Description

The majority of the small- to medium-size data centers employ cooling units that are controlled by the temperatures measured at the returns of the cooling units instead of the server inlets. This indirect and uncoordinated mode of control fails to ensure the efficient cooling of the data center on its own. Existing coordinated controls aim at controlling the supply temperatures of cooling units in accordance with the prevailing conditions. However, such controls find limited scope due to the inability of the return-airtemperature- controlled cooling units to explicitly govern their supply temperature. Moreover, such control systems typically need an extensive network of sensors for temperature monitoring. We here present a coordinated, real-time monitoring and control algorithm that ensures efficient cooling for data centers equipped with return-airtemperature- controlled cooling units. The algorithm predicts the server inlet temperatures using the concept of influence index metrics developed by the authors of this paper, eliminating the need of an extensive sensor network.

The algorithm evaluates the thermal health of the data center through its intelligent temperature analyzer, based on the predicted server inlet temperatures, and responds automatically through coordinated recommendations in terms of setpoints of cooling units. The optimization technique aims to take the data center to a thermally safe, efficient, and stable state in a gradual fashion. The control algorithm was tested in a state-of-the-art, thermally optimized production data center. A systematic increase in the setpoints of the cooling units from 24°C (75°F) to as high as 28°C (82°F) was observed, resulting in the saving of an additional 9% in the cooling energy consumption of an already optimized data center. To the best of the authors’ knowledge, this is the first control algorithm catering to the requirements of controlling a data center equipped with return-air-temperature-controlled cooling units.

Citation: ASHRAE Transactions – Volume 121, Part 1, Chicago, IL

Product Details

Published:

2015

Number of Pages:

14

File Size:

1 file , 2.4 MB

Product Code(s):

D-CH-15-039