Improving Energy Efficiency Through Thermal Control of a Modular Data Center

2012 ◽  
Author(s):  
John Guinn ◽  
Srujan Gondipalli
Keyword(s):  
Heat Exchanger ◽  
Data Center ◽  
Data Centers ◽  
Water Flow Rate ◽  
Thermal Control ◽  
Cooling Capacity ◽  
Thermal Control System ◽  
Modular Data ◽  
Fan Speed ◽  
Usage Efficiency

As the IT industry’s demand for greater power density racks grows, the operating cost associated with power and cooling IT equipment remains an ever present concern facing data centers. A key component in reducing Total Cost of Ownership (TCO) for a facility is to optimize their cooling system design. Hewlett-Packard has developed a self contained enclosure for high density servers and mass storage devices called a Modular Data Center (MDC). This unit is intended to reduce issues that have plagued large scale data centers by increasing cooling capacity and efficiency. This paper takes a look at the thermal control logic for the MDC and explains ways to decrease energy costs by developing a thermal control scheme centered on optimizing Power Usage Efficiency (PUE). Tests were conducted to understand the relationships between fan power and fan speed, facility power and thermal capacity. Areas of large power drains were isolated and analyzed. Tests showed that there are two parts in managing power usage on the MDC, system and facility control. In the development of a smarter control algorithm, the fans and water valve (“system”) performance curves provided a road map to the hardware’s capability. This was accomplished by understanding how key variables such as inlet water temperature, water flow rate and fan speed impact the behavior of server inlet air temperature and cooling capacity. Facility control comes from optimizing what equipment is in place to support the MDC (i.e. dedicated chiller, campus chiller, pumps, etc…) within a data center. A significant goal of this project was to minimize the dependency MDC has on external cooling by optimizing the variables that affect facility power. For instance controlling heat removal rate and exiting water temperature affects chiller power; while water flow rate affects pump power. Knowledge of your system and facility’s capabilities directly impacts power management. Thermal performance testing of the heat exchanger in the MDC provided insight into how increasing thermal efficiency at the heat exchanger produced an overall drop in facility power. Tests revealed that the optimized thermal control system achieved an infrastructure energy savings up to 33% with a PUE improvement from 1.35 to 1.23 for a 100KW IT heat load. The results show that characterizing and incorporating the behavior of the fans and heat exchanger into the thermal control system produced an improved Power Usage Efficiency (PUE) and a smarter control method.

Exergy Analysis of Data Center Thermal Management Systems

2003 ◽  
Author(s):  
Amip J. Shah ◽  
Van P. Carey ◽  
Cullen E. Bash ◽  
Chandrakant D. Patel
Keyword(s):  
Thermal Management ◽  
Data Center ◽  
Data Centers ◽  
Exergy Analysis ◽  
Thermal Control ◽  
Management Systems ◽  
World Systems ◽  
Sample Data ◽  
Potential Applications ◽  
Temperature Ranges

Data centers today contain more computing and networking equipment than ever before. As a result, a higher amount of cooling is required to maintain facilities within operable temperature ranges. Increasing amounts of resources are spent to achieve thermal control, and tremendous potential benefit lies in the optimization of the cooling process. This paper describes a study performed on data center thermal management systems using the thermodynamic concept of exergy. Specifically, an exergy analysis has been performed on sample data centers in an attempt to identify local and overall inefficiencies within thermal management systems. The development of a model using finite volume analysis has been described, and potential applications to real-world systems have been illustrated. Preliminary results suggest that such an exergy-based analysis can be a useful tool in the design and enhancement of thermal management systems.

scholarly journals Thermal Performance and Energy Saving Analysis of Indoor Air–Water Heat Exchanger Based on Micro Heat Pipe Array for Data Center

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 393 ◽  
Author(s):  
Heran Jing ◽  
Zhenhua Quan ◽  
Yaohua Zhao ◽  
Lincheng Wang ◽  
Ruyang Ren ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Heat Exchanger ◽  
Energy Saving ◽  
Heat Pipe ◽  
Flow Rate ◽  
Data Center ◽  
Data Centers ◽  
Air Flow ◽  
Air Flow Rate ◽  
Cold Energy

According to the temperature regulations and high energy consumption of air conditioning (AC) system in data centers (DCs), natural cold energy becomes the focus of energy saving in data center in winter and transition season. A new type of air–water heat exchanger (AWHE) for the indoor side of DCs was designed to use natural cold energy in order to reduce the power consumption of AC. The AWHE applied micro-heat pipe arrays (MHPAs) with serrated fins on its surface to enhance heat transfer. The performance of MHPA-AWHE for different inlet water temperatures, water and air flow rates was investigated, respectively. The results showed that the maximum efficiency of the heat exchanger was 81.4% by using the effectiveness number of transfer units (ε-NTU) method. When the max air flow rate was 3000 m3/h and the water inlet temperature was 5 °C, the maximum heat transfer rate was 9.29 kW. The maximum pressure drop of the air side and water side were 339.8 Pa and 8.86 kPa, respectively. The comprehensive evaluation index j/f1/2 of the MHPA-AWHE increased by 10.8% compared to the plate–fin heat exchanger with louvered fins. The energy saving characteristics of an example DCs in Beijing was analyzed, and when the air flow rate was 2500 m3/h and the number of MHPA-AWHE modules was five, the minimum payback period of the MHPA-AWHE system was 2.3 years, which was the shortest and the most economical recorded. The maximum comprehensive energy efficiency ratio (EER) of the system after the transformation was 21.8, the electric power reduced by 28.3% compared to the system before the transformation, and the control strategy was carried out. The comprehensive performance provides a reference for MHPA-AWHE application in data centers.

Transient Thermal Performance of Rear Door Heat Exchanger in Local Contained Environment During Water Side Failure

2017 ◽  
Author(s):  
Kourosh Nemati ◽  
Husam A. Alissa ◽  
Mohammad I. Tradat ◽  
Bahgat Sammakia
Keyword(s):  
Heat Exchanger ◽  
Thermal Performance ◽  
Data Centers ◽  
Water Flow Rate ◽  
Thermal Response ◽  
Outlet Temperature ◽  
Rear Door ◽  
Liquid Systems ◽  
Transient Thermal ◽  
The Impact

The constant increase in data center computational and processing requirements has led to increases in the IT equipment power demand and cooling challenges of high-density (HD) data centers. As a solution to this, the hybrid and liquid systems are widely used as part of HD data centers thermal management solutions. This study presents an experimental based investigation and analysis of the transient thermal performance of a stand-alone server cabinet. The total heat load of the cabinet is controllable remotely and a rear door heat exchanger is attached with controllable water flow rate. The cooling performances of two different failure scenarios are investigated. One is in the water chiller and another is in the water pump for the Rear Door Heat eXchanger (RDHX). In addition, the study reports the impact of each scenario on the IT equipment thermal response and on the cabinet outlet temperature using a mobile temperature and velocity mesh (MTVM) experimental tool. Furthermore, this study also addresses and characterizes the heat exchanger cooling performance during both scenarios.

Server Rack Rear Door Heat Exchanger and the New ASHRAE Recommended Environmental Guidelines

2009 ◽  
Author(s):  
Roger Schmidt ◽  
Madhusudan Iyengar
Keyword(s):  
Energy Efficiency ◽  
Heat Exchanger ◽  
Case Studies ◽  
Data Center ◽  
Hot Spots ◽  
Data Centers ◽  
Failure Modes ◽  
Environmental Temperature ◽  
Rear Door ◽  
Environmental Guidelines

The patented [1] rear door heat exchanger mounted to the rear of IT equipment racks was announced in April, 2005 by IBM and has shown improvements in data center energy efficiency and reducing hot spots. It also allows data center operators to more easily implement some of the features of the newly approved ASHRAE data center recommended data center environmental guidelines [2]. This paper will describe several case studies of implementing the rear door heat exchanger in various data center layouts. The implementation of the water cooled rear door in these data centers will show the effects of various failure modes and how the new ASHRAE environmental temperature guidelines are still being met with the failure modes examined.

Experimentally Verified Transient Models of Data Center Crossflow Heat Exchangers

2014 ◽  
Author(s):  
Tianyi Gao ◽  
Bahgat Sammakia ◽  
James Geer ◽  
Milnes David ◽  
Roger Schmidt
Keyword(s):  
Heat Exchanger ◽  
Data Center ◽  
Heat Exchangers ◽  
Distribution Systems ◽  
Data Centers ◽  
Computational Time ◽  
Cooling Systems ◽  
Rear Door ◽  
Compact Models ◽  
Liquid Heat

Heat exchangers are key components that are commonly used in data center cooling systems. Rear door heat exchangers, in-row coolers, overhead coolers and fully contained cabinets are some examples of liquid and hybrid cooling systems used in data centers. A liquid to liquid heat exchanger is one of the main components of the Coolant Distribution Unit (CDU), which supplies chilled water to the heat exchangers mentioned above. Computer Room Air Conditioner (CRAC) units also consist of liquid to air cross flow heat exchangers. Optimizing the energy use and the reliability of IT equipment in data centers requires Computational Fluid Dynamics (CFD) tools that can accurately model data centers for both the steady state and dynamic operations. Typically, data centers operate in dynamic conditions due to workload allocations that change both spatially and temporally. Additional dynamic situations may also arise due to failures in the thermal management and electrical distribution systems. In the computational simulation, individual component models, such as transient heat exchanger models, are therefore needed. It is also important to develop simple, yet accurate, compact models for components, such as heat exchangers, to reduce the computational time without decreasing simulation accuracy. In this study, a method for modeling compact transient heat exchangers using CFD code is presented. The method describes an approach for installing thermal dynamic heat exchanger models in CFD codes. The transient effectiveness concept and model are used in the development of the methodology. Heat exchanger CFD compact models are developed and tested by comparing them with full thermal dynamic models, and also with experimental measurements. The transient responses of the CFD model are presented for step and ramp change in flow rates of the hot and cold fluids, as well as step, ramp, and exponential variation in the inlet temperature. Finally, some practical dynamic scenarios involving IBM buffer liquid to liquid heat exchanger, rear door heat exchanger, and CRAC unit, are parametrically modeled to test the developed methodology. It is shown that the compact heat exchanger model can be used to successfully predict dynamic scenarios in typical data centers.

Net-Zero Water (NZW) Reuse Desiccant Assisted Evaporative Cooling System for Data Centers

2019 ◽  
Author(s):  
David Okposio ◽  
A. G. Agwu Nnanna ◽  
Harvey Abramowitz
Keyword(s):  
Surface Area ◽  
Data Center ◽  
Data Centers ◽  
Waste Heat ◽  
Cooling System ◽  
Evaporative Cooling ◽  
Water Flow Rate ◽  
Peltier Effect ◽  
Water Recovery ◽  
Data Center Cooling

Abstract The cooling effect of evaporative cooling systems is well documented in literature. Evaporative cooling however introduces humidity into the cooled space, which is unsuitable for data centers. Desiccants (liquid, solid or composites) adsorb moisture from the cooled air to control humidity and is regenerated using waste heat from the data center. This work is an experimental and theoretical investigation of the use of desiccant assisted evaporative cooling for data center cooling according to ASHRAE thermal guideline TC 9.9 . The thickness of the cooling pads is varied with specific surface area, velocity of air through the pad measured, the product of the air velocity and surface area yields the volumetric flowrate of the air, the water flow rate varied as well. The configuration is such that the rotary desiccant wheel (impregnated with silica gel) comes after the evaporative cooler. A novel water recovery system using the Peltier effect is proposed to recover moisture from the return air stream thereby optimizing the water consumption of evaporative cooling technology and providing suitable air quality for data center cooling.

scholarly journals Power Usage Efficiency (PUE) Optimization with Counterpointing Machine Learning Techniques for Data Center Temperatures

2021 ◽  
Vol 6 (6) ◽  
pp. 1594-1611
Author(s):  
Rajendra Kumar ◽  
Sunil Kumar Khatri ◽  
Mario José Diván
Keyword(s):  
Machine Learning ◽  
Data Center ◽  
Data Centers ◽  
Optimization Technique ◽  
Electrical Power ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Optimization Approach ◽  
Information And Communication ◽  
Usage Efficiency

The rapid increase in the IT infrastructure has led to demands in more Data Center Space & Power to fulfil the Information and Communication Technology (ICT) services hosting requirements. Due to this, more electrical power is being consumed in Data Centers therefore Data Center power & cooling management has become quite an important and challenging task. Direct impacting aspects affecting the power energy of data centers are power and commensurate cooling losses. It is difficult to optimise the Power Usage Efficiency (PUE) of the Data Center using conventional methods which essentially need knowledge of each Data Center facility and specific equipment and its working. Hence, a novel optimization approach is necessary to optimise the power and cooling in the data center. This research work is performed by varying the temperature in the data center through a machine learning-based linear regression optimization technique. From the research, the ideal temperature is identified with high accuracy based on the prediction technique evolved out of the available data. With the proposed model, the PUE of the data center can be easily analysed and predicted based on temperature changes maintained in the Data Center. As the temperature is raised from 19.73 oC to 21.17 oC, then the cooling load is decreased in the range 607 KW to 414 KW. From the result, maintaining the temperature at the optimum value significantly improves the Data Center PUE and same time saves power within the permissible limits.

scholarly journals Too Hot for Comfort

2006 ◽  
Vol 128 (12) ◽  
pp. 32-34
Author(s):  
Alan S. Brown
Keyword(s):  
Power Management ◽  
Data Center ◽  
Data Centers ◽  
Cooling Capacity ◽  
Power Supplies ◽  
Heat Output ◽  
Air Conditioner ◽  
The Past ◽  
Heated Air ◽  
It Managers

Electronics have grown much hotter over the past decade, making cooling a top priority in data centers. APC, better known for backup power supplies, supplies cold air from rack-size towers mounted along each row. It then monitors server temperatures, adjusting each individual air conditioner tower to achieve optimal cooling. Such localized cooling is efficient, such that users can boost server rack power to 18 kW—nearly nine times the average found by Uptime. IBM believes even the largest, most sophisticated data center managers need help with cooling. Like APC, it encloses its racks with a roof, but unlike APC it uses a cold rather than hot center aisle and exhausts the heated air into the data center. IBM also removes heat with a water-cooled heat exchanger attached to the back of a rack. In addition, IBM provides power management software that enables IT managers to adjust power and heat output. This ensures that power managers can power down based on workload, or move workload to environments that are not effectively using the power and cooling capacity that they have.

Sign in / Sign up

Export Citation Format

Share Document