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[Display omitted] • Full physics-based IT equipment CFD model is built. • Machine learning is integrated with detailed IT equipment CFD model to improve the CFD model accuracy at off-design conditions. • Novel approach for developing compact IT equipment models which can predict its power consumption, airflow demand, and exhaust air temperature is proposed. • The derived IT equipment compact model is validated with the experimental results. In most data centers, performance reliability is often ensured by setting the amount of airflow provided by the cooling units to substantially exceed that which is needed by the IT equipment. This overly conservative strategy requires additional energy expenditure, which inevitably results in a huge amount of energy being wasted by the cooling system. To eliminate adopting such wasteful policies, conducting proper management of airflow, temperature, and energy is critical. To that end, this work proposes a novel approach to developing a compact IT equipment model at off-design conditions. This model is designed to support thermal and energy management functions in data centers. The benefit of this model is that it can accurately predict not only the IT equipment power consumption, but also the amount of flowrate required for the equipment and the air temperature leaving the equipment. While the compact model's power consumption was derived as a function of CPU utilization, its flowrate demand and exhaust temperature were obtained from a dynamic detailed CFD model. Results from the compact model were validated with experiments where the maximum mismatch was found to be 5.7% in the outlet temperature field and 11.4% in flowrate. Compared to a state-of-the-art IT equipment compact model, the developed model was found to reduce the prediction error of the equipment's flowrate and outlet air temperature by up to 5.2% and 9.3 % that of the state-of-the-art IT equipment compact model, respectively. [ABSTRACT FROM AUTHOR]

• Novel approach using porous partitions is introduced for improving overall data center energy efficiency. • The effect of partitions on data center performance metrics is evaluated. • Practical measures for reducing airflow non-uniformity is investigated to reduce energy losses. • Parametric study for investigating the partitions geometry impact on the Supply heat index is conducted. This investigation focuses on improving the airflow distribution in data centers. In many data centers vortices form in the plenum during operations. These vortices cause spatial and temporal non-uniformities and may give rise to hot regions in the data center which in turn impacts performance and reliability of the IT equipment. The current study identifies a novel approach using porous partitions in the plenum and demonstrates a significant generalized approach that is easily adoptable in existing and future data centers. For improving the overall data center energy efficiency and the cooling system effectiveness by eliminating a critical source of inefficiency. The results of quantitative and qualitative analyses of the underfloor plenum pressure field, perforated tiles airflow rate, and air temperature at the rack intake side with and without partitions are presented. Different data center configurations are studied using physics-based experimentally validated Computational Fluid Dynamics (CFD) model. The CFD model results showed that the partitions eliminated the presence of vortices in the underfloor plenum and thus enabled a more uniform pressure distribution and tile airflow delivery. Regarding rack inlet temperature, the results showed that the partitions significantly improved the air temperature at the rack inlet. Finally, a geometrical parametric study is performed. An ideal design is and demonstrated to improve the Supply Heat Index (SHI) by about 10%, while the amount of IT equipment that exceeded the ASHRAE recommended supply air temperature (SAT) was reduced by about 40%, and the floor leakage was cut in half. [ABSTRACT FROM AUTHOR]

• A new indicator, coefficient of power usage effectiveness (COPUE) is proposed. • Energy performances of data centers in various climatic conditions is compared. • A simplified method is developed to present benchmark cooling technology. The power usage effectiveness (PUE) is commonly used as the key performance indicator to evaluate the energy performance of data centers. However, using only PUE cannot enable a fair comparison when data centers are operating in different regions, due to the unneglectable impacts of climatic conditions on the power consumption of cooling systems. To solve this problem, a new indicator, coefficient of PUE (COPUE), is proposed, which is defined as the ratio of the measured PUE of real data centers to the local benchmark PUE. The benchmark PUE is reckoned based on the current most commonly used cooling technology, which consists of water-cooled chillers and water cooling towers. A simplified method for calculating benchmark PUE is also developed. The degree hour of water cooling is introduced to consider the impacts of local climatic conditions. It presents the annual accumulated hours, in which chilled water is needed to satisfy the cooling demand of data centers. Through several case studies, COPUE has been proved to be an effective indicator for comparing the energy performance of data centers. When the same cooling technology is adopted, it can reflect how good the design and operation are; while, when different cooling technologies are adopted, it can be used to demonstrate which one is superior. [ABSTRACT FROM AUTHOR]

• Discuss data centers from perspective of energy prosumers in district energy system. • Review renewable energy usage in data centers and the related advanced controls. • Review data center's waste heat recovery and reuse for district heating. • Review existing green data centers and related economic/environmental analysis. • Identify future research directions for improving data center's overall performance. As large energy prosumers in district energy systems, on the one hand, data centers consume a large amount of electricity to ensure the Information Technologies (IT) facilities, ancillary power supply and cooling systems work properly; on the other hand, data centers produce a large quantity of waste heat due to the high heat dissipation rates of the IT facilities. To date, a systematic review of data centers from the perspective of energy prosumers, which considers both integration of the upstream green energy supply and downstream waste heat reuse, is still lacking. As a result, the potentials for improving data centers' performances are limited due to a lack of global optimization of the upstream renewable energy integration and downstream waste heat utilization. This study is intended to fill in this gap and provides such a review. In this regard, the advancements in different cooling techniques, integration of renewable energy and advanced controls, waste heat utilization and connections for district heating, real projects, performance metrics and economic, energy and environmental analyses are reviewed. Based on the enormous amount of research on data centers in district energy systems, it has been found that: (1) global controls, which can manage the upstream renewable production, data centers' operation and waste heat generation and downstream waste heat utilization are still lacking; (2) regional climate studies represent an effective way to find the optimal integration of renewable energy and waste heat recovery technologies for improving the data centers' energy efficiency; (3) the development of global energy metrics will help to appropriately quantify the data center performances. [ABSTRACT FROM AUTHOR]