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Purpose: The purpose of this paper is to improve the overall heat transfer performance and the temperature uniformity of the heat sink and to explore the effects of the jet Reynolds number and the nanoparticle volume fraction of the nanofluids on the flow and heat transfer performance. Design/methodology/approach: A heat sink with discontinuous arc protrusions in the wall jet region is proposed for confined slot jet impingement. A sloping upper cover plate is added to improve the heat transfer effect in this area. An Al2O3–water nanofluid is selected as the working fluid of the jet for better heat transfer. The Standard k-e turbulence model is used for numerical calculation. The key structural parameters of the heat sink are optimized by the response surface method and a genetic algorithm. The effects of the jet Reynolds number (Re) and the nanofluid concentration (ϕ) on the flow and heat transfer performance of the optimized heat sink are investigated. Findings: The average Nusselt number of the optimal heat sink is 8.2% higher and the friction resistance is 5.9% lower than that of the initial flat plate heat sink when ϕ = 0.02 and Re = 8,000. The discontinuous arc protrusions and the sloping upper cover plate substantially enhance the heat transfer in the later stage of jet development, improving the temperature uniformity of the heat sink. The maximum temperature difference of the optimal heat sink is 28.1% lower than that of the flat plate heat sink at the same nozzle height. As the jet Reynolds number and the nanofluid particle concentration increase, the Nusselt number of the optimized heat sink and the friction coefficients increase, resulting in a decrease in the evaluation coefficient. However, the overall temperature uniformity of the heat sink is improved under all conditions. Originality/value: The novel heat sink structure provides a new way to enhance the heat transfer and temperature uniformity of confined slot jet impingement. The flow and heat transfer performance of the heat sink impinged by confined slot jet of nanofluids are obtained. The combination of response surface method and genetic algorithm can be applied to the multi-objective optimization of heat resistance and flow resistance of heat sink. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to present development of genetic algorithm (GA)-based framework aimed at minimizing data center cooling energy consumption by optimizing the cooling set-points while ensuring that thermal management criteria are satisfied. Design/methodology/approach: Three key components of the developed framework include an artificial neural network-based model for rapid temperature prediction (Athavale et al., 2018a, 2019), a thermodynamic model for cooling energy estimation and GA-based optimization process. The static optimization framework informs the IT load distribution and cooling set-points in the data center room to simultaneously minimize cooling power consumption while maximizing IT load. The dynamic framework aims to minimize cooling power consumption in the data center during operation by determining most energy-efficient set-points for the cooling infrastructure while preventing temperature overshoots. Findings: Results from static optimization framework indicate that among the three levels (room, rack and row) of IT load distribution granularity, Rack-level distribution consumes the least cooling power. A test case of 7.5 h implementing dynamic optimization demonstrated a reduction in cooling energy consumption between 21%–50% depending on current operation of data center. Research limitations/implications: The temperature prediction model used being data-driven, is specific to the lab configuration considered in this study and cannot be directly applied to other scenarios. However, the overall framework can be generalized. Practical implications: The developed framework can be implemented in data centers to optimize operation of cooling infrastructure and reduce energy consumption. Originality/value: This paper presents a holistic framework for improving energy efficiency of data centers which is of critical value given the high (and increasing) energy consumption by these facilities. [ABSTRACT FROM AUTHOR]