Modeling and Control of Hybrid Si-Based Micro-Fluid Cooling System for Data Center Application

2018 ◽  
Author(s):  
Haoran Chen ◽  
Yong Han ◽  
Gongyue Tang ◽  
Xiaowu Zhang
Keyword(s):  
Data Center ◽  
Cooling System ◽  
Modeling And Control ◽  
Fluid Cooling ◽  
And Control

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.

scholarly journals A Cyber–Physical Systems Approach to Data Center Modeling and Control for Energy Efficiency

2012 ◽  
Vol 100 (1) ◽  
pp. 254-268 ◽  
Author(s):  
Luca Parolini ◽  
Bruno Sinopoli ◽  
Bruce H. Krogh ◽  
Zhikui Wang
Keyword(s):  
Energy Efficiency ◽  
Data Center ◽  
Systems Approach ◽  
Cyber Physical Systems ◽  
Modeling And Control ◽  
Physical Systems ◽  
And Control

Operational Benefits of Real-Time Monitoring and Visualization in Data Centers

2009 ◽  
Author(s):  
Tahir Cader ◽  
Ratnesh Sharma ◽  
Cullen Bash ◽  
Les Fox ◽  
Vaibhav Bhatia ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Data Center ◽  
Data Centers ◽  
Cooling System ◽  
Global Network ◽  
Real Time Monitoring ◽  
Us Epa ◽  
And Control ◽  
Control Management

The 2007 US EPA report to Congress (US EPA, 2007) on the state of energy consumption in data centers brought to light the true energy inefficiencies built into today’s data centers. Marquez et al. (2008) conducted an initial analysis on the productivity of a Pacific Northwest National Lab computer using The Green Grid’s Data Center Energy Productivity metric (The Green Grid, 2008). Their study highlights how the Top500 ranking of computers disguises the serious energy inefficiency of today’s High Performance Computing data centers. In the rapidly expanding Cloud Computing space, the race will be won by the providers that deliver the lowest cost of computing — such cost is heavily influenced by the operational costs incurred by data centers. As a means to address the urgent need to lower the cost of computing, solution providers have been intensely focusing on real-time monitoring, visualization, and control/management of data centers. The monitoring aspect involves the widespread use of networks of sensors that are used to monitor key data center environmental variables such as temperature, relative humidity, air flow rate, pressure, and energy consumption. Such data is then used to visualize and analyze data center problem areas (e.g., hotspots), which is then followed by control/management actions designed to alleviate such problem areas. The authors have been researching the operational benefits of a network of sensors tied in to a software package that uses the data to visualize, analyze, and control/manage the data center cooling system and IT Equipment for maximum operational efficiency. The research is being conducted in a corporate production data center that is networked in to the authors’ company’s global network of data centers. Results will be presented that highlight the operational benefits that are realizable through real-time monitoring and visualization.

Modeling and control of a single-phase marine cooling system

2013 ◽  
Vol 21 (12) ◽  
pp. 1726-1734 ◽  
Author(s):  
Michael Hansen ◽  
Jakob Stoustrup ◽  
Jan Dimon Bendtsen
Keyword(s):  
Single Phase ◽  
Cooling System ◽  
Modeling And Control ◽  
And Control

Neural Network-Based Prediction and Control of Air Flow in a Data Center

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Flavio de Lorenzi ◽  
Christof Vömel
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Real Time ◽  
Data Center ◽  
Cooling System ◽  
Air Flow ◽  
Data Center Cooling ◽  
Prediction And Control ◽  
Reduce Energy Consumption ◽  
And Control

As modern data centers continue to grow in power, size, and numbers, there is an urgent need to reduce energy consumption by optimized cooling strategies. In this paper, we present a neural network-based prediction of air flow in a data center that is cooled through perforated floor tiles. With a significantly smaller execution time than computational fluid dynamics, it predicts in real-time server inlet temperatures and can detect whether prevalent air flow cools the servers sufficiently to guarantee safe operation. Combined with a cooling system model, we obtain a temperature and air flow control algorithm that is fast and accurate enough to find an optimal operating point of the data center cooling system in real-time. We also demonstrate the performance of our algorithm on a reference data center and show that energy consumption can be reduced by up to 30%.

Modeling and control of an Internal Bubble Cooling system

2012 ◽  
Author(s):  
D. Belloli ◽  
S. M. Savaresi ◽  
A. Cologni ◽  
F. Previdi ◽  
D. Cazzola
Keyword(s):  
Cooling System ◽  
Modeling And Control ◽  
And Control

Modeling and control of a hydrogen-based green data center

2021 ◽  
Vol 199 ◽  
pp. 107374
Author(s):  
Nouhaila Lazaar ◽  
Mahmoud Barakat ◽  
Morad Hafiane ◽  
Jalal Sabor ◽  
Hamid Gualous
Keyword(s):  
Data Center ◽  
Modeling And Control ◽  
Green Data Center ◽  
And Control

Modeling and Control for Cooling Management of Data Centers With Hot Aisle Containment

2011 ◽  
Author(s):  
Rongliang Zhou ◽  
Zhikui Wang ◽  
Cullen E. Bash ◽  
Alan McReynolds
Keyword(s):  
Data Center ◽  
Data Centers ◽  
Cooling Power ◽  
Cooling Efficiency ◽  
Modeling And Control ◽  
Control Approach ◽  
Model Predictive Controllers ◽  
And Control ◽  
Predictive Controllers ◽  
Decentralized Model

In traditional raised-floor data center design with hot aisle and cold aisle separation, the cooling efficiency suffers from recirculation resulting from the mixing of cool air from the Computer Room Air Conditioning (CRAC) units and the hot exhaust air exiting from the back of the server racks. To minimize recirculation and hence increase cooling efficiency, hot aisle containment has been employed in an increasing number of data centers. Based on the underlying heat transfer principles, we present in this paper a dynamic model for cooling management in both open and contained environment, and propose decentralized model predictive controllers (MPC) for control of the CRAC units. One approach to partition a data center into overlapping CRAC zones of influence is discussed. Within each zone, the CRAC unit blower speed and supply air temperature are adjusted by a MPC controller to regulate the rack inlet temperatures, while minimizing the cooling power consumption. The proposed decentralized cooling control approach is validated in a production data center with hot aisles contained by plastic strips. Experimental results demonstrate both its stability and ability to reject various disturbances.

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