A Statistical Prediction of Cold Aisle End Airflow Boundary Conditions

A statistical prediction of cold aisle end airflow boundary conditions. This paper is a continuation of the development of a software tool (VanGilder and Shrivastava, 2006) that estimates, in 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 the tool 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 such as rack power and rack position, tile flow rate, and room environment conditions including the location of and airflow rate through the return vents. This paper describes the statistical model developed to estimate the end airflow rate. End airflow values are calculated from several hundred CFD scenarios covering a broad range of rack 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 values is typically less than 25% of the accepted value or per-tile airflow rate