In this work a suggested method to reduce the energy consumption of the cooling system in a data center is modelled and evaluated. Introduced is different approaches to distributed airflow control, in which different amounts of airflow can be supplied in different parts of the data center (instead of an even airflow distribution). Two different kinds of distributed airflow control are compared to a traditional approach without airflow control. The difference between the two control approaches being the type of server rack used, either traditional ones or a new kind of rack with vertically placed servers. A model capable of describing the power consumption of the data center cooling system for these different approaches to airflow control was constructed. Based on the model, MATLAB simulations of three different server work load scenarios were then carried out. It was found that introducing distributed airflow control reduced the power consumption for all scenarios and that the control approach with the new kind of rack had the largest reduction. For this case the power consumption of the cooling system could be reduced to 60% - 69% of the initial consumption, depending on the workload scenario. Also examined was the effect on the data center of different parameters and process variables (parameters held fixed with the help of feedback loops), as well as optimal set point values.

A measurement-based reduced-order heat transfer modeling framework is developed to optimize cooling costs of dynamic and virtualized data centers. The reduced-order model is based on a proper orthogonal decomposition-based model order reduction technique. For data center heat transfer modeling, the framework simulates air temperatures and CPU temperatures as a parametric response surface with different cooling infrastructure design variables as the input parameters. The parametric framework enables an efficient design optimization tool and is used to solve several important problems related to energy-efficient thermal design of data centers. The first of these problems is about determining optimal response time during emergencies such as power outages in data centers. To solve this problem, transient air temperatures are modeled with time as a parameter. This parametric prediction framework is useful as a near-real-time thermal prognostic tool. The second problem pertains to reducing temperature monitoring cost in data centers. To solve this problem, transient air temperatures are modeled with spatial location as the parameter. This parametric model improves spatial resolution of measured temperature data and thereby reduces sensor requisition for transient temperature monitoring in data centers. The third problem is related to determining optimal cooling set points in response to dynamically-evolving heat loads in a data center. To solve this problem, transient air temperatures are modeled with heat load and time as the parameters. This modeling framework is particularly suitable for life-cycle design of data center cooling infrastructure. The last problem is related to determining optimal cooling set points in response to dynamically-evolving computing workload in a virtualized data center. To solve this problem, transient CPU temperatures under a given computing load profile are modeled with cooling resource set-points as the parameters.

Information and communications technology (ICT) is an important part in today’s society and around 60% of the world's population are connected to the internet. Processing and storing ICT data corresponds to approximately 1% of the global electricity demand. Locations that store ICT data produce a lot of heat that needs to be cooled, and the cooling systems stand for up to 40% of the total energy used in ICT-centre locations. Investigating the efficiency of the cooling in ICT-centres is important to make the whole ICT-centre more energy efficient, and possibly saving operational costs. Unwanted operational behaviour in the cooling system can be analysed by using unsupervised machine learning and clustering of data. The purpose of this thesis is to characterise cooling patterns, using K-means clustering, in two water-cooled ICT rooms. The rooms are located at Ericsson’s facilities in Linköping Sweden. This will be fulfilled answering the research questions: RQ1. What is the cooling power per m2 delivered by the cooling equipment in the two different ICT rooms at Ericsson?  RQ2. What operational patterns can be found using a suitable clustering algorithm to process and compare data for LCP at two ICT-rooms?   RQ3. Based on information from RQ1 and patterns from RQ2 what undesired operational behaviours can be identified for the cooling system? The K-means clustering is applied to time series data collected during the year of 2022 which include temperatures of water and air; electric power and cooling power; as well as waterflow in the system. The two rooms use Liquid Cooling Packages (LCP)s, also known as in-row cooling units, and room 1 (R1) also include computer room air handlers (CRAHs). K-means clusters each observation into a group that share characteristics and represent different operating scenarios. The elbow-method is used to determine the number of clusters, it created four clusters for R1 and three clusters for room 2 (R2).  Results show that the operational patterns differ between R1 and R2. The cooling power produced per m2 is 1.36 kW/m2 for R1 and 2.14 kW/m2 for R2. Cooling power per m3 is 0.39 kW/m3 for R1 and 0.61 kW/m3 for R2. Undesirable operational behaviours were identified through clustering and visual representation of the data. Some LCPs operate very differently even when sharing the same hot aisle. There are disturbances such as air flow and setpoints that create these differences, which results in that some LCPs operate with high cooling power and others that operate with low cooling power. The cluster with the highest cooling power is cluster 4 and 3 for R1 and R2 respectively. Cluster 2 has the lowest cooling power in R1 and R2. For LCPs operating in cluster 2 where waterflow mostly at 0 l/min and therefore where not contributing to the cooling of the rooms. Lastly, the supplied electrical power and produced cooling power match in R1 but do not in R2. Implying that heat leave the rooms by other means than via the cooling system or faulty measurements. There is a possibility to investigate this further. Water in R1 and R2 is found to, at occasions, exit the room with temperature below the ambient room temperature. It is also concluded that the method functions to identify unwanted operational behaviours, knowledge that can be used to improve ICT operations.  To summarize, undesired operational behaviours can be identified using the unsupervised machine learning technique K-means clustering.

<p>Evaporative cooling is a highly energy efficient alternative to conventional vapor compression cooling system. The sensible cooling effect of evaporative cooling systems is well documented in the literature. Direct evaporative cooling however increases the relative humidity of the air as it cools it. This has made it unsuitable for data centers and other applications where humidity control is important. Desiccant-based dehumidifiers (liquid, solid or composites) absorb moisture from the cooled air to control humidity and is regenerated using waste heat from the data center. This work is an experimental and theoretical investigation of the use of desiccant assisted evaporative cooling for data center cooling according to ASHRAE thermal guidelines, TC 9.9. The thickness (depth) of the cooling pad was varied to study its effect on sensible heat loss and latent heat gain. The velocity of air through the pad was measured to determine its effect on sensible cooling. The flow rate of water over the pad was also varied to find the optimal flow for rate for dry bulb depression. The configuration was such that the rotary desiccant wheel (impregnated with silica gel) comes after the direct evaporative cooler. The rotary desiccant wheel was split in a 1:1 ratio for cooling and reactivation at lower temperatures. The dehumidification effectiveness of a fixed bed desiccant dehumidifier was compared with that of a rotary desiccant wheel and a thermoelectric dehumidifier. A novel condensate recovery system using the Peltier effect was proposed to recover moisture from the return air stream, (by cooling the return air stream below its dew point temperature) thereby optimizing the water consumption of evaporative cooling technology and providing suitable air quality for data center cooling. The moisture recovery unit was found to reduce the mass of water lost through evaporation by an average of fifty percent irrespective of the pad depth.</p> <p> </p>

Given the coinciding growth-trend in the production of consumer electronics and generation of data, the increase in server halls and data centers, as a means for hosting storage capacity for the generated data, has been prominent over the last decades. The establishment of data centers in already existing infrastructure can entail major changes in terms of energy system design. The activity of data processing and storage is power intensive and as the centers demonstrate substantial heat generation, one of the most important fractions of the energy use comes from the need to provide cooling. The study is a techno-economic analysis purposed for determining the feasibility of different cooling systems for a data center in Sweden. The investigated building currently hosts an industrial printing press hall in which paper printing has been conducted for the several decades. This press hall is subject to a refurbishment process to eventually be converted into a data center. In order to achieve the objectives, a data center building model is developed, designated for the estimation of the internal heat generation and demand for cooling. The design and energy requirements of a number of cooling solutions are then investigated and evaluated using a number of performance metrics: Power Usage Effectiveness (PUE), Capital Expenditure (CapEx), Operational Expenditure (OpEx) and Life Cycle Cost (LCC). More specifically the systems incorporate technologies for utilizing air-based free cooling, ground-source free cooling through borehole ground source heat exchangers (GHEs), mechanical cooling through compressor-driven machines as well as District Cooling (DC). The results of the study show that free cooling is a viable solution for covering the vast majority of the yearly cooling requirements, during sufficiently low outdoor temperatures. Free cooling, provided through borehole GHE’s, is feasible as a partial solution from a technical point of view, to provide cooling capacity during warmer periods. However, it can not alone act to provide a major part of the relatively high and constant cooling capacity requirements throughout the year. All of the investigated scenarios display a similar energy performance in terms of total PUE, at values well below the national average of 1.37. It is also seen, that the scenario that displays the lowest LCC includes a combination of free cooling and compressor-driven cooling. This holds for the studied sensitivity cases. It is found that a combined system incorporating borehole GHE’s and compressor cooling machines perform the best in terms of a low PUE. However, the relative difference in energy performance turns out to be lesser than the relative difference in LCC, when substituting the borehole GHE’s for additional cooling machine capacity. / I takt med digitaliseringen och en ökad global användningen- och produktionen av hemelektronik, vilket föranlett en ökad generering av data, har antalet datahallar blivit allt fler de senaste decennierna. Datahallens syfte är att hantera och bereda lagringskapacitet för den data som genereras vilket involverar en rad energikrävande processer. Upprättandet av datahallar i redan befintlig infrastruktur kan medföra förändringar när det kommer till utformningen av byggnadens energisystem. Att bedriva datalagring och informationsbehandling kräver påtagliga mängder elektricitet vilket medför stor intern värmealstring och därtill behov av aktiv kylning.  Denna studie, som valt att benämnas som en tekno-ekonomisk fallstudie, undersöker lämpligheten i implementeringen av olika kylsystem för ett byggnadskomplex i Stockholm. I byggnadens lokaler återfinns idag en industrihall där det sedan flera decennier bedrivits tryckeriverksamhet. Industrihallen är föremål för en konverteringsprocess för att på sikt bli en datahall. Studien är centrerad kring denna konverteringsprocess. För att utvärdera kylbehoven för den framtida datahallen har en modell utvecklats som uppskattar interna värmelaster samt reglerar inomhusklimatet efter rådande krav på inomhuskomfort. Därefter studeras utformning och energibehov för flera olika typer av kylsystemlösningar där en utvärdering av dessa system görs utifrån indikatorerna Power Usage Effectiveness (PUE), Capital Expenditure (CapEx),Operational Expenditure (OpEx) and Life Cycle Cost (LCC). Mer konkret undersöks kombinerade kylsystem som utnyttjar luftburen frikyla, geotermisk frikyla via bergvärmeväxlare (GHEs), mekanisk kyla via kompressordriven kylmaskin samt regional fjärrkyla. Resultaten från studien visar att frikyla från kylmedelskylare är en lämplig lösning för att täcka majoriteten av datahallens kylbehov över ett år, med undantag för årets varmare perioder. Geotermisk frikyla via borrhål är möjlig som partiell lösning ur ett tekniskt perspektiv, men kan inte enskild leverera en majoritet av effekt- eller energibehovet av kyla. Resultatet visar också att alla undersökta scenarier uppvisar en liknande energiprestanda i termer av total PUE, med värden som underskrider det nationella genomsnittet 1,37. Lägst LCC påvisades för ett system som kombinerar luftburen frikyla via kylmedleskylare och mekanisk kyla via kompressordrivna kylmaskiner. Denna låga LCC är signifikant vilket påvisas i utförd känslighetsanalys. Slutligen konstateras att ett system innefattande luftburen och geotermisk frikyla i kombination med kompressordrivna kylmaskiner resulterar i lägst PUE bland de undersökta scenarierna. Den relativa skillnaden i energiprestanda visar sig vara mindre än den relativa skillnaden i LCC, när geotermisk frikyla ersätts med ytterligare kapacitet från kylmaskiner.