A novel integrated fuzzy control system toward automated local airflow management in data centers. (July 2021)

• Record Type: Journal Article
• Authors: Mohsenian, Ghazal; Khalili, Sadegh; Tradat, Mohammad; Manaserh, Yaman; Rangarajan, Srikanth; Desu, Anuroop; Thakur, Dushyant; Nemati, Kourosh; Ghose, Kanad; Sammakia, Bahgat
• Is Part Of: Control engineering practice. Volume 112(2021)
• Publication Date: 2021-07
• Subjects: Airflow management -- Dynamic workload -- Colocation data centers -- CRAHs' control -- Artificial neural network -- Automated data center; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2021.104833
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Today, Data Centers (DCs) are dynamic environments with considerable fluctuations in workload and power dissipation. As a result, active monitoring and dynamic thermal management strategies are essential. In this study, an automated dynamic airflow management technique using air dampers was introduced to manage cold air delivery to individual aisles based on the Information Technology Equipment (ITE) airflow demand. Within this management system, pressure measurements inside the Cold Aisle Containment (CAC) and the plenum were considered controlled variables. First, a feedback fuzzy controller was designed to regulate the airflow delivered to the aisles by adjusting the Open Area Ratio (OAR) of the air dampers. Then, to improve the system's performance and to implement a control system which was adaptable to environmental changes, another fuzzy controller was developed to adjust the blower speed of the cooling units. To estimate the required airflow for provisioning all the ITE in a DC, an Artificial Neural Network (ANN) was developed to characterize the air dampers. This study experimentally examined several opportunities for improving the thermal management and energy performance of DCs with automatic control schemes. Experimental data showed that by using the proposed cooling control strategy, 75% of the cooling units' blower powers and 16% of the chiller's power were saved while maintaining proper thermal management conditions compared to the worst case scenario in which the air dampers were completely open and the cooling units' blower speeds were at maximum. Experimental data from the implementation of the holistic control methodology indicated that there was minimal air leakage from the plenum to the room. Additionally, this approach achieved more efficient airflow delivery from CRAH units to the cold aisles with minimal air loss through leakage. Highlights: An experimental dynamic airflow management strategy is designed using two fuzzy controllers for air dampers' openness and blower speeds of CRAH units. The feedback controllers operate independent of the model, generation, number and workload of installed ITE, and layout of data centers. An artificial neural network is developed to determine dampers' flow rate for different dampers' openness and plenum pressure. The proposed cooling control strategy saved 75% of blowers' power and 16% of chiller's power of the cooling units, in one of the studied scenarios.