<p>A procedure for calibrating a discrete element (DE) computational soil model for various moisture contents using a conventional Asperity-Spring friction modeling technique is presented in this thesis. The procedure is based on the outcomes of two physical soil experiments:</p> <p>(1) Compression and (2) unconfined shear strength at various levels of normal stress and normal pre-stress. The Compression test is used to calibrate the DE soil plastic strain and elastic strain as a function of Compressive stress. To calibrate the DE inter-particle friction coefficient and adhesion stress as a function of soil plastic strain, the unconfined shear test is used. This thesis describes the experimental test devices and test procedures used to perform the physical terramechanical experiments. The calibration procedure for the DE soil model is demonstrated in this thesis using two types of soil: sand-silt (2NS Sand) and silt-clay(Fine Grain Soil) over 5 different moisture contents: 0%, 4%, 8%, 12%, and 16%. The DE based models response are then validated by comparing them to experimental pressure-sinkage results for circular disks and cones for those two types of soil over 5 different moisture contents. The Mean Absolute  Percentage Error (MAPE) during the compression calibration was 26.9% whereas during the unconfined shear calibration, the MAPE was calculated to be 11.38%. Hence, the overall MAPE was calculated to be 19.34% for the entire calibration phase.</p>

In recent years, digital twins (DT) have gained traction in enhancing operation and maintenance procedures across various industrial sectors, such as manufacturing, energy, and aerospace. Yet their utilization remains more limited in the built environment. The aim of this paper is to investigate the current conceptualizations of DTs within the context of the Swedish built environment, shedding light on the maturity of DTs for built assets and the key value drivers influencing their adoption. The study reveals that DTs for built assets are in early developmental stages, lacking the maturity required to encompass the full spectrum of attributes inherent in a conventional DT. Despite stakeholders labeling these initiatives as DTs, our findings indicate a closer alignment with the concept of BIM-based digital models. This observation resonates with existing literature, highlighting the absence of a universally accepted definition for DTs. Within the Operations and Maintenance (O&amp;M) phase, we have identified the following four primary value drivers shaping the adoption of BIM-based DTs: 1) commercial transparency and trusted transactions, 2) efficiency and cost reduction in routine operations, 3) operational continuity, and 4) added services. These findings contribute to the ongoing discourse surrounding the convergence of BIM and DTs, offering insights into the practical implications and value propositions that drive the integration of these technologies within the built environment. / Under de senaste åren har användningen av digitala tvillingar (DT:ar) blivit allt vanligare för drift- och underhållsförfaranden inom olika industrisektorer, såsom tillverkning, energi och flyg- och rymdindustrin. Användningen är dock fortfarande begränsad inom bygg- och fastighetsbranschen. Syftet med denna studie är att undersöka de nuvarande konceptualiseringarna av DT:ar inom ramen för den svenska fastighetsbranschen, belysa mognaden hos tekniken och undersöka de viktigaste värdedrivande faktorerna som påverkar utvecklingen och användningen i drift- och underhåll av fastigheter. Studien visar att användningen av DT:ar i fastighetsbranschen befinner sig i ett tidigt utvecklingsstadie och att begreppet inom industrin inte helt överensstämmer med visionen kring konceptet. Resultaten stämmer överens med befintlig litteratur, som belyser avsaknaden av en allmänt accepterad definition för DT:ar. I drift- och underhållsfasen har vi också identifierat följande fyra primära värdedrivande faktorer för användingen av BIM-baserade DT:ar: 1) kommersiell transparens och pålitliga transaktioner, 2) kostnadsbesparingar i rutinmässigt drift- och underhållsarbete, 3) kontinuerligt driftsarbete, samt 4) tilläggstjänster. Resultaten från denna studie bidrar till den pågående diskussionen om koncepten BIM och DT, och ger ytterligare insikt i de praktiska värdeskapande processerna och belyser användningsområden av DT:ar i fastighetsbranschen.

<p>  </p> <p>A <em>digital twin</em> (DT), which keeps track of nuclear reactor history to provide real-time predictions, has been recently proposed for nuclear reactor monitoring. A digital twin can be implemented using either a differential equations-based physics model, or a data-driven machine learning model<strong>. </strong>The principal challenge in physics model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, while the main challenge in data-driven DT appears in the extensive training requirements and potential lack of predictive ability. </p> <p>In this thesis, we investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. In this way, PINNs establish theoretical constraints and biases to supplement measurement data and provide solution to several limitations of purely data-driven machine learning (ML) models. We develop a PINN model to solve the point kinetic equations (PKEs), which are time dependent stiff nonlinear ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring the spatial dependence of the neutron flux. PKEs portray the kinetic behavior of the system, and this kind of approach is the basis for most analyses of reactor systems, except in cases where flux shapes are known to vary with time. This system describes the nuclear parameters such as neutron density concentration, the delayed neutron precursor density concentration and reactivity. Both neutron density and delayed neutron precursor density concentrations are the vital parameters for safety and the transient behavior of the reactor power. </p> <p>The PINN model solution of PKEs is developed to monitor a start-up transient of the Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The facility under modeling, PUR-1, is a pool type small research reactor located in West Lafayette Indiana. It is an all-digital light water reactor (LWR) submerged into a deep-water pool and has a power output of 10kW. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. </p> <p>The findings of this thesis research indicate that the PINN model achieved highest performance and lowest errors in data interpolation. In the case of extrapolation data, three different test cases were considered, the first where the extrapolation is performed in a five-seconds interval, the second where the extrapolation is performed in a 10-seconds interval, and the third where the extrapolation is performed in a 15-seconds interval. The extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.</p>

The transportation industry stands for a big chunk of the worlds total carbon emissions. To counter this problem electric vehicles are seen as a good solution. However, these vehicles come at a greater cost and do not offer the same range as their less environmentally friendly counterpart. To lessen costs and development time when optimizing electric vehicles, simulations of the vehicles functionality can be utilized. One way of getting such simulations is to design a digital twin of the physical system. A digital twin is able to mimic the functionality of the physical system and can therefore offer well based indications of how a change in design will change the performance in reality. In this thesis a digital twin of the cooling system of an electric boat is designed with realistic results. Cooling systems in the scope of electric vehicles are of grave importance since the electric driveline becomes hot during use which can hinder performance of the vehicle. This is especially true for the high voltage batteries that tend to have quite a narrow range of temperatures within which performance is optimal. This thesis handles an attempt at optimizing the cooling system, replicated by the digital twin, by the use of a temperature predictive model. Three different machine learning models were tested and the resulting best model achieved a mean absolute error of 2.4 and a mean average percentage error of 5.7. However, the model was unable to foresee sudden temperature spikes and drops. A possible fix, that could not be tested in this thesis, would be to implement further input data such as driver profiles and/or GPS data with speed limits. / Transportindustrin står för en stor del av världens totala koldioxidutsläpp. För att motverka detta problem ses elfordon som en bra lösning. Dessa fordon kommer dock till en högre kostnad och erbjuder inte samma räckvidd som deras mindre miljövänliga motpart. För att minska kostnader och utvecklingstid vid optimering av elfordon kan simuleringar av fordonens funktionalitet användas. Ett sätt att få sådana simuleringar är att designa en digital tvilling av det fysiska systemet. En digital tvilling kan efterlikna det fysiska systemets funktionalitet och kan därför erbjuda välbaserade indikationer på hur en förändring i design kommer att förändra prestandan. I detta examensarbete designas en digital tvilling av kylsystemet i en elbåt med realistiska resultat. Kylsystem i elfordon är av stor betydelse eftersom den elektriska drivlinan blir varm under användning, vilket kan hindra fordonets prestanda. Detta gäller särskilt för högspänningsbatterierna som tenderar att ha ett ganska smalt temperaturintervall för optimal prestanda. Denna avhandling behandlar ett försök att optimera kylsystemet, replikerat av den digitala tvillingen, genom att använda en temperaturförutsende modell. Tre olika maskininlärningsmodeller testades och den resulterande bästa modellen uppnådde ett genomsnittligt absolut fel på 2.4 och ett genomsnittligt procentuellt fel på 5.7. Modellen kunde dock inte förutse plötsliga temperaturspikar och -fall. En möjlig fix, som inte kunde testas i denna avhandling, skulle vara att implementera ytterligare indata såsom förarprofiler och/eller GPS-data med hastighetsbegränsningar.

More dynamic markets, shorter product life cycles and comprehensive variant management are challenges that dominate today's market. These maxims apply to the automotive sector, which is currently highly exposed to trade wars, changing mobility patterns and the emergence of new technologies and competitors. To meet these challenges, this thesis presents the creation of a digital twin of an existing production line of electric motors using discrete event simulation. Based on a detailed literature research, a step-by-step establishment of the simulation model of the production line using the software Plant Simulation is presented and argued. Finally, different experiments are carried out with the created model to show how a production line can be examined and optimized by means ofsimulation using different parameters. Within the scope of the different experiments regarding the number of workpiece carriers, number of operators as well as buffer sizes, the line was examined concerning the increase of the output. Furthermore, the simulation model was used to make decisions for future investments in additional XXX machines. Four different scenarios were examined and optimized. By examining the different parameters, optimization potentials of XXX% in the first scenario and up to XXX% in the fourth scenario were achieved. Finally, it was proven that the developed simulation model can be used as a tool for optimizing an existing production line and can generate useful investment information. Beyond that, the development of the simulation model can be employed to investigate further business questions at hand for the specific production line in question. / Mer dynamiska marknader, kortare produktlivscykler och omfattande varianthantering är utmaningar som dominerar dagens marknad. Dessa maximer gäller bilindustrin, som för närvarande är mycket utsatt för handelskrig, förändrade rörlighetsmönster och framväxten av ny teknik och nya konkurrenter. För att möta dessa utmaningar innebär denna avhandling skapandet av en digital tvilling av en befintlig produktionslinje av elmotorer med diskret händelsesimulering. Baserat på en detaljerad litteraturforskning presenteras och argumenteras en steg-för-steg-etablering av simuleringsmodellen för produktionslinjen med hjälp av programvaran Plant Simulation. Slutligen utförs olika experiment med den skapade modellen för att visa hur en produktionslinje kan undersökas och optimeras med hjälp av simulering med hjälp av olika parametrar. Inom ramen för de olika experimenten när det gäller antalet arbetsstyckesbärare, antalet operatörer samt buffertstorlekar undersöktes linjen om ökningen av produktionen. Dessutom användes simuleringsmodellen för att fatta beslut för framtida investeringar i ytterligare hårnålsmaskiner. Fyra olika scenarier undersöktes och optimerades. Genom att undersöka de olika parametrarna uppnåddes optimeringspotentialer på XXX % i det första scenariot och upp till XXX % i det fjärde scenariot. Slutligen bevisades det att den utvecklade simuleringsmodellen kan användas som ett verktyg för att optimera en befintlig produktionslinje och kan generera användbar investeringsinformation. Utöver detta kan utvecklingen av simuleringsmodellen användas för att undersöka ytterligare affärsfrågor till hands för den specifika produktionslinjen i fråga.

The use of simulation programs in production environments is crucial due to the many advantages they offer, such as facilitating calculations of production capacity, cycle times, and optimal buffer sizes. These tools allow for the simulation of various scenarios and cost-effective testing of different strategies, making them invaluable for companies striving to improve production and performance.  The purpose of this work is to conduct a case study using FACTS Analyzer and then compare the results with another simulation software that supports 3D simulations. By conducting the same case study in both programs, we can draw conclusions about the factors companies should consider when acquiring simulation software. The data will be collected through qualitative methods such as observations and interviews, complemented by quantitative methods through experiments within the case study. The results show that FACTS Analyzer and the chosen program Plant Simulation serve different purposes but within similar areas. FACTS Analyzer offers a simple and effective 2D simulation experience that is particularly useful for students and companies planning production. On the other hand, Plant Simulation provides extensive capabilities to model everything from individual production lines to entire facilities in a clear 3D environment, making it easier for people without in-depth knowledge of the field to understand the simulations. In conclusion, the study shows that both programs can complement each other by offering unique capabilities in different domains, providing companies with a competitive advantage by optimizing their production and planning processes.

In the context of recent global disruptions, enhancing supply chain resilience has become critical for businesses. This thesis proposes a conceptual model for implementing a Digital Twin (DT) in the supplier selection process to enhance supply chain resilience. By integrating real-time monitoring, scenario testing, and collaborative platforms, the model addresses three main pillars of supply chain resilience: SC Visibility, SC Agility, and SC Collaboration. The research employs a systematic literature review for the identification of the three pillars and the development of the Supplier Management Digital Twin (SMDT) framework. The findings suggest that the DT can significantly improve supply chain visibility through continuous KPI monitoring, enhance agility by simulating disruption scenarios, and foster collaboration via shared data platforms. Additionally, a case study on the supplier selection process was conducted, through semi-structured interviews, that when applied through the SMDT framework showcased what factors in the supplier selection process that can be applied to a DT. This study contributes to the theoretical understanding of DTs in Supply Chain Management and offers practical insights for managers seeking to enhance resilience to their supply chain. Future research could explore greater insights from DT experts and implementation through simulation models to further validate and expand the model's utility. / I samband med dagens globala störningar har förbättringen av leveranskedjans motståndskraft blivit mer kritiskt än någonsin för världens företag. Denna rapport föreslår en konceptuell modell för att implementera en Digital Tvilling (DT) i leverantörsvalprocessen för att stärka leveranskedjans motståndskraft. Genom att integrera realtidsövervakning, scenariotestning och samarbetsplattformar adresserar modellen tre grundpelare av leveranskedjans motståndskraft: Synlighet, Agilitet och Samarbete. Rapporten använder en systematisk litteraturstude för att identifiera de tre grundpelarna samt utveckla ett ramverk för en DT. Resultaten tyder på att en DT avsevärt kan förbättra leveranskedjans synlighet genom kontinuerlig KPI-övervakning, öka agiliteten genom att simulera scenarier av olika störningar och främja samarbetet via en delad plattform. Dessutom genomfördes en fallstudie av leverantörvalsprocessen genom semi-strukturerade intervjuer, vilket via en analys av Leverantörshanterings DT-ramverket visar vilka faktorer i leverantörvalsprocessen som kan tillämpas på en DT. Denna studie bidrar till den teoretiska kunskapen av DT:ar i leveranskedjehantering och erbjuder praktiska insikter för chefer som söker att öka motståndskraften i sin leveranskedja. Framtida forskning kan utforska djupare insikter från DT-experter och testa implementering genom simuleringsmodeller för att ytterligare validera och utöka modellens användbarhet.

As digital tools and technologies, such as AR, VR, Digital Twin, IoT and other interactive visualization technologies are being developed, new opportunities arise to use these to optimize the efficiency of operations, not least in the area of maintenance. One industry where maintenance is of utmost importance is the railway industry, where the goal of having a punctual, reliable and cost-effective railway traffic is dependent on the availability of maintained railway vehicles. The maintenance procedures of the vehicles are performed by technicians, working in depots to fix faults and service the vehicles. However, a lack of resources, poor coordination and information handling, and complex vehicles make the work and procedures of technicians cumbersome. The purpose of this thesis project was to investigate and give recommendations on how digital tools and technologies can facilitate the maintenance of railway vehicles. To do so, a qualitative study has been performed consisting of 12 interviews with technicians and depot staff. The purpose of the study was to understand their main challenges and time-consuming activities. Moreover, the potential use of digital tools and technologies to facilitate the identified challenges has been analyzed to establish recommendations on what tools and technologies can be used and how they should be used to aid technicians. The main challenges and time-consuming activities found include finding and accessing relevant information quickly and easily, understanding the information available, and collaborating with actors for support and expertise. The digital tools and technologies recommended consist of tablets to access information quick and easy, AR to visualize instructions and create an environment for remote support, AI to quickly search and filter information to enhance troubleshooting procedures, Digital Model to visualize vehicle configuration and link and structure information, and lastly, real-time data transfer utilizing eMaintenance technologies to faster receive information about the vehicle and its faults. The results of this thesis project can give direction for future research and the development and integration of digital tools and technologies to facilitate technicians in the maintenance of railway vehicles. / I takt med att digitala verktyg och tekniker såsom AR, VR, Digital Tvilling, IoT och andra interaktiva visualiseringsteknologier utvecklas uppstår nya möjligheter att använda dessa för att optimera effektiviteten inom verksamheter, inte minst inom området för underhåll. En bransch där underhåll är av största vikt är järnvägsbranschen, där målet att ha en punktlig, pålitlig och kostnadseffektiv järnvägstrafik är beroende av tillgången av underhållna spårfordon. Underhåll på fordon utförs av fordonstekniker som arbetar i depåer för att åtgärda fel och serva fordonen. Brist på resurser, dålig samordning och informationshantering samt komplexa fordon gör dock fordonsteknikernas arbete och rutiner besvärliga. Syftet med detta examensarbete var att undersöka och ge rekommendationer hur digitala verktyg och tekniker kan underlätta underhållet av spårfordon. För att uppnå det har en kvalitativ studie genomförts bestående av 12 intervjuer med fordonstekniker och depåpersonal. Syftet med studien var att förstå de största utmaningarna och tidskrävande aktiviteter de stöter på i sitt arbete. Vidare har potentiella användningsområden hos digitala verktyg och tekniker för att underlätta de identifierade utmaningarna analyserats för att fastställa rekommendationer om vilka verktyg och tekniker som kan användas och hur de bör användas för att hjälpa fordonsteknikerna. De främsta utmaningarna och tidskrävande aktiviteterna som hittats inkluderar att snabbt och enkelt hitta och komma åt relevant information, förstå tillgänglig information, samt samarbetet med aktörer för stöd och expertis. De digitala verktygen och teknikerna som rekommenderas består av surfplattor för att nå information snabbt och enkelt, AR för att visualisera instruktioner och skapa en miljö för fjärrsupport, AI för att snabbare söka och filtrera användbar information för att effektivisera felsökningsprocedurerna, Digital Modell för att visualisera fordonskonfiguration samt länka och strukturera information, och slutligen eMaintenance teknik för överföring av realtidsdata för att snabbare få information om fordonet och dess fel. Resultatet av detta examensarbete kan ge en riktning för framtida forskning, utveckling och integration av digitala verktyg och tekniker för att underlätta för fordonstekniker i underhållet av spårfordon.

The concept of a digital twin was originated by Nasa who used advanced simulation techniques during their first moon landing but has since evolved and the areas of application have broadened considerably. In recent years, the phenomenon has received a lot of attention in relation to the real estate industry in particular, which have been followed by plenty of research into digital twins within the informatics field. However, there is still a great lack of studies that focus on the supplier’s perspective of the implementation. Therefore, the aim of this study is to identify the root causes of perceived challenges with digital twins from a supplier perspective by identifying what gives rise to problems and difficulties with the implementation of digital twins. This research contributes empirically by delving into the supplier's viewpoint, enhancing theoretical understanding through the identification of underlying causes, and practically by proposing ways to manage these foundational challenges effectively. The findings underscore the importance of addressing the knowledge gap between suppliers and clients early in the implementation process, recommending organizational and technical strategies to optimize digital twin utilization and management.

Thermomechanical processes such as hot rolling have been used in the industry for a long time to process and shape metals to a desired form with specific properties. However it can be difficult to make changes to the different process parameters. That's where it is beneficial to use a hot deformation simulator such as the Gleeble 3800-GTC. It can be used to test metals in a controlled environment where the deformation, temperature and other parameters are easily changed. When the machine uses a Hydrawedge module, it is able to simulate hot rolling using uniaxial compression at high temperatures. Swerim AB has one such machine and has requested to investigate what occurs inside a specimen during testing in the Gleeble, specifically inside two low-alloyed steels with a hardness between 400 and 500 HV. Such tests were replicated using LS-DYNA, an FE software. The goal was to acquire true stress-strain graphs that showed similar behaviour to the data from the Gleeble and plots of the effective plastic strain which could be correlated to the grain structure pattern inside the deformed cylinders. An FE-model was created which replicates the procedure. The model was verified through numerous steps. An initial mesh verification was done where the simulation time took at least 5 hours and at most 86 hours. Using a technique called mass scaling, the elements inside the model were manipulated using additional mass to increase their time step and reduce the computational time. A verification of the mass scaling was done where the computational time was weighed off against accuracy. Afterwards the friction had to be verified where it was found that the Gleeble test specimens were deformed more than necessary which was taken into account and the models were adjusted for friction verification. After all was said and done, the model had a reasonable friction coefficient with an optimal mesh and mass scaling configuration. The resulting model simulated a test of 0.5 seconds in 15 minutes and only costing at most 10 MPa in accuracy when experimental results have maximum values between 110 to 220 MPa depending on the scenario. This equals an approximate error of around 5-10%. When investigating the grain structure after 100 seconds of relaxation, the computational time amounted to 52 hours but could be reduced to 12 hours when simulating 30 seconds as there was no change in the effective plastic strain after that time. The final model has a high enough accuracy which, when combined with the Gleeble, is able to confirm material models and describe what occurs in the material during conditions akin to hot rolling.