Optimization of Cooling Airflow in Data Center by CFD Analysis in a New Energy Efficient Cooling System Using CO2 as Cooling Medium

2020 ◽  
pp. 1175-1185
Author(s):  
Weirong Zhang ◽  
Hongye Li ◽  
Yifei Bai ◽  
Zhaofeng Wang
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Cooling System ◽  
Cfd Analysis ◽  
Cooling Medium ◽  
New Energy

Smart Cooling of Data Centers

2003 ◽  
Author(s):  
Chandrakant D. Patel ◽  
Cullen E. Bash ◽  
Ratnesh Sharma ◽  
Monem Beitelmal ◽  
Rich Friedrich
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Air Conditioning ◽  
Cooling System ◽  
Industry Standard ◽  
Single Location ◽  
Environment Variables ◽  
Variable Capacity ◽  
Commodity Computing ◽  
And Control

The data center of tomorrow is characterized as one containing a dense aggregation of commodity computing, networking and storage hardware mounted in industry standard racks. In fact, the data center is a computer. The walls of the data center are akin to the walls of the chassis in today’s computer system. The new slim rack mounted systems and blade servers enable reduction in the footprint of today’s data center by 66%. While maximizing computing per unit area, this compaction leads to extremely high power density and high cost associated with removal of the dissipated heat. Today’s approach of cooling the entire data center to a constant temperature sampled at a single location, irrespective of the distributed utilization, is too energy inefficient. We propose a smart cooling system that provides localized cooling when and where needed and works in conjunction with a compute workload allocator to distribute compute workloads in the most energy efficient state. This paper shows a vision and construction of this intelligent data center that uses a combination of modeling, metrology and control to provision the air conditioning resources and workload distribution. A variable cooling system comprising variable capacity computer room air conditioning units, variable air moving devices, adjustable vents, etc. are used to dynamically allocate air conditioning resources where and when needed. A distributed metrology layer is used to sense environment variables like temperature and pressure, and power. The data center energy manager redistributes the compute workloads based on the most energy efficient availability of cooling resources and vice versa. The distributed control layer is no longer associated with any single localized temperature measurement but based on parameters calculated from an aggregation of sensors. The compute resources not in use are put on “standby” thereby providing added savings.

Renewable Night Cooled Chill Water Source for Energy Efficient Indoor Radiant Cooling

2016 ◽  
Vol 26 ◽  
pp. 86-98 ◽  
Author(s):  
Muhammad Syukri Imran ◽  
Azhaili Baharun ◽  
Siti Halipah Ibrahim ◽  
Wan Azlan bin Wan Zainal Abidin
Keyword(s):  
Low Income ◽  
Energy Efficient ◽  
Cooling System ◽  
Operating Cost ◽  
Water Source ◽  
Cold Season ◽  
Cooling Medium ◽  
Radiant Cooling ◽  
Hot Season ◽  
System Operating

This study investigates cooling of water at night in Malaysian climate as renewable cooling medium source for radiant cooling purpose. An experiment with a 1.95 m2 steel roof rig structure was constructed and night cooling cycle was conducted during the hot season and cold season of the year. Regression model was developed to predict water temperature after the night cooling process and the corresponding water and roof ratio was established. An annual simulation of a low income home model retrofitted with radiant cooling system charged by night cooled water as cooling medium shows that 99% of the time the thermal condition could meet ISO 7730 category C PMV between-0.7 and + 0.7 . For an outdoor ASHRAE design day condition, the peak indoor operative temperature of 37oC could be lowered to about 30oC with the use of radiant cooling system. The calculated energy saving for the home model was 85% or about 15% of the conventional air system operating cost.

Thermal Control Strategies for Reliable and Energy-Efficient Data Centers

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
Rehan Khalid ◽  
Aaron P. Wemhoff
Keyword(s):  
Data Center ◽  
Energy Efficient ◽  
Cooling System ◽  
System Level ◽  
Component Level ◽  
Simple Method ◽  
Control Schemes ◽  
Data Center Cooling ◽  
Efficient Data ◽  
The Impact

Two self-developed control schemes, ON/OFF and supervisory control and data acquisition (SCADA), were applied on a hybrid evaporative and direct expansion (DX)-based model data center cooling system to assess the impact of controls on reliability and energy efficiency. These control schemes can be applied independently or collectively, thereby saving the energy spent on mechanical refrigeration by using airside economization and/or evaporative cooling. Various combinations of system-level controls and component-level controls are compared to a baseline no-controls case. The results show that reliability is consistently met by employing only sophisticated component-level controls. However, the recommended conditions are met approximately 50% of the simulated time by employing system-level controls only (i.e., SCADA) but with a reduction in data center cooling system power usage effectiveness (PUE) values from 3.76 to 1.42. Moreover, the recommended conditions are met at all averaged times with an even lower cooling system PUE of 1.13 by combining system-level controls only (SCADA and ON/OFF controls). Thus, the study introduces a simple method to compare control schemes for reliable and energy-efficient data center operation. The work also highlights a potential source of capital expenses and operating expenses savings for data center owners by switching from expensive built-in component-based controls to inexpensive, yet effective, system-based controls that can easily be imbedded into existing data center infrastructure systems management.

A New Energy-Efficient Hybrid and Clustering Routing for Wireless Sensor Networks

2017 ◽  
Vol 7 (3) ◽  
pp. 176
Author(s):  
Muneer Bani Yassein ◽  
Yaser Khamayseh ◽  
Ismail Hmeidi ◽  
Ahmed Al-Dubai ◽  
Mohammed Al-Maolegi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
New Energy

DATA CENTER ENVIRONMENT MONITORING AND COOLING SYSTEM BASED ON ARDUINO AND GSM NETWORK

2019 ◽  
Author(s):  
 Najmadin Boskany ◽  
◽  
Shakhawan Wady ◽  
Keyword(s):  
Data Center ◽  
Cooling System ◽  
Environment Monitoring ◽  
Gsm Network

scholarly journals Physical and Data-Driven Models for Edge Data Center Cooling System

2020 ◽  
Author(s):  
Mikko Siltala ◽  
Rickard Brannvall ◽  
Jonas Gustafsson ◽  
Quan Zhou
Keyword(s):  
Data Center ◽  
Cooling System ◽  
Data Driven ◽  
Data Center Cooling
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