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Dissertation
Dimensionering van een HVAC installatie voor een ééngezinswoning
Authors: --- ---
Year: 2021 Publisher: Diepenbeek : UC Limburg

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Abstract

Tijdens mijn stage maak ik deel uit van de projectdienst HVAC bij de firma STG Lambrechts. Het hoofddoel is het volledig uitwerken van een stageproject met verschillende technieken. Het project is gekozen in samenspraak met mijn bedrijfspromotor, dhr. Joris Gunther. Deze is daarna ook goedgekeurd door de stagebegeleider dhr. Naelaerts Koen. Een tweede doel is om het bedrijfsleven te leren kennen en te helpen met kleine projecten/taken op de projectdienst, zoals o.a. het lanceren van het warmteverliesberekening programma Unitherm. Het stageproject omvat een eengezinswoning te voorzien van verwarming, ventilatie en eventuele koeling. De mogelijkheden voor hernieuwbare energie worden hierin ook overwogen. Voor elk onderdeel worden er verschillende voorstellen gedaan. Deze zijn gebaseerd op de beschikbare bouwplannen en EPB-verslagen. De uitdaging bestaat hierin om een goed voorstel te formuleren, te staven en te verdedigen. Om tot een resultaat te komen is er tijdens de stage de volgende werkwijze toegepast. In de eerste weken waren er verschillende informatiesessies om mijn kennis te verruimen over de verschillende onderdelen en om deze verder uit te diepen. Na de verkregen informatie zijn er verschillende oefenprojecten gemaakt waarop de technieken zijn toegepast. Eenmaal dat deze technieken goed beheerst waren, kon er begonnen worden aan het project. Dankzij deze stage is mijn kennis enorm verbreed en kan ik een woning op verschillende manieren voorzien van verwarming, koeling en ventilatie. Ook is er geholpen met de opstart van het programma Unitherm. Nu kan er binnen de projectdienst een warmteverliesberekening gemaakt worden.

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Dissertation
Supplementing Matched Filtering with Machine Learning – applications in gravitational-wave searches
Authors: --- ---
Year: 2022 Publisher: Leuven KU Leuven. Faculteit Ingenieurswetenschappen

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The first direct observation of gravitational waves by the Laser Interferometer Gravitational Wave Observatory (LIGO) in 2015 was a monumental event. Ever since the need for better processing of the data is needed. This must be done efficiently, i.e. fast and with high enough accuracy. On that account, the idea of using machine learning models is very fascinating as they can identify properties buried in huge amounts of data. The training of such models can be quite intensive, but predictions can be made relatively quickly. Additionally, they allow the compression of a huge reference data set into a smaller machine model [19]. This project serves as a proof of concept where a novel idea is worked out. It aims to combine machine learning with the gravitational wave data processed in LIGO. Normally, one characterizes an observed gravitational wave signal by a single optimal pre-computed waveform, i.e. the most optimal template. The procedure in this project utilizes a distribution of templates for a single observed signal to retain more information. The idea is then to classify two sets of simulations. The first set contains simulated signals which belong to the same family of signals as the templates. For the second set of simulations, this is no longer true as an additional spin effect is introduced. One can confirm that corresponding simulations of the two sets, which only differ in spin, retrieve different template distributions from the LIGO processing pipeline. On a data set, built using the two groups of simulations, classification is performed using a Random Forest model and two Support Vector Machine models. No significant results were obtained. However, the framework behind this project has been worked out and can be used for further analysis.

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Dissertation
Spin decoherence in nitrogen-vacancy centers in diamond created by ion implantation

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The search for highly accurate sensors is becoming more relevant. This can be seen in advanced devices like an MRI (magnetic resonance imaging) scanner. Another popular topic is the development of a quantum computer to solve problems no normal computer is able to. In doing so, researchers then can claim that their device achieved quantum supremacy. However, a common issue in these highly advanced and accurate instruments is that they often require strong cooling. To be specific, cooling with liquid nitrogen or liquid helium and that means reaching around -200 °C or -270 °C. Therefore, it might be interesting to use an impurity in a diamond crystal that can be used at room temperature. The impurity is called a nitrogen-vacancy defect and is composed of a nitrogen atom that is neighboured by a missing atom in the diamond crystal. In this project, a study is conducted on a novel formation procedure of these nitrogen-vacancy defects and their properties. The new process consists of heating a highly pure synthetic diamond to around 800 °C and then directing a beam of nitrogen particles onto it for the formation of nitrogen-vacancy defects. The density of the formed defects and their quantum performance is measured by using optical signals. The results are compared with results from nitrogen-vacancies formed in a diamond which is exposed to a nitrogen beam but at room temperature. The conclusion is that the new high-temperature treatment is indeed beneficial for the formation of nitrogen-vacancy impurities. The quantum performance is also of a high standard, but it cannot be directly related to the heating of the diamond. This is due to a difficulty in the calibration of the nitrogen beam.

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