Chalmers Open Digital Repository

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Här hittar du:

  • Studentarbeten utgivna på lärosätet, såväl kandidatarbeten som examensarbeten på grund- och masternivå
  • Digitala specialsamlingar, som t ex Chalmers modellkammare
  • Utvalda projektrapporter
 

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Senast inlagda

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Implementering av Artificiell Intelligens inom Installationsteknik: Jämförelse mellan AI-baserade och traditionella metoder och verktyg
(2024) Saad Eddin, Nour; Bergstrand, Veronica; Chalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE); Chalmers tekniska högskola / Institutionen för arkitektur och samhällsbyggnadsteknik (ACE); Trüschel, Anders; Lindholm, Torbjörn
Den snabbt växande trenden för digitalisering får allt fler företag att omvärdera sina rutiner och arbetssätt för att behålla sin konkurrenskraft på marknaden. Det ställs högre krav när det kommer till hållbarhetsfrågor och vikten av korrekt planering redan i tidiga stadier. De senaste innovationerna inom artificiell intelligens har därför blivit mycket omtalade på grund av dess potential att effektivisera och underlätta planeringsprocessen samt minska uppkomst av felkällor. När det kommer till VVS-branschen är AI fortfarande relativt outforskat, och få företag har erfarenhet av dess funktioner. Detta har bland annat inspirerat till att skriva om ämnet och därigenom väcka intresset för innovationen hos andra. I detta arbete har syftet varit att presentera och utforska den grundläggande teorin bakom artificiell intelligens för att ge mer klarhet kring begreppet och dess användningsområden. Dessutom undersöks dagens arbetssätt inom VVS-sektorn i dess tidiga skeden för att sedan förstå hur AI kan nyttjas i olika projekt. Därefter genomförs de praktiska momenten med inspiration från två befintliga projekt inom byggbranschen som redan har testat implementera AI i tidiga skeden med mål att kunna besvara arbetets syfte och frågeställningar. Det ena projektet är ett forskningssamarbete mellan BIP-koder och SBUF som syftar till att ge flödesprediktioner baserade på maskininlärningsalgoritmer. Det andra projektet har utvecklats på avdelningen för construction management på Chalmers och undersöker hur språkmodellen ChatGPT kan underlätta arbetet under planeringsfasen. Teorin och arbetsmetoderna från båda projekten har därefter använts i ett fiktivt övningsprojekt för detta arbete. De slutsatser som har dragits efter genomförandet av samtliga moment är att AI, särskilt språkmodellen ChatGPT, har potential att effektivisera arbetet inom VVS-sektorn i tidiga skeden. Detta i sin tur kan minska risken för missförstånd mellan olika discipliner, då alla har tillgång till samma underlag. Det krävs dock ytterligare utveckling och ökat intresse för innovationen innan den kan implementeras inom den dagliga verksamheten. Vidare behövs ännu mer forskning gällande AI inom VVS-branschen för att bedöma dess nytta och risker ur ett hållbarhetsperspektiv.
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Humidity Dependent Surface Charging In Air For HVDC Applications
(2024) Wang , Jin; Chalmers tekniska högskola / Institutionen för elektroteknik; Xu, Xiangdong; Hjortstam, Olof
Abstract This thesis investigates the dynamic interactions between surface charge generation at electrode-air interface and charge accumulation at dielectric-air interfaces under different atmospheric humidity, crucial for enhancing insulation performance in high-voltage direct current (HVDC) systems. Despite their critical role, the mechanisms of interface charging and the influences of humidity, have been underexplored. Through comprehensive experimental setups and a detailed test matrix, this study systematically explores how varying humidity levels affect surface charge dynamics. Employing a new experimental setup with newly developed humidity control systems, the research offers insights into the charge generation mechanism by investigating the dielectric-air interface charge accumulation dynamics under different stress conditions, providing a foundation for future enhancements in HVDC insulation technology.
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Investigative study on environmental impacts of electronics A scenario-based analysis
(2023) Ashok, Privitesh; Kanni, Swasthik; Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation; Chalmers University of Technology / Department of Technology Management and Economics; Ljunggren, Maria; Nordelöf, Anders
The automotive electronics industry is rapidly evolving, revolutionizing technological ecosystems and enhancing the efficiency, quality, reliability, and productivity of transportation. This study focuses on assessing the environmental impacts of manufacturing Electronic Control Units (ECUs) for the automotive sector, with the help of Life Cycle Assessment (LCA). A Multi-Criteria Decision Analysis (MCDA) inspired selection framework has been employed to guide in selecting the subjects for performing cradle-to-gate LCAs. Data from the International Material Data System (IMDS) is essential for performing Lifecycle Assessments (LCAs) as it provides an inventory of materials. The aim of the thesis is to develop a method for analyzing the environmental performance of ECUs. To model ECUs with varying levels of detail, precision, and workload, we have developed four modelling scenarios. We compare the newly developed scenarios with a baseline scenario that represents the current practices followed at Scania. The baseline scenario, though under-represented by not considering manufacturing stages on a component level, highlights the need for reconsideration and redesign of traditional approaches in electronics and ECU manufacturing. The results show that pursuing the highest level of detail demands substantial effort, but challenges arise when data is insufficient. The advantage of a high level of detail is to identify the environmental hotspots across the value chain. The manufacturing of printed wiring boards and integrated circuits contributes the most to emissions due to energy and process-intensive production techniques. A direct relationship has been established between the weight of printed circuit board assembly and the emissions. By presenting a range of scenarios, we contribute to the ongoing discourse on sustainable practices in the automotive electronics industry, encouraging a more holistic and environmentally conscious approach.
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Overcoming logistic barriers to enable circular supply chains A case study of physical distribution of cascaded biomass
(2024) Thrane Ødum, Miri; Chalmers tekniska högskola / Institutionen för teknikens ekonomi och organisation; Chalmers University of Technology / Department of Technology Management and Economics; Vural, Ceren Altunas; Cardenas Barbosa, Ivan Dario
The green transition is increasing the demand for renewable energy based on various sources of biomasses. Energy recovery via cascading of by-product biomasses has great potential for supporting the green transition and is enabled by circular supply chains. However, there is limited empirical research and a current lack of practical knowledge on how to structure such circular supply chains handling i.e. cascaded biomass. In turn, potentially viable circular business models fall short, despite a competitive market. This is problematic since the demand for biomass is high, and regional markets are under pressure to deliver to the demand. The consuming sectors are therefore highly dependent on facilitation of global biomass supply to produce renewable energy sources such as biogas. The study aims to explore how to overcome practical and economical barriers in a supply chain of cascading biomasses with high melting points for biogas production via logistics improvements. Through an exploratory and problem-solving approach, a scenario analysis of a case company's potential standard transport scenario and an alternative transport scenario, was conducted and analyzed according to costs and practical functionality. As the business cases had no prior examples, data was limited and had to be based on estimations and assumptions. This motivated a sensitivity analysis of the alternative solution to provide better understanding of how the business case was impacted by change in parameters. The overall aim was to enable better decision making on uncertain ground regarding the company's potential business cases. The study suggests that cost efficiency should be priority in the supply chain strategy due to current expensive standard solutions and the characteristics of circular offerings. Moreover, suggestions are made on how to overcome practical barriers and reduce costs via incremental changes in a standard logistics solution, based on changes in equipment, modes, and placing key activities where it is considered optimal both in the supply chain and cost wise. However, the focus on reducing costs to enable viability can compromise finding solutions that actually fits the changed requirements and overcome the barriers that circular offerings induce. Instead, sub optimal solutions are developed, with the same problems in a slightly cheaper package. Bundling, strategic partnerships, and sectorial innovation are proposed as three dimensions to focus attention towards. Bundling will enable volumes that can enable more tailor-made solutions for transportation, and together enable establishing solutions outside the standard scope. Strategic partnerships can enable innovation. Many potential solutions are already possible outside the actor’s standard domain, through industrial symbioses and exploitation of resources and facilities of other industrial actors. Radical innovation is dependent on strategic partnerships as well, to optimally design circular supply chains. Large volumes and many actors can leverage together and push innovation. It is crucial to critically challenge the squared, standardized, and conventional logistics sector, and its ability to adapt to new kinds of products. Radical innovation of the fundamental structures of a conventional sector does not happen overnight. But if global transportation of i.e. biomass gradually increases volumes and causes a phase-out of transportation of fossil energy sources, design of circular transport solutions might become more the norm than the exception. Enabling that tipping point might create the opportunity to rethink fundamentals of current logistics structures.
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Signal Detection with a Digital Antenna Array using Machine Learning
(2023) Ohlman, Elin; Chalmers tekniska högskola / Institutionen för elektroteknik; Maaskant, Rob; Karlsson, Alexander; Norgren, Eric
Radars and radar warning receiver systems engage in a perpetual battle for superiority. This constant struggle demands the development of new methods, and recent advancements in machine learning and image recognition may serve as sources of inspiration. In this thesis, a “You Only Look Once”-style neural network is designed to detect pulsed radar signals and estimate their angle of arrival, frequency, time of arrival, and pulse width. Employing a digital multi-channel uniform linear antenna array, a short-time Fourier transform is applied to the output from each antenna element. Afterward, beamforming is performed for various angles combining the spectrograms, producing a 3D image upon which image recognition is applied. We find that using simulated data this method works for detecting two sinusoidal signals, even in noisy environments.