Energy Consumption Of Data Centers Research Articles

Environmental impacts of earth observation data in the constellation and cloud computing era

Numbers of Earth Observation (EO) satellites have increased exponentially over the past decade reaching the current population of 1193 (January 2023). Consequently, EO data volumes have mushroomed and data storage and processing have migrated to the cloud. Whilst attention has been given to the launch and in-orbit environmental impacts of satellites, EO data environmental footprints have been overlooked. These issues require urgent attention given data centre water and energy consumption, high carbon emissions for computer component manufacture, and difficulty of recycling computer components. Doing so is essential if the environmental good of EO is to withstand scrutiny. We provide the first assessment of the EO data life-cycle and estimate that the current size of the global EO data collection is ~807 PB, increasing by ~100 PB/year. Storage of this data volume generates annual CO2 equivalent emissions of 4101 t. Major state-funded EO providers use 57 of their own data centres globally, and a further 178 private cloud services, with considerable duplication of datasets across repositories. We explore scenarios for the environmental cost of performing EO functions on the cloud compared to desktop machines. A simple band arithmetic function applied to a Landsat 9 scene using Google Earth Engine (GEE) generated CO2 equivalent (e) emissions of 0.042–0.69 g CO2e (locally) and 0.13–0.45 g CO2e (European data centre; values multiply by nine for Australian data centre). Computation-based emissions scale rapidly for more intense processes and when testing code. When using cloud services such as GEE, users have no choice about the data centre used and we push for EO providers to be more transparent about the location-specific impacts of EO work, and to provide tools for measuring the environmental cost of cloud computation. The EO community as a whole needs to critically consider the broad suite of EO data life-cycle impacts.

Cooling Technologies for Internet Data Center in China: Principle, Energy Efficiency, and Applications

The highlighted energy consumption of Internet data center (IDC) in China has become a pressing issue with the implementation of the Chinese dual carbon strategic goal. This paper provides a comprehensive review of cooling technologies for IDC, including air cooling, free cooling, liquid cooling, thermal energy storage cooling and building envelope. Firstly, the environmental requirements for the computer room and the main energy consumption items for IDC are analyzed. The evaluation indicators and government policies for promoting green IDC are also summarized. Next, the traditional cooling technology is compared to four new cooling technologies to find effective methods to maximize energy efficiency in IDC. The results show that traditional cooling consumes a significant amount of energy and has low energy efficiency. The application of free cooling can greatly improve the energy efficiency of IDC, but its actual implementation is highly dependent on geographical and climatic conditions. Liquid cooling, on the other hand, has higher energy efficiency and lower PUE compared to other cooling technologies, especially for high heat density servers. However, it is not yet mature and its engineering application is not widespread. In addition, thermal energy storage (TES) based cooling offers higher energy efficiency but must be coupled with other cooling technologies. Energy savings can also be achieved through building envelope improvements. Considering the investment and recovery period for IDC, it is essential to seek efficient cooling solutions that are suitable for IDC and take into account factors such as IDC scale, climate conditions, maintenance requirements, etc. This paper serves as a reference for the construction and development of green IDC in China.

Design of an energy efficient dynamic virtual machine consolidation model for smart cities in urban areas

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.

Combination optimization of green energy supply in data center based on simulated annealing particle swarm optimization algorithm

At present, the high energy consumption of data centers based on grid power supply not only brings huge direct cost of electricity, but also indirectly produces a lot of greenhouse gases, which affects the natural environment. Academia and industry are beginning to introduce clean renewable energy sources such as wind and solar power into data centers to reduce operating costs and environmental damage by building new green data centers. To solve this problem, this study considers the use of waste heat for refrigeration while taking natural gas power generation into account, and introduces wind energy as a green energy source. On the premise of considering the response level of data centers, the two resources are combined and deployed to improve resource utilization and reduce energy consumption costs. Aiming at the instability of wind power generation, a particle swarm energy scheduling optimization algorithm based on simulated annealing algorithm was proposed by combining simulated annealing algorithm and particle swarm optimization algorithm. The research shows that, considering the response level of data centers, the use of natural gas and wind energy as the main energy supply can effectively reduce the overall energy consumption of data centers.

Setting Sustainability Goals for Biodiversity Informatics Infrastructure

Biodiversity data are of crucial importance in the implementation of sustainable development strategies (UN Sustainable Development Goals, EU Taxonomy for Sustainable Activities), because they contribute to deepening our understanding of marine and terrestrial ecosystems and our assessment of the impact of human activities on the planet. Yet, biodiversity informatics itself is lacking an overall sustainability plan that addresses “the three E’s of environment, equity, and economics” (Brinkmann 2021) in relation to biodiversity data, their lifecycle, and related technologies. This is especially problematic given that data-driven biodiversity research has had a ten-fold increase (GBIF Secretariat 2022). This implies higher costs for data management, storage and transmission, a larger carbon footprint, and open issues about data access and reuse. SMART (Specific, Measurable, Assignable, Realistic, Time-Related) goals can help to set a sustainability strategy for biodiversity informatics because they bring together the ambitions of an organisation with the action plan necessary to realise these ambitions. When G.T. Doran proposed the use of SMART goals (Doran 1981), his aim was not to create a checklist, but a tool to get results and produce deliverables, which is precisely what is required for implementing an effective sustainability strategy. Three examples are provided here to explain the value of a SMART approach to set sustainability goals in biodiversity informatics. Goal 1, Increase energy efficiency of computing hardware: A large part of the energy consumption in data centres is due to hardware operations. Several strategies can be pursued to increase energy efficiency in this area, such as activity optimisation for Graphic Processing Units (GPUs), improvements in the effectiveness of cooling systems, and replacement of Central Processing Units (CPUs) with more efficient GPUs. This sustainability goal is specific because it clearly identifies what needs to be achieved; it is measurable because pre- and post- energy consumption are known and can be easily compared with the expected improvement; it is assignable because data centre managers can be identified as the agents of change; it is realistic because there are multiple technological solutions to achieve the goal; and time-related because a roadmap for change stretching through months or years can be set up. Achieving this goal is evidently beneficial for both the budget and the carbon footprint of biodiversity informatics, as higher energy efficiency is associated to lower running costs and also contributes to reduce greenhouse gases emissions. Goal 2, Reducing data storage costs per terabyte: Data storage is becoming one of the most significant operating expenses in data-driven organisations due to the considerable increase in the amount of data produced. It is therefore imperative to reduce costs by selecting appropriate storage options (on-premises or cloud services), by adopting compressed formats whenever possible, and by avoiding data redundancy. This goal is SMART because it sets a target (the expected cost reduction can be quantified) and establishes measures (cost per terabyte) to assess its achievement. It can be assigned to data managers, who can choose among different technologies and service providers to lower costs, and can be scheduled over time according to budget needs. The achievement of this goal has clear economic benefits, but also increases the social sustainability of biodiversity informatics, because it makes data-driven research affordable even for organisations with limited financial means. Goal 3, Decreasing API (Application Programming Interface) response time and bandwidth for data transmission: Data sharing is a key activity in biodiversity informatics and APIs have become the standard technology for achieving it. Improving API performance is therefore crucial to enhance the user experience for stakeholders interested in biodiversity data. In particular, API response time and bandwidth required for data transmission control whether users will be able to access immediately and download satisfactorily the data they are interested in. Goal 3 is SMART because API response time and bandwidth are known measures and can be improved using strategies such as caching and concurrency to handle data requests more rapidly and delivering data in compressed formats to reduce bandwidth. Software developers and project managers involved in API design and maintenance are the people in charge of this goal. They decide the best strategy and timeline to pursue changes. Improving API performance increases the social sustainability of biodiversity informatics because it promotes uncomplicated data access for all stakeholders interested in biodiversity data. It also lowers energy consumption for downloading data benefitting the environment.

Study of the operating characteristics of a pump-driven thermosyphon loop with gas–liquid separation

Pump-driven thermosyphon loop (PDTL) is a highly efficient cooling method for air-conditioning systems in data centers. Typically, the refrigerant in gas–liquid two-phase state leads to significant pressure drop in pipes following evaporator, which results in more energy consumption for data centers cooling. This study proposes a novel PDTL with gas–liquid separation (PDTLGLS), aiming to separate the gas–liquid phase refrigerant into liquid phase and gas phase, to reduce pressure drop following evaporator and improve the PDTL performance using a gas–liquid separator. Driven by the pressure difference between the gas–liquid separator and the reservoir, the gas-phase refrigerant is condensed into liquid-phase refrigerant back to the reservoir and the liquid-phase refrigerant returns to a reservoir directly without heat exchanging. This paper firstly conducted a theoretical analysis of PDTLGLS. Then the experiments were conducted using R134a to investigate the influence of refrigerant flow rate and heating power on the performance of PDTLGLS. Furthermore, transient characteristics of the PDTLGLS were experimentally analyzed by varying the heating power. The experimental results show that the PDTLGLS achieves 36.8%-53.3% less heat dissipation temperature difference (from evaporator to condenser) and 28.3%–33.3% higher EER than traditional PDTL, and lower dryness could enlarge the PDTLGLS advantages. Furthermore, both analysis results and experimental results show that the PDTLGLS can adjust to diverse refrigerant mass flow and heating power via the self-regulation of the liquid height in the gas–liquid separator, which implies that the PDTLGLS is an effective way to enhance the traditional PDTL performance and could sustain stable operation across a broad spectrum of operating conditions.

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

Improving energy and water consumption of a data center via air free-cooling economization: The effect weather on its performance

In the past two decades, data centers have significantly grown in size and number around the globe, leading to substantial energy and water stress wherever they operate. Thermal management typically represents half of the total energy consumption in traditional data centers. This work studies the energy and water savings that air-side free-cooling yields under diverse weather conditions. We modeled a free-cooling system and a conventional data center using MATLAB. The hourly weather data was obtained from a publicly available online repository for the Chilean territory. Using air-side free-cooling for data center thermal management is feasible in various weather conditions. Locations with colder temperatures (< 20 °C) and higher humidity (> 60%) are more favorable as they require minimal use of the chiller and/or humidifier. These conditions are present in BSk’(s) Cold Semi-arid climate with dry summer and oceanic influence, as well as Csb’ Mediterranean climate (warm summer) with oceanic influence. However, locations with drier air (< 50%) and warmer temperatures (> 20 °C), such as those found in ET(w) Tundra climate with dry winter, BWk(w) Cold Desert climate with dry winter, or BSk(s) Cold Semi-arid climate with dry summer, are less suitable for air free cooling. Even under suboptimal energy and water savings, the air-side economizer still performed considerably better than a conventional data center cooling method (using CRAH units).

Modeling and Thermal Simulation of 2U-Rack Servers Applying Computational Fluid Dynamics Techniques

The data center industry consumes between 196 and 400 terawatt-hours annually, between 1% and 2% of the world's energy consumption. A high percentage of data center energy consumption is associated with air conditioning and cooling systems. The optimization of the data center cooling process can occur at multiple scales, being one of the most relevant to the generation and heat transfer processes within servers. In this approach, we must study the temperature fields of each server’s components and understand airflow behavior within the server chassis. The present work, in its first stage, performs a thermal and cooling analysis for a two-unit rack server (2U-Rack) using computational fluid dynamics (CFD) techniques via Ansys Fluent code. A geometric server model was developed, considering heat generation in the main electrical components and heat and air mass transfer with the data center. The model was validated by comparing simulation results with the server's sensor values and the results of thermography testing. Following CFD simulation, airflow velocity fields, temperature contours, and sensitivity scenarios were obtained, with the goal of proposing an optimization model that would allow us to improve server heat transfer processes in the following steps.

THE ROLE OF INFORMATION AND COMMUNICATION TECHNOLOGY IN CLIMATE CHANGE MITIGATION AND ADAPTATION.

In an era marked by the escalating implications of climate change, the importance of Information and Communication Technology (ICT) cannot be overemphasised. This paper elucidates the multifaceted roles of ICT in both the mitigation of and adaptation to climate change. On the mitigation front, ICT offers tools for monitoring and modelling greenhouse gas emissions, optimising energy consumption, and facilitating the transition to renewable energy sources. In terms of adaptation, ICT enhances the prediction and management of climate-induced risks, supports real-time communication during extreme weather events, and aids in the planning and implementation of resilient infrastructure. Moreover, ICT bolsters the communication of climate science to diverse audiences, fostering education and advocacy. However, while the potential of ICT is significant, challenges such as e-waste, energy consumption of data centres, and digital divides necessitate holistic strategies to maximise ICT’s positive impact. This paper underscores the need for informed policy-making, integration with traditional ecological knowledge, and interdisciplinary collaboration to leverage ICT effectively in the global climate response.

Energy and carbon-aware initial VM placement in geographically distributed cloud data centers

Energy and carbon efficiency has become one of the most important research topics in the cloud computing research area because of the growing demand for cloud data center infrastructures. In addition to the environmental impact of emitting carbon, local authorities impose a carbon tax based on the amount of emitted carbon by the data centers. Therefore, this paper introduces the energy and carbon-aware initial VM placement (ECAIVMP), one of the most efficient strategies for minimizing the total energy consumption and carbon emission of cloud data centers without sacrificing the quality of service. Our proposed algorithm considers multiple geo-distributed cloud data centers and acknowledges both IT and non-IT power usage during the virtual machine placement process. An extensive simulation in the CloudSim Plus framework is conducted to evaluate the performance of our proposed algorithm using real-world datasets, including workloads, power model benchmarks, and outside temperature. The experimental results demonstrate that our algorithm is able to reduce energy consumption by 17% and carbon emissions by 6% on average compared to other competitor algorithms.

Advanced data analytics modeling for evidence-based data center energy management

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world’s growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC’s two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management.

A resource scheduling method for cloud data centers based on thermal management

With the rapid growth of cloud computing services, the high energy consumption of cloud data centers has become a critical concern of the cloud computing society. While virtual machine (VM) consolidation is often used to reduce energy consumption, excessive VM consolidation may lead to local hot spots and increase the risk of equipment failure. One possible solution to this problem is to utilize thermal-aware scheduling, but existing approaches have trouble realizing the balance between SLA and energy consumption. This paper proposes a novel method to manage cloud data center resources based on thermal management (TM-VMC), which optimizes total energy consumption and proactively prevents hot spots from a global perspective. Its VM consolidation process includes four phases where the VMs scheduler uses an improved ant colony algorithm (UACO) to find appropriate target hosts for VMs based on server temperature and utilization status obtained in real-time. Experimental results show that the TM-VMC approach can proactively avoid data center hot spots and significantly reduce energy consumption while maintaining low Service Level Agreement (SLA) violation rates compared to existing mainstream VM consolidation algorithms with workloads from real-world data centers.

Effects of different water-cooled heat sinks on the cooling system performance in a data center

Data center, as a special building form, has attracted much attention because of its increasing energy consumption. In order to reduce the energy consumption of data center caused by cooling system, research on the water-cooled heat sinks used to cool chips has become increasingly important with the steep increase in the heat flux density of data centers. To explore the effect of different water-cooled heat sinks on cooling system performance, a server water-cooled system for the server cabinet was built, and the chip heat dissipation performance, optimal thermal management, and system energy consumption were simulated by equipping a cooling tower and two types of fin-type water-cooled heat sinks (A-type and B-type). By comparing the effects of A- and B-type heat sinks on the server water-cooled system performance, the optimal water working condition was obtained under different atmospheric conditions by minimizing the system power consumption to guarantee that the chip temperature is no higher than 70 °C. It was found that the optimal cooling water flow rate fluctuates in a large range under different atmospheric conditions. A server water-cooled system equipped with B-type can achieve significant energy savings compared to those from A-type system when the atmospheric relative humidity and temperature are high. Considering a case study from Tianjin, China, it is shown that the month-averaged power usage effectiveness in June is 1.2 for A-type and 1.14 for B-type systems.

An energy-efficient load balance strategy based on virtual machine consolidation in cloud environment

In the cloud computing environment, unbalanced utilization of multi-dimensional resources in physical servers generates resource fragmentation, leading to inefficient resource utilization and energy wastage in data centers. The inefficient utilization of resources makes high energy consumption and low quality of service (QoS) in resource management an urgent problem to be solved. We propose a load balancing strategy based on virtual machine consolidation (LBVMC), which aims to reduce the energy consumption and service level agreement (SLA) violation of data centers by balancing the multi-dimensional resource utilization in physical machines (PMs). First, we present a load state classification algorithm for PMs with load abnormality considering current and future loads to reduce unnecessary virtual machine (VM) migrations caused by occasional load fluctuations. Then, we propose a resource-weight based selection model for migratable VMs, which selects appropriate VMs to be migrated based on multi-dimensional resource utilization, and reduces resource fragmentation caused by load imbalance. Finally, we design a VM placement algorithm based on resource fitness and load correlation to deploy VMs on the optimal destination PMs to assure load balancing of the destination PMs after VM placement. We perform simulated experiments under homogeneous, heterogeneous, and bottleneck resource environments. Experimental results show that LBVMC is superior to other strategies in reducing energy consumption and SLA violations and achieves better overall performance.

Hardware Implementation of Edge Neural Network Computing for Sensor With Memristors Based on Single-Crystalline LiNbO3 Thin Film

With the development of artificial intelligent IoT (AIoT) and dramatic increase of the amount of data, conventional architecture, in which all the data collected by sensors are sent to the data center for processing and computing, suffered from heavy computing load in the data center, latency, and high energy consumption. Edge neural network computing at the sensor terminal is demanding. Based on the advantages of memristor in nature co-location of memory and computing, high computing parallelism, low energy consumption, and miniaturization potential, we proposed to take use of memristors to conduct edge neural network computing at sensor and realize the integration of sensing memory computing. Also, in order to solve the problems caused by resistance states variation and device-to-device variation without transistor connected in series, highly uniform memristors based on single-crystalline LiNbO3 (LN) thin film with two stable resistance states were fabricated and utilized to realize binarized neural networks computing, and the coupling between the pressure sensor output signal with the input of the memristor array has been built. The hardware implementation of memristor-based edge neural network computing on the signals of pressure sensor array has been realized. With the memristor-based edge neural network computing, recognition of three letters (“V,” “Z,” and “T”) wrote on the pressure sensor array has been realized.

LOAD BALANCING IN DATA CENTRE OF CLOUD COMPUTING

The internet is expanding its viewpoint into each conceivable part of the cutting-edge economy. Unshackled from our web programs programs today, the internet is characterizing our way of life, regardless of whether it’s sitting in front of the TV or driving an independent car. Cloud computing which is an on violently expanded our computerized lives, through, there have been critical improvement as far as accessibility fluc-tution, time and quality in administration are concerned, the unbounded development of our computerized way of life requires monstrous measures of power especially for the data centres that fill in as the mind of the advanced economy. All things considered, the move by clients toward cloud, will exceeding any energy productivity increase, which has record for wer 70% os data centre development in 2018. Many research advancements are already made in this domain for minimizing the energy utilization of the computing types of gear included; for effificient power energy consumption, decrease of carbon impression and e-squander. These procedures are supporters of green cloud computing, which are focused on planning and advancing energy-profificient activities to contain inordinate energy utilization in data centers. Cloud computing is a commercial and economic paradigm that has gained traction since 2006 and is presently the most signifificant technology in IT sector. From the notion of cloud computing to its energy effificiency, cloud has been the subject of much discussion. The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030. The data centres require a lot of power to provide services, which increases CO2 emissions. In this survey paper, software based technologies that can be used for building green data centers and include power management at individual software level has been discussed. The paper discusses the energy effificiency in containers and problem-solving approaches used for reducing power consumption in data centers. The cloud computing revolution is redesigning modern networking, and offering promising environmentalprotection prospects as well as economic and technological advantages. These technologies have the potential to improve energy effificiency and to reduce carbon footprints and (e- )waste. These features can transform cloud computing into green cloud computing. In this survey, we review the main achievements of green cloud computing. First,an overview of cloud computing is given.Then, recent studies and developments are summarized, and environmental issues are specififically addressed. Finally, future research directions and open problems regarding green cloud computing are presented. This survey is intended to serve asup-to-date guidance for research with respect to green cloud computing. Key Words: Server, Memory, Data center, Load balancing, Workload categorization, Energy effificiency ,Green information

Refrigeration equipment model construction based in data center cooling station

ABSTRACT The energy consumption of data centers (DCs) is rising year by year, and cooling station accounts for more than 40% of the total DCs’ energy consumption, which has a huge energy-saving potential. Building models for whole DCs’ cooling station can help predict total power to improve the energy efficiency of the system, before this, establishing a single model for each component is a basic work. This paper mainly studies the chiller and cooling tower models and compares the predictive performance of the empirical model, hybrid model, and neural network model of chillers and cooling towers. Giving the model selection scheme of the chiller and cooling tower for the establishment of the whole system of the refrigeration station. For the chiller model, the Yoshida function model in empirical models has the highest accuracy with a mean square error of 0.0592, followed by the neural network model with a mean square error of 0.2, and the hybrid model has a lower accuracy than the former two models. For the cooling tower model, the empirical models and the neural network model have similar accuracy, and both are higher than the hybrid model.

Experimental and numerical investigations of heat transfer characteristics of vertical falling film heat exchanger in data center cabinets

To reduce the energy consumption in data centers, this paper proposes a novel structure that integrates a cabinet with a vertical falling film heat exchanger in data center to perform natural evaporative cooling. First, an experimental setup is built to verify the heat and mass transfer characteristics of a falling film in a vertical tube and to obtain the mass transfer coefficient 0.05. Then, the characteristics of tube length direction and heat and mass transfer are analyzed using compiled user-defined functions for different parameters of moist air and water in the tube. The maximum growth rates of heat transfer coefficient and mass transfer rate are 67.65% and 55.23% at increasing inlet moist air Rema,i, respectively. Finally, the heat transfer characteristics of the falling film heat exchanger with staggered and in-line are compared for inlet air velocities in the range of 1–3.5 m·s−1. The corresponding results demonstrate that the heat flow rate, heat transfer coefficient, and overall heat transfer performance PEC of the staggered are better than those of the in-line as the inlet air velocity of the heat exchanger continues to increase. The maximum PEC of the staggered heat exchangers is 40.07% higher than that of the in-line heat exchangers.

Conjugate Heat Transfer Modeling of a Cold Plate Design for Hybrid-Cooled Data Centers

Liquid-cooled servers can be deployed to reduce the energy consumption and environmental footprint of hybrid-cooled data centers. A computational fluid dynamics (CFD) model can bring extremely useful insights and results for thermal simulations of air- and liquid-cooled servers in a single environment. In this study, a conjugate heat transfer (CHT) numerical model is developed and validated with experimental data to simulate heat transfer from the CPU to the air and cold plate considering the effect of thermal paste. The cooling performance of an in-house developed cold plate design is thoroughly investigated via the validated CHT model. A dataset containing one hundred samples of various flow, thermal and workload conditions was generated using the Latin hypercube sampling (LHS) method, which was further utilized in the series of CHT simulations. Finally, a novel empirical equation is proposed for the prediction of heat transfer from the CPU to the air. The accuracy of the proposed equation is confirmed by comparing estimated and simulated results for a test dataset. A thermal analysis of a rack containing air and liquid-cooled servers is performed using the presented approach. The simulation results reveal that the proposed compact model can be used reliably for the thermal simulation of a hybrid-cooled data center.