| Issue |
E3S Web Conf.
Volume 165, 2020
2020 2nd International Conference on Civil Architecture and Energy Science (CAES 2020)
|
|
|---|---|---|
| Article Number | 06004 | |
| Number of page(s) | 5 | |
| Section | Electrical and Power Engineering | |
| DOI | https://doi.org/10.1051/e3sconf/202016506004 | |
| Published online | 01 May 2020 | |
Dynamic Optimization of Airflow Organization in Green Data Center Based on MATLAB
1 School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou, Henan Province, 450001, China
2 School of Chemical Engineering, Zhengzhou University, Zhengzhou, Henan Province, 450001, China
* Corresponding author’s e-mail: qhwpudding@163.com
In order to make data centers meet the requirements of green data center standards, this paper starts with air conditioning systems with high energy consumption, and dynamically optimizes airflow organization. It uses limited space convection heat transfer and air kinematics to dissipate multiple processes in the data center. The process is solved by MATLAB optimization and reverse demonstration. In this paper, the influence of cold air flow on cabinet cooling is divided into four key processes: mixing loss during descent, convective heat transfer in descending process, the convection heat exchange during the steady flow process, and mixed airflow discharge. By analyzing the temperature change of cold airflow in each process, and seeking the relationship between velocity v and air resistance F in viscous gas flow, the temperature-velocity relationship is obtained. Then the relationship between cold loss, PUE value and indoor height of the data center was analyzed to obtain the ceiling height corresponding to the standard cabinet height. The best combination is the ceiling height of 3.5m and the cabinet height of 2m and the corresponding PUE value is about 1.87, which meets the requirements of green data centers and is highly economical.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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