<p>The modern world of manufacturing is in the middle of an industrial revolution with the digital and physical worlds integrating through cyber-physical systems.  Through a virtual model that is able to communicate with its physical system known as the Digital Twin, catered decisions can be made based on the current state of the system.  The digital twin presents immense opportunities and challenges as there is a greater need to understand how these new technologies work together. </p> <p><br></p> <p>This thesis is an experimental investigation of the characteristics of the essential components of the Digital Twin.  A Digital Twin Framework is developed to explore the impacts of model accuracy and update frequency on the system’s performance measure. A simple inventory management system and a more complex manufacturing plant is modeled through the framework providing a method to study the interactions of the physical and digital systems with empirical data.</p> <p><br></p> <p><br></p> <p>As the decision policies are affected by the state changes in the system, designing the Digital Twin must account for the direct and indirect impact of its components. </p> <p>Furthermore, we show the importance of communication and information exchange between the Digital Twin and its physical system.  A key characteristic for developing and applying a digital twin is to monitor the update frequency and its impact on performance.  Through the study there are implications of optimal combinations of the digital twin components and how the physical system responds.  There are also limits to how effective the Digital Twin can be in certain instances and is an area of research that needs further investigation.  </p> <p><br></p> <p>The goal of this work is to help practitioners and researchers implement and use the Digital Twin more effectively.  Better understanding the interactions of the model components will help guide designing Digital Twins to be more effective as they become an integral part of the future of manufacturing.</p>

The Digital Twin (DT), a tool dedicated for decision-making and management, has recently started making its way into today’s digitalized world. It is described as a digital representation of a physical object, system, or process, mimicking its physical counterpart using real-time data and monitoring which can be used for decision-making, optimization, and designing (Singh et al., 2021). Not only can DTs be used for a single object, but can also be implemented on whole cities generating valuable insights improving a city’s management, operability, and realization. However, due to the infancy of the concept, further research is required to presuppose accurate city DTs. This can be done by exploring the implementation of DTs in the different sectors of a city (Shahat et al., 2021). Therefore, this report aims to identify areas in which an university in Sweden could benefit from the implementation of a DT, contributing to the knowledge gap regarding DT implementation in the education sector and furthermore the city DT development as a whole. To identify areas in which the university could benefit from a DT, semi-structured interviews were held with chief positions at the university. The aim of the interviews was to identify the university’s main challenges, and then map the challenges to DT solutions in literature and previous research. The identified challenges at the university were Decision-making processes, Recruitment procedures and forecasting, Quality assurance, and Unclear action plan on becoming a sustainable campus. The challenges Decision-making processes and Recruitment procedures and forecasting were mapped to the Digital Twin of an Organization (DTO). This kind of DT can help organizations with, for instance, responding to changes and delivering value for customers (van der Aalst et al., 2021). The last challenge, Unclear action plan on becoming a sustainable campus, was connected to a DT that can help the university in enhancing its energy management.

This paper will give an overview about the technological change in Industrial Hydraulics and the impact of the Digital Twin on the related new engineering processes and methods in order to overcome the challenges coming out of that technology change. Simulation models will more and more become a decisive factor for the engineering process. The Digital Twin will be a window of opportunity for innovations and a technology push for the engineering process and the products in the Industrial Hydraulics.

Many people suffer from diabetes and as a result of the disease, circulatory issues in feet are common. To find such issues at an early stage, Vistafeet were developing a product that measures the temperature in feet. This product needed to be calibrated and for that purpose, this master thesis was evaluating a proposed calibration rig and the possibility of using a digital twin in order to predict the temperature of the rig. The concept of a digital twin includes a physical product, a virtual model of the same product, and information flow between them. By receiving information from the physical product, the digital model should be able to estimate or predict information about the physical product, information that is then used to improve the physical product. In order to be a true digital twin, it should be automatic and in real time. If the data flow is delayed, a better description is digital shadow or digital model, depending on the level of connection. Due to time limitations during this master thesis, the real time connection required for a proper digital twin was not achieved. The scope was then limited from a digital twin to a digital model. The evaluation of the rig was conducted through a case study of the rig including a number of tests, with the purpose of replicating and verifying the result from a previous study by Xiao and Fan [23]. The digital model was made by logging data from the physical product to later use within the simulated environment. First the digital model was compared and adjusted to the information from several thermal points of the physical model. The thermal points were spread out to give as much information as possible the de simulation, but only placed on sides of the rig that would have easy access if the rig were to be used for calibration. Once the digital model was adjusted, the final simulation was made, and temperature data was compared in verification points to see how well the digital model fit the physical model. The verification points were chosen on the calibrating side of the rig and spread out to see if the model managed to predict potentially tricky places. To finalize the investigation of the rig, errors within the model and the rig itself were evaluated. The result showed that it was possible to fulfill the conclusions from a previously made study. The digital model turned out to be accurate and managed to predict the temperature down to ± 0,1 degrees for most verification points. However, one verification point close to a heater element had much less accuracy than the rest. The result was still acceptable, but this indicates that it is not possible to assume that the model can predict entirely correct temperature within the whole rig only because some points are correct. Especially if trying to predict temperature in more difficult places such as close to a heater. The investigation of errors within the digital model showed that the digital model simulated well within the limits as the temperature range and the controller changed. The sensor close to the heater remained in the same error range as in the first simulation. The errors in hardware were evaluated and the variation between sensors was measured to about 0,1 degrees. However, there might also be a slight offset from the true temperature due to errors affecting all thermistors equally. Even though a 0,1 resolution between sensors is quite good, it is insufficient resolution for this test since the errors in the tests were about ± 0,1 degrees. Despite that, the error of the simulation was still in an acceptable range for a digital model setup. For further improvement, a proper real time digital twin could be implemented, but also higher resolution sensors are required. / Många människor lider av diabetes och som ett resultat av denna sjukdom är cirkulations problem i fötter vanligt. För att på ett tidigt stadie upptäcka sådan problem utvecklade Vista feet en produkt som mäter temperaturen i fötterna för att upptäcka cirkulationsproblem. För att produkten ska fungera måste sensorerna i den kalibreras. I detta examensarbete görs en utvärdering av ett förslag på en kalibreringsrigg. Även möjligheten att använda en digital tvilling för att förutspå temperaturen kommer utvärderas. En digital tvilling inkluderar en fysisk produkt, and virtuell modell av samma produkt samt informationsflöde mellen dem. Genom att få information från den fysiska produkten ska den digitala modellen kunna uppskatta eller förutse information om den fysiska produkten, information som används för att förbättra den fysiska produkten. För att vara en äkta digital tvilling ska informationsflödet ske automatiskt och i realtid. Om informationsflödet sker i efterhand, är en bättre beskrivning en digital skugga eller en digital modell, beroende på nivån av automation och fördröjning. På grund av tidsbegränsning i examensarbetet var det inte möjligt att göra en realtidsuppkopplad digital tvilling, och omfattningen av arbetet begränsades till en digital modell. Utvärderingen av riggen gjordes i form av en fallstudie innehållande ett antal test och genom en replikering av en tidigare gjord studie av Xiao och Fan [23]. Den digital modellen gjordes genom att data från den fysiska produkten sparades och sedan användes för att genomföra den digitala simuleringen. Den digital modellen and passades sedan efter informationen i ett antal ”termiska” punkter (thermal points). Dessa punkter var utspridda för att ge så mycket information som möjligt, men enbart placerade på sidor som lätt kan nås om riggen skulle användas för kalibrering. När modellen var anpassad gjordes en sista simulering. Då jämfördes temperaturen i ett antal verifieringspunkter (verification points) för att se om modellen lyckades förutspå temperaturen i dess punkter. Verifieringspunkterna var på kalibreringssidan av riggen, utspridda för att se om modellen klarade att förutspå även potentiellt svåra platser. Slutligen undersöktes även felkällor i modellen och i riggen. Resultatet av studien visade att det var möjligt att uppfylla slutsatserna från den replikerade rapporten. Den digitala modellen blev mycket noggrann och lyckades förutse temperaturen med en noggrannhet på ± 0,1 grad för de flesta punkterna. Det var dock tydligt att det simulerade värdet i en punkt nära ett element hade betydligt sämre noggrannhet än resten av punkterna. Det var fortfarande en godkänd noggrannhet, men den stora variationen från övriga punkter tyder på att bara för att resultatet stämmer bra i vissa punkter så stämmer det nödvändigtvis inte lika bra i alla punkter. Speciellt om det är en punkt som avviker från övriga och därmed är svårare att uppskatta, som i detta fall, nära ett element. Undersökningen av fel i den digitala modellen visade att den stämde fortsatt bra även när temperaturen och kontrollsystemet ändrades, även om noggrannheten på sensorn nära värmaren var fortsatt låg, var det på ungefär samma nivå. I hårdvaran uppmättes skillnaden i temperatur som termistorerna mätte till 0,1 grad. Dock är det sannolikt ett lite större konstant fel då hårdvaru felen ofta påverkar alla termistorer lika. En noggrannhet mellan sensorerna på 0,1 grad är bra, men inte tillräckligt när felet mellan den simulerade modellen och mätta temperaturen är ± 0,1. Trots det är felet inom gränsen för vad som är acceptabelt för en digital modell. För att förbättra arbetet skulle in riktig realtidsuppkopplad tvilling kunna implementeras, men det krävs också att sensorerna har högre upplösning.

To realize key business capabilities and secure long-term growth, Volvo Construction Equipment (Volvo CE) set out to define a vision for digital transformation. The latest trends in AI-powered smart electronics open up endless opportunities to help Volvo CE's operators use Wheel Loaders – Construction machines to increase productivity. To ensure operators are working in a way that delivers optimum fuel efficiency and productivity to achieve optimum results on-site, the company aspires to create visual tools to keep track of operator behavior in the operator environment. Monitor operator behavior with key indicators then visualized to inform how this affects important results for the customers and for Volvo CE. The audience is operators themselves, and internal staff like UX engineers and Product owners. Data-driven concept design (DDCD) is a decision-making approach that heavily relies on collected data and highlights the need to proactively plan and design. It is a popular approach to capturing tacit customer needs and makes a great contribution to data visualization design. Also, an emerging concept like the digital twin provides inspired ideas in data visualization conceptual design. However, little research is on the DDCD for data visualization. Thus, this work aims to explore appropriate data visualization techniques under the DDCD framework. The result is to help Volvo CE, primarily via data visualization, keep track of operator behaviors, and how these affect wheel loader productivity and energy efficiency data on different levels and in a wider context. To carry out, A series of DDCD cases for the improvement of wheel loader operator behaviors are researched and designed, to present data in a clear and concise visual way for both internal audience and operator training. As the result, a prototype containing a series of visualization techniques is proposed for two target groups and corresponding application scenarios including coaching and aid decision-making. Created a series of dashboards with expected functionalities based on understanding the current machine. The prototype for the internal audience has functionality: site and time selection, weekly overview window, phase selection, cycle thread trace, insight window, data presentation, and toolbox. The prototype for operator training has functionality: site and time selection, opponent selection, phase selection, cycle thread trace, external data window, individual comparison section, and insights block. / För att förverkliga viktiga affärsmöjligheter och säkra långsiktig tillväxt har Volvo Construction Equipment (Volvo CE) tagit fram en vision för digital omvandling. De senaste trenderna inom AIdriven smart elektronik öppnar oändliga möjligheter att hjälpa Volvo CE:s operatörer att använda hjullastare - anläggningsmaskiner för att öka produktiviteten. För att säkerställa att förarna arbetar på ett sätt som ger optimal bränsleeffektivitet och produktivitet för att uppnå optimala resultat på plats strävar företaget efter att skapa visuella verktyg för att hålla koll på förarens beteende i förarmiljön. Övervaka operatörens beteende med nyckelindikatorer som sedan visualiseras för att informera om hur detta påverkar viktiga resultat för kunderna och för Volvo CE. Målgruppen är operatörerna själva och intern personal som UX-ingenjörer och produktägare. Datadriven konceptdesign (DDCD) är en beslutsmetod som i hög grad bygger på insamlade data och belyser behovet av proaktiv planering och design. Det är ett populärt tillvägagångssätt för att fånga upp tysta kundbehov och ger ett stort bidrag till design av datavisualisering. Dessutom ger ett framväxande koncept som den digitala tvillingen inspirerande idéer för konceptuell utformning av datavisualisering. Det finns dock lite forskning om DDCD för datavisualisering. Det här arbetet syftar därför till att utforska lämpliga datavisualiseringstekniker inom ramen för DDCD. Resultatet är att hjälpa Volvo CE, främst via datavisualisering, att hålla koll på förarnas beteenden och hur dessa påverkar data om hjullastares produktivitet och energieffektivitet på olika nivåer och i ett större sammanhang. För att genomföra, En serie DDCD-fall för förbättring av beteenden hos hjullastarförare undersöks och utformas, för att presentera data på ett tydligt och kortfattat visuellt sätt för både intern publik och förarutbildning. Som resultat föreslås en prototyp som innehåller en serie visualiseringstekniker för två målgrupper och motsvarande tillämpningsscenarier, inklusive coaching och stöd för beslutsfattande. Skapade en serie instrumentpaneler med förväntade funktioner baserat på förståelse av den nuvarande maskinen. Prototypen för den interna målgruppen har följande funktioner: val av plats och tid, fönster för veckoöversikt, val av fas, spårning av cykeltråd, insiktsfönster, datapresentation och verktygslåda. Prototypen för operatörsutbildning har följande funktioner: val av plats och tid, val av motståndare, val av fas, spårning av cykeltråd, fönster för externa data, avsnitt för individuella jämförelser och block för insikter.

Indoor environment modeling has become a relevant topic in several applications fields including Augmented, Virtual and Mixed Reality. Furthermore, with the Digital Transformation, many industries have moved toward this technology trying to generate detailed models of an environment allowing the viewers to navigate through it or mapping surfaces to insert virtual elements in a real scene. Therefore, this Thesis project has been conducted with the purpose to review well- established deterministic methods for 3D scene reconstruction and researching the state- of- the- art, such as machine learning- based approaches, and a possible implementation on mobile devices. Initially, we focused on the well- established methods such as Structure from Motion (SfM) that use photogrammetry to estimate camera poses and depth using only RGB images. Lastly, the research has been centered on the most innovative methods that make use of machine learning to predict depth maps and camera poses from a video stream. Most of the methods reviewed are completely unsupervised and are based on a combination of two subnetwork, the disparity network (DispNet) for the depth estimation and pose network (PoseNet) for camera pose estimation. Despite the fact that the results in outdoor application show high quality depth map and and reliable odometry, there are still some limitations for the deployment of this technology in indoor environment. Overall, the results are promising. / Modellering av inomhusmiljö har blivit ett relevant ämne inom flera applikationsområden, inklusive Augmented, Virtual och Mixed Reality. Dessutom, med den digitala transformationen, har många branscher gått mot denna teknik som försöker generera detaljerade modeller av en miljö som gör det möjligt för tittarna att navigera genom den eller kartlägga ytor för att infoga virtuella element i en riktig scen. Därför har detta avhandlingsprojekt genomförts med syftet att granska väletablerade deterministiska metoder för 3Dscenrekonstruktion och undersöka det senaste inom teknik, såsom maskininlärningsbaserade metoder och en möjlig implementering på mobil. Inledningsvis fokuserade vi på de väletablerade metoderna som Structure From Motion (SfM) som använder fotogrammetri för att uppskatta kameraställningar och djup med endast RGBbilder. Slutligen har forskningen varit inriktad på de mest innovativa metoderna som använder maskininlärning för att förutsäga djupkartor och kameraposer från en videoström. De flesta av de granskade metoderna är helt utan tillsyn och baseras på en kombination av två undernätverk, skillnadsnätverket (DispNet) för djupuppskattning och posenätverk (PoseNet) för kameraposestimering. Trots att resultaten i utomhusanvändning visar djupkarta av hög kvalitet och tillförlitlig vägmätning, finns det fortfarande vissa begränsningar för användningen av denna teknik i inomhusmiljön, men ändå är resultaten lovande.

Enhancing operational efficiency and competitiveness in modern manufacturing environment requires the incorporation of Industry 4.0 technology. The Digital Twin is one of its enablers, and it is a transformative tool that can be used to optimize systems, processes, and real assets by using virtual models synchronized with real-time data. However, it can be difficult to fully utilize the potential benefits of the massive volumes of data companies generate. By tailoring Digital Twins to the unique data requirements of various user profiles inside companies, this study seeks to overcome this difficulty and enable efficient data access and well-informed decision-making. This study, which was carried out at Robert Bosch España Madrid, aimed to comprehend the unique data requirements of several departments, such as Engineering, Maintenance, and Production. To learn more about department-specific data needs, questionnaires and interviews were used as part of a qualitative research project. The creation of a customized Digital Twin visualizer prototype for the USS6 shopfloor was influenced by these findings. The research findings indicated some differences in the data needs of departments, highlighting the significance of preserving unique profiles in the Digital Twin Visualizer while encouraging cooperation and synergy between departments. Production requires real-time key performance indicator (KPI) monitoring, including cycle time and other production KPIs. The Maintenance department needs to track equipment maintenance events, Mean Time to Repair (MTTR), and Mean Time Between Failures (MTBF). Engineering requires data more related to machines values, status and performance. Most importantly, these findings have significant implications outside of the sensor manufacturing industry; they offer insightful knowledge that is applicable to many different industries. Organizations across diverse industries can enhance their operational performance and decision-making capacities by customizing best practices to suit their unique settings through the application of broader insights gained from these findings. This knowledge-sharing across industries is essential to pushing Industry 4.0 adoption and promoting organizational performance in the digital age. In conclusion, this study enhances knowledge on customized Digital Twin implementations and emphasizes how data-driven insights and well-informed decision-making can be leveraged to create operational excellence across industries. / För att öka den operativa effektiviteten och konkurrenskraften i moderna tillverkningsmiljöer krävs att Industri 4.0-tekniken införlivas. Den Digital Twin (digital tvilling) är ett av verktygen för detta, och det är ett transformativt verktyg som kan användas för att optimera system, processer och egendomar genom att använda virtuella modeller som synkroniseras med realtidsdata. Det kan dock vara svårt att fullt ut utnyttja de potentiella fördelarna med den enorma datavolym som företag genererar. Genom att skräddarsy Digital Twins efter de unika databehov som olika användarprofiler inom företag har, försöker denna studie övervinna denna svårighet och tillhåta effektiv datatillgång och välinformerat beslutsfattande. Målet med den här studien, som genomfördes på Robert Bosch España Madrid, var att förstå de unika databehov på, till exempel, Tekniksavdelningen, Underhållsavdelningen och Tillverksningsavdelningen. För att få veta mer om avdelningsspecifika databehov användes enkäten och intervjuer som delar av ett kvalitativt forskningsprojekt. Dessa resultat påverkade skapandet av en skräddarsydd prototyp av Digital Twin-visualiseraren för USS6:s verkstadsgolv. Forskningsresultaten visade skillnader i avdelningarnas databehov, vilket belyser vikten av att bevara unika profiler i Digital Twin Visualizer och samtidigt som uppmana avdelningarna att samarbeta i synergi mellan avdelningarna. Tillverkningsavdelningen behöver övervakning av KPI:er (Key Performance Indicators, nyckeltal) i realtid, inklusive genomslopptid och andra tillverknings-KPI:er. Underhållsavdelningen behöver overväkning av underhållshändelser för utrustningen, MTTR (eng. Mean Time To Repair, genomsnittlig tid för reparation) och MTBF (eng. Mean Time Between Failures, medeltid mellan fel). Teknikavdelningen behöver data som är relaterade till maskinernas värden, status och prestanda. Dessa resultat har betydande konsekvenser utanför sensortillverkningsindustrin; de erbjuder insiktsfull kunskap som är tillämplig på många olika branscher. Organisationer i olika branscher kan förbättra sina operativa resultat och sin beslutstagande förmåga genom att anpassa bästa praxis till sina unika miljöer med hjälp av de bredare insikter som dessa resultat ger. Detta kunskapsutbyte mellan branscher är avgörande för att driva på införandet av Industri 4.0 och främja organisationers prestanda i den digitala tidsåldern. Till sist ökar denna studie kunskapen om skräddarsydda implementeringar av Digital Twin och betonar hur datadrivna insikter och välinformerat beslutsfattande kan utnyttjas för att skapa operativ excellens i olika branscher.

Purpose – The purpose of this study is to enhance the understanding of how to successfully collaborate on digital platforms in the manufacturing industry by developing a contingency framework. To fulfill this purpose, the following research questions were derived: RQ1: Which challenges arise when collaborating on digital platforms in the manufacturing industry? and RQ2: How can collaboration challenges on digital platforms in the manufacturing industry be managed? Method – The study was conducted as an explorative, inductive single case study of a digital platform. More specifically, the study examined the development process of a digital twin platform created by a large high-technological company and its collaborative actors. In total, 21 interviews were conducted at eight different companies. The respondents all had experience of digital twin platforms, where some were working with digital twins sporadically and others on a daily basis. The data were analyzed through a thematic analysis. Findings – The analysis reveals that actors on digital platforms can face five types of challenges that hinder a successful collaboration: disadvantages of dependency, uncertainty regarding data management, varying customer needs, insufficient work methods, and unsuitable payment models. The analysis also reveals four strategies that can be used to address the challenges: transparency strategy, incentive model strategy, servitization strategy, and control strategy. Moreover, these findings are summarized in a contingency framework that explains which types of challenges that can be addressed with which strategies based on the specific prerequisites of each collaboration. Theoretical and practical implications – The study extends the digital platform literature by providing empirical evidence for several collaboration challenges among the actors on a digital platform, which has previously bee not been studied. Additionally, the study provides evidence of how these challenges can be addressed. Our framework helps manufacturing companies to successfully adopt digital platforms by providing managers with the tools to handle the required collaboration. Limitations and further research – The study is limited by a single case study of a specific digital platform. Thus, to extend the findings, further research that examines other contexts are recommended. Moreover, the establishment of the studied platform is currently in an early phase which limits the study to hypothetical challenges and management methods. To validate the findings, further research that examines a fully developed and implemented platform is recommended.

A digital twin is an enabling technology that facilitates monitoring, understanding, and providing continuous feedback to improve quality of life and well-being. Thus, a digital twin can consider a solution to enhance one's mood to improve the quality of life and emotional well-being. However, there remains a long road ahead until we reach digital twin systems that are capable of empowering development and the deployment of digital twins. This is because there are so many elements and components that can guide the design of a digital twin. This thesis provides a general discussion for the central element of an emotional digital twin, including emotion detection, emotional biofeedback, and emotion-aware recommender systems. In the first part of this thesis, we propose and study the emotion detection models and algorithms. For emotions, which are known to be highly user dependent, improvements to the emotion learning algorithm can significantly boost its predictive power. We aimed to improve the accuracy of the classifier using peripheral physiological signals. Here, we present a hybrid sensor fusion approach based on a stacking model that allows for data from multiple sensors and emotion models to be jointly embedded within a user-independent model. In the second part of this thesis, we propose a real-time mobile biofeedback system that uses wearable sensors to depict five basic emotions and provides the user with emotional feedback. These systems apply the concept of Live Biofeedback through the introduction of an emotion-aware digital twin. An essential element in these systems guides users through an emotion-regulation routine. The proposed systems are aimed at increasing self-awareness by using visual feedback and provide insight into the future design of digital twins. We focus on workplace environments, and the recommendations are based on human emotions and the regulation of emotion in the construct of emotional intelligence. The objective is to suggest coping techniques to a user during an emotional, stressful episode based on her or his preferences, history of what worked well and appropriateness for the context. The developed solution has been studied based on usability studies and extensively compared to related works. The obtained results show the potentials use as an emotional digital twin. In turn, the proposed solution has been providing significant insights that will guide future developments of digital twins using several scenarios and settings.

For each generation of electronic equipment there is a trend towards higher power den-sities. Increased heat generation is an undesired consequence that the thermal design unit in a company must handle. The goal of thermal design engineer/unit is to utilizethe same volume to more efficiently transfer more heat from the equipment. This can bedone by exploring more complex and advanced heat sink geometries, optimizing the finshapes and so on. The new prototypes developed will be tested for their reliability and endurance in special chambers called climate chambers, that simulate desired environ-ments. The measurements by thermal design teams in these kind of climate chambers are mainly of outdoor products, whose cooling is based on natural convection. Forcedcooling using fans is optional for these outdoor products. The climate chambers in general provides temperature measurement as the outputto the analysis, though there are other important parameters that define the operationalfunctionality of an equipment. The ability to visualize the flow characteristics duringthe process of testing is a valuable aid in the design process. A virtual/CFD form of thephysical climate chamber (CC) would empower the design process, while alleviating theusage of the climate chambers for such analyses. CFD offers a wide range of capabilitiesthat lets the user change the boundary conditions with great ease compared to that ofthe experimental setup. The numerical model developed in this thesis project provides results, that help inunderstanding the physics involved in fluid flow inside the physical climate chamber.Turbulence quantification of the flow is the main aim of this thesis project, which wouldbe resourceful in future works. Experiments are conducted inside the climate chamber, in order to aid the construction of numerical model as well as serve as source of vali-dation for the numerical results. Laminar transient case simulations are preferred over use of any turbulence models, to limit any kind of predictions made by these turbulencemodels. Integral length scales and turbulence intensities are compared and reason fordiscrepancies are addressed. The results from the comparisons show that, the numerical model emulates physicsof actual flow inside the climate chamber. However, there are many factors that directlyaffect the results, making it difficult to precisely quantify the error, within the time periodof this thesis project.