Predicting Xenon Oscillations in PWRs using Intrusive Reduced Order Modelling

The increasing presence of intermittent energy sources in the Swedish electricity grid necessitatesa transition of Swedish nuclear reactors from constant base load operation to load-followingmode. However, such changes in power can induce xenon oscillations, a phenomenonthat poses operational challenges and the risk of fuel damage. Xenon oscillations occur dueto the decay characteristics of iodine-135 and xenon-135 produced in the fission process,exhibiting a periodicity of 15 to 30 hours. Detecting these oscillations proves challengingas they may result in localized power variations while the overall power of the reactor coreremains relatively stable.This thesis aims to develop a computationally efficient and transparent model capable ofpredicting the susceptibility of nuclear reactors to unstable xenon oscillations. Two models arecreated and assessed: a simple physics-transparent model based on a one-group homogeneouscore representation, and a more involved model, which incorporates two energy groups anda heterogeneous spatial discretization with nodal resolution.Comparative analysis of the models reveals notable disparities in predicting instabilities relatedto xenon oscillations. The number of energy groups emerges as the primary factorcontributing to the discrepancies observed. Moreover, spatial resolution is critical in capturingeigenmode coupling when spatial offsets exist in the equilibrium neutron flux distribution.It is demonstrated that the latter model indicates a higher level of system instability concerningxenon oscillations.The findings underscore the significance of considering both spatial and energy resolution toaccurately assess the stability of the system

The legally mandated approximate language about AI

In light of the current explosion of application of machine learning in data analysis and inference, we examine a particular challenge raised by the new EU General Data Protection Regulation (GDPR). The challenge we address pertains particularly to the demand that analyses of a person's data must be comprehensible to that person. While there is a long tradition in viewing the world in terms of objects and properties in intuitive ways, recent decades have entertained a tension between more rule-based theories of mind (e.g., the Representational Theory of Mind) and more holistic approaches (e.g., Connectionism). While both approaches have merit, one seems to depart too much from a classical understanding of "knowing" to adequately satisfy the imminent legal reality, and the other seems to be incapable of adequately capturing modern data analysis (as of yet). As a solution to this predicament we propose a pragmatic compromise based on argumentation theory which seems to be able to provide a solid foundation in classical concepts, while at the same time permitting enthymematic presuppositions. We argue that developing a framework for explaining machine behavior in terms of abstract argumentation theory can address this dilemma -- provide sufficient expressivity while remaining true to established definitions of epistemology -- to satisfy the conditions of the GDPR and motivating concerns

Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy

Astrophysics and cosmology are rich with data. The advent of wide-area digital cameras on large aperture telescopes has led to ever more ambitious surveys of the sky. Data volumes of entire surveys a decade ago can now be acquired in a single night and real-time analysis is often desired. Thus, modern astronomy requires big data know-how, in particular it demands highly efficient machine learning and image analysis algorithms. But scalability is not the only challenge: Astronomy applications touch several current machine learning research questions, such as learning from biased data and dealing with label and measurement noise. We argue that this makes astronomy a great domain for computer science research, as it pushes the boundaries of data analysis. In the following, we will present this exciting application area for data scientists. We will focus on exemplary results, discuss main challenges, and highlight some recent methodological advancements in machine learning and image analysis triggered by astronomical applications

Georadarundersøkelse: Utstein Gard og Utstein Kloster (ID87030). Mosterøyveien, Klosterøy, gnr. 254 bnr. 1 & 25. Stavanger Kommune, Rogaland

Oppdragsgiver: Arkeologisk Museum (UiS)I tidsrommet september 2023 foretok Arkeologisk Museum, Universitetet i Stavanger, en geofysisk undersøkelse ved Utstein Kloster, på gnr. 254. bnr. 1 & 25, på Klosterøy i Stavanger kommune (figur 1). Museet undersøkte et avgrenset område tilknyttet klosteret (ID 87030), og omkringliggende kulturlandskap som omfatter en betydelig mengde arkeologiske lokaliteter. Undersøkelsen er tilknyttet forskningsprosjektet Maktens Havn som er et samarbeid mellom Arkeologisk museum, UiS, Stavanger Maritime Museum, Karmøy kommune, Geopluss og Saga Subsea. Utstein kloster er landets best bevarte klosteranlegg fra middelalderen, med både kirken og nedre etasje av øst- og sørfløyen stående og i bruk (Ekroll, 2000), og er et av 12 freda kulturmiljø i Norge (Holgersen 2015). Det opptrer i historiske kilder allerede på 1000-tallet, og da som kongsgård for Harald Hårfagre etter slaget i Hafrsfjord i år 872. Klosteret ble bygd fra 1260-årene, men det er mulig at noen bygningsdeler er eldre og skriver seg fra et tidligere kongsgårdanlegg. I middelalderen var det kloster for augustinermunker. Etter reformasjonen var klosteret ubebodd i lengre perioder og bygningene forfalt, men det ble igjen tatt i bruk på 1700-tallet som fogdegård og dette var våningshuset på Utstein Gard frem til 1933. Utstein Kloster drives i dag som museum, konferansested, selskapslokaler og konsertarena gjennom Museum Stavanger. Vår kunnskap om klosteranlegget skriver seg hovedsakelig fra historiske kilder. Det er gjort mange metalldetektorfunn på områdene rundt klosteranlegget med datering fra romersk jernalder til middelalder, men det er ikke gjort mange arkeologiske undersøkelser av området. Den geofysiske undersøkelsen hadde derfor som formål å kartlegge klosteret og de dyrkede områdene rundt, for å forsøke å finne arkeologiske strukturer som i dag ikke er synlig på overflaten. Med bakgrunn i de mange metalldetektorfunnene, historiske kilder, og blant annet grøftegraving i området, vet vi at det er stort potensiale for å finne arkeologiske spor der. Innsamling av data med georadar i felt foregikk over ca. 1 uke. Etter endt arbeid ble dataen prosessert og visualisert i dybdeskiver og deretter tolket. Det ble konkludert med at undersøkelsen avdekket en stor mengde sikre (og mulige) arkeologiske strukturer