Multi-objective optimization of supply air jet enhancing airflow uniformity in data center with Taguchi-based grey relational analysis

Abstract

In most data centers (DCs), hot spots reduce the reliability, durability, and efficiency of server cooling. The hot spots are mainly attributed to the uneven airflow distribution and the resulting mixing of cold and hot air. To evenly distribute the airflow, the relay jet fan, an active local adjustment technology, is introduced into the under-floor air distribution in DCs. The jet fans are set in front of the closed cold aisle where the hot spots most often appear. The nozzle height, nozzle angle, horizontal position and attachment distance greatly influence the performance of the new system. To comprehensively evaluate the multiple levels of these affecting parameters, a large number of cases that need testing make it impossible to conduct research. An approach based on the Taguchi method with grey relational analysis optimizes the system and the L16 (44) orthogonal array is employed to design the experiments. The Taguchi method combined with computational fluid dynamics determines the optimal combinations of the Supply Heat Index (SHI), Return Temperature Index (RTI), β, and Index of Mixing (IOM). The significance of the affecting parameters is revealed and ranked, and through the main effect analysis and the analysis of variance, the nozzle angle is chosen as the most important among the four affecting parameters. After that, the Taguchi-based grey relational analysis transforms the multi-objective into a single-objective to determine the optimal combination of design variables. Finally, a confirmatory test was conducted, and the optimal combination reduces the server temperature by up to 7.2 °C.