Analysis of airflow imbalances in an open compute high density storage data center. (5th September 2016)

• Record Type: Journal Article
• Authors: Alissa, Husam A.; Nemati, Kourosh; Puvvadi, Udaya L.N.; Sammakia, Bahgat G.; Schneebeli, Ken; Seymour, Mark; Gregory, Tom
• Is Part Of: Applied thermal engineering. Volume 108(2016:Sep.)
• Page Start: 937
• Page End: 950
• Publication Date: 2016-09-05
• Subjects: Storage -- High density -- IT -- Containment -- HDD -- Open compute -- Reliability -- Flow curves -- Utilization; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2016.07.186
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Experimental and numerical analysis of the open compute storage data center. Reliability and utilization impacts on IT during airflow imbalance in containment. Server and rack levels airflow prediction models. Airflow, pressure, and temperature flow curves to model IT fan control system. Abstract: This article reports experimental and numerical testing performed to characterize the operation and reliability of the open compute (OC) storage system in contained environment from server to aisle levels. The study is comprised of three parts. The first part is an experimental analysis of the high density (HD) 3D array storage unit thermal and utilization responses during airflow imbalances. This is done with the stress test proposed for IT in containment to mimic possible mismatch and cascade failure scenarios. It is found that downstream HDDs are most prone to overheating and loss in utilization during an airflow imbalance. This was proven to undermine the storage capacity of the hard disk drives. An IT level airflow prediction model is discussed for the storage unit and validated for different fan speeds. In the second part, a computational fluid dynamics model is created for a high density open rack based on the active flow curve method. Here, the measured airflow response curves for the open compute IT (storage and compute servers) are used to build compact models and run rack level testing for IT air systems sensitivity and create a rack level AFC (active flow curve) airflow demand prediction model. Finally, the experimental characterization data is used to build an aisle level model (POD) that incorporates IT fan control systems (FCS). This modeling approach yields shorter uptime during chiller failure due to increased recirculation induced by increased IT airflow demand during cases such as chiller failure or high economizer temperatures.