the in row cooler performance are presented. The modeling results and corresponding analyses are used to develop general installation and operation guidance for the in row cooler strategy of a data center.

Parametric Study of Hybrid Cooling Solution for Thermal Management of Data Centers

2007 ◽  
Author(s):  
Veerendra Mulay ◽  
Saket Karajgikar ◽  
Dereje Agonafer ◽  
Roger Schmidt ◽  
Madhusudan Iyengar
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Parametric Study ◽  
Heat Load ◽  
Data Centers ◽  
High Heat ◽  
Inlet Temperature ◽  
Air Cooling ◽  
Air Conditioners ◽  
Hybrid Cooling

The power trend for Server systems continues to grow thereby making thermal management of Data centers a very challenging task. Although various configurations exist, the raised floor plenum with Computer Room Air Conditioners (CRACs) providing cold air is a popular operating strategy. The air cooling of data center however, may not address the situation where more energy is expended in cooling infrastructure than the thermal load of data center. Revised power trend projections by ASHRAE TC 9.9 predict heat load as high as 5000W per square feet of compute servers’ equipment footprint by year 2010. These trend charts also indicate that heat load per product footprint has doubled for storage servers during 2000–2004. For the same period, heat load per product footprint for compute servers has tripled. Amongst the systems that are currently available and being shipped, many racks exceed 20kW. Such high heat loads have raised concerns over limits of air cooling of data centers similar to air cooling of microprocessors. A hybrid cooling strategy that incorporates liquid cooling along with air cooling can be very efficient in these situations. A parametric study of such solution is presented in this paper. A representative data center with 40 racks is modeled using commercially available CFD code. The variation in rack inlet temperature due to tile openings, underfloor plenum depths is reported.

Thermodynamics Energy Efficiency Analysis and Thermal Modeling of Data Center Cooling Using Open and Closed-Loop Cooling Systems

2007 ◽  
Author(s):  
Ali Heydari
Keyword(s):  
Heat Exchanger ◽  
Thermal Management ◽  
Data Center ◽  
Closed Loop ◽  
Cfd Simulation ◽  
Open Loop ◽  
Cfd Model ◽  
Spot Cooling ◽  
Coil Heat Exchanger ◽  
Coil Heat

There is a strong need to improve our current capabilities in thermal management and electronic cooling, since estimates indicate that IC power density level could reach 500 W/cm2 in near future. This paper presents several possible closed and open loop cooling schemes for thermal management of electronic equipment in data centers. To be able to identify the overall energy consumption impact, a thermodynamics coefficient of performance (COP) analysis for a data center under each one of the proposed schemes is presented. A limited condition condition 2nd law of thermodynamics thermal efficiency (ηII) analysis of the proposed open-loop schemes is also presented. Using available performance data, the overall data center COP of open and closed-loop cooling schemes is evaluated. Also, the 2nd law efficiency of open-loop schemes is evaluated. To properly design and size the components of a liquid or refrigeration-assisted open or closed-loop cooling scheme requires heat exchanger modeling that need to be incorporated in existing CFD simulation models. For that, analytical modeling of two kinds of direct expansion refrigeration cooling evaporator and a secondary liquid cooling fan coil heat exchanger in conjunction with a computational fluid dynamics (CFD) model to analyze a refrigeration cooled high heat density electronic and computer data center installed on a raised floor is presented. Both models incorporate an accurate tube-by-tube thermal hydraulic modeling of the heat exchanger. The refrigeration coil analysis incorporates a multi region heat exchanger analysis for a more precise modeling of two phase refrigerant flow in the evaporator. The single phase secondary loop fan coil heat exchanger modeling uses an effectiveness method for regional modeling of the spot-cooling coil. Using an iterative method, results of the heat exchanger modeling is simultaneously incorporated in the CFD model and an optimal design of spot cooling heat exchanger is developed. The presented cooling schemes, theoretical thermodynamics analysis along with the detailed thermal-hydraulic heat exchanger simulation in conjunction with the state-of-the-art CFD simulation code should enable data center designers to be able to handle expected increased in heat density of the future data centers.

scholarly journals Impact of Fan Airflow of IT Equipment on Thermal Environment and Energy Consumption of a Data Center

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6166
Author(s):  
Naoki Futawatari ◽  
Yosuke Udagawa ◽  
Taro Mori ◽  
Hirofumi Hayama
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Data Center ◽  
Air Conditioning ◽  
Thermal Environment ◽  
Operating Efficiency ◽  
Inlet Temperature ◽  
Total Energy Consumption ◽  
Air Supply ◽  
Machine Room

Energy-saving in regard to heating, ventilation, and air-conditioning (HVAC) in data centers is strongly required. Therefore, to improve the operating efficiency of the cooling equipment and extend the usage time of the economizer used for cooling information-technology equipment (ITE) in a data center, it is often the case that a high air-supply temperature within the range in which the ITE can be sufficiently cooled is selected. In the meantime, it is known that when the ambient temperature of the ITE rises, the speed of the built-in cooling fan increases. Acceleration of the built-in fan is thought to affect the cooling performance and energy consumption of the data center. Therefore, a method for predicting the temperature of a data center—which simply correlates supply-air temperature with ITE inlet temperature by utilizing existing indicators, such as air-segregation efficiency (ASE)—is proposed in this study. Moreover, a method for optimizing the total energy consumption of a data center is proposed. According to the prediction results obtained under the assumption of certain computer-room air-conditioning (CRAC) conditions, by lowering the ITE inlet temperature from 27 °C to 18 °C, the total energy consumption of the machine room is reduced by about 10%.

scholarly journals Minimizing Thermal Stress for Data Center Servers through Thermal-Aware Relocation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Muhammad Tayyab Chaudhry ◽  
T. C. Ling ◽  
S. A. Hussain ◽  
Atif Manzoor
Keyword(s):  
Thermal Stress ◽  
Data Center ◽  
Data Centers ◽  
Heat Dissipation ◽  
Thermal State ◽  
Inlet Temperature ◽  
Outlet Temperature ◽  
State Monitoring ◽  
Performance Loss ◽  
Cooling Mechanism

A rise in inlet air temperature may lower the rate of heat dissipation from air cooled computing servers. This introduces a thermal stress to these servers. As a result, the poorly cooled active servers will start conducting heat to the neighboring servers and giving rise to hotspot regions of thermal stress, inside the data center. As a result, the physical hardware of these servers may fail, thus causing performance loss, monetary loss, and higher energy consumption for cooling mechanism. In order to minimize these situations, this paper performs the profiling of inlet temperature sensitivity (ITS) and defines the optimum location for each server to minimize the chances of creating a thermal hotspot and thermal stress. Based upon novel ITS analysis, a thermal state monitoring and server relocation algorithm for data centers is being proposed. The contribution of this paper is bringing the peak outlet temperatures of the relocated servers closer to average outlet temperature by over 5 times, lowering the average peak outlet temperature by 3.5% and minimizing the thermal stress.

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

2013 ◽  
Author(s):  
Sami A. Alkharabsheh ◽  
Bharathkrishnan Muralidharan ◽  
Mahmoud Ibrahim ◽  
Saurabh K. Shrivastava ◽  
Bahgat G. Sammakia
Keyword(s):  
Data Center ◽  
Average Error ◽  
Inlet Temperature ◽  
Cfd Model ◽  
Flow Measurements ◽  
Flow Rates ◽  
System Calibration ◽  
Equivalent Models ◽  
Tile Flow ◽  
Good Agreement

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.

Comparison and Evaluation of Different Monitoring Methods in a Data Center Environment

2017 ◽  
Author(s):  
M. Tradat ◽  
S. Khalili ◽  
B. Sammakia ◽  
M. Ibrahim ◽  
Th. Peddle ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Center ◽  
Energy Savings ◽  
Thermal Environment ◽  
Temperature Data ◽  
Environmental Data ◽  
Sensor Data ◽  
Inlet Temperature ◽  
Monitoring Methods ◽  
Potential Difficulty

The operation of today’s data centers increasingly relies on environmental data collection and analysis to operate the cooling infrastructure as efficiently as possible and to maintain the reliability of IT equipment. This in turn emphasizes the importance of the quality of the data collected and their relevance to the overall operation of the data center. This study presents an experimentally based analysis and comparison between two different approaches for environmental data collection; one using a discrete sensor network, and another using available data from installed IT equipment through their Intelligent Platform Management Interface (IPMI). The comparison considers the quality and relevance of the data collected and investigates their effect on key performance and operational metrics. The results have shown the large variation of server inlet temperatures provided by the IPMI interface. On the other hand, the discrete sensor measurements showed much more reliable results where the server inlet temperatures had minimal variation inside the cold aisle. These results highlight the potential difficulty in using IPMI inlet temperature data to evaluate the thermal environment inside the contained cold aisle. The study also focuses on how industry common methods for cooling efficiency management and control can be affected by the data collection approach. Results have shown that using preheated IPMI inlet temperature data can lead to unnecessarily lower cooling set points, which in turn minimizes the potential cooling energy savings. It was shown in one case that using discrete sensor data for control provides 20% more energy savings than using IPMI inlet temperature data.

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