Numerical investigation for performance indices and categorical designs of a fan-assisted data center cooling system

The majority of contemporary data centers incorporate raised hot/cold-aisle designs that provide advanced cooling systems. However, while these solutions have proven to be effective, more advanced techniques are required that are energy efficient and capable of controlling the distribution of air to an optimal rate. Through implementing multivariate analysis of variance (MANOVA) and examining how variations in key categorical parameters have an impact on the air distribution and metric thermal features, this paper described the application of fan-assisted floor perforations, both with and without a tile flow-straightening effect, as a method of enhancing the performance of a data center cooling system. In addition, the non-dimensional parametric study employed the Rack-level Supply Heat Index (RLSHI) to evaluate the cooling operation of a data center configuration that included fan-assisted floor perforations. The research concluded that the incorporation of fan-assisted floor cooling systems, via the use of the well-constructed performance indices and categorical designs, could significantly enhance the energy arrangements and efficiencies of data centers.