Prediction of Cold Aisle End Airflow Boundary Conditions Using Regression Modeling

Abstract

This paper is a continuation of the development of software tools that estimate, in real time or in near-real time, the cooling performance of a cluster of racks bounding a common cold aisle in a raised-floor data center environment. A fundamental assumption within the algorithm of these tools is that the computation of airflow patterns inside the cold aisle can be decoupled from the room environment. The effect of the room environment impacts the solution in the estimation of the cooling performance primarily through the airflow boundary conditions prescribed at the ends of the cold aisle. Consequently, the accuracy of the cooling-performance tool is directly linked to the accuracy of the end airflow prediction for any room environment. The end airflow is a complex function of many factors including the location and airflow rate of each rack, the perforated-tile airflow rate, and room environment conditions. As shown here, the dominant room-environment parameter is the difference between ambient and supply air temperatures. This paper describes the model developed to estimate the end airflow rate. End airflow values are calculated from several hundred computational fluid dynamics (CFD) scenarios covering a broad range of rack airflow (and power) distributions, tile flow rates, and room environments. An end airflow model is developed based on a regression analysis from the CFD data, which facilitates the real-time prediction of the end airflow for any practical cluster layout and room environment. The difference between accepted and predicted end airflow values is typically less than 25% of the accepted value or per-tile airflow rate.