Ranking and Optimization of CAC and HAC Leakage Using Pressure Controlled Models

Abstract

In cold aisle containment (CAC) the supply of cold air is separated within the contained volume. The hot air exhaust leaves the IT and increases the room’s temperature before returning to the cooling unit. On the other hand, hot aisle containment (HAC) generates a cooler environment in the data center room as a whole by segregating hot air within the containment. Hot air is routed back to the cooling unit return by a drop ceiling or a chimney. Each system has different characteristics and airflow paths. For instance, leakage introduces different effects for CACs and HACs since the hot and cold aisles are switched. This article utilizes data center measurements and containment characterization carried out circa April 2015 in the ES2 Data Center lab at Binghamton University. Details on the containment model include leakages at below racks, above racks, below CAC doors, between doors, and above doors. The model deploys the experimentally obtained flow curves approach for flow-pressure correlation. Data center operators rely on the pressure differential to measure how much the IT is provided. Hence, in this study the level of provisioning was expressed in terms of pressure differentials between the hot and cold aisles. In this manner the model reflected real-life DC thermal management practices. This was done by integrating a pressure differential based controller to the cooling unit model. Leakages in each system are quantified and ranked based on a proposed LIF (Leakage Impact Factor) metric. The LIF describes the transport contribution each leakage location has. This metric can be used by containment designers and data center operators to prioritize their sealing efforts or consider deploying the containment solution differently. Finally, a systematic approach is shown in which containment models can be used to optimize operations at the real-life site.