dc.contributor.author | Μαμαλουκάς, Πάτροκλος Μιλτιάδης![]() |
el |
dc.contributor.author | Mamaloukas, Patroklos Miltiadis![]() |
en |
dc.date.accessioned | 2023-01-09T08:19:33Z | |
dc.date.available | 2023-01-09T08:19:33Z | |
dc.identifier.uri | https://dspace.lib.ntua.gr/xmlui/handle/123456789/56545 | |
dc.identifier.uri | http://dx.doi.org/10.26240/heal.ntua.24243 | |
dc.description | Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Φυσική και Τεχνολογικές Εφαρμογές” | el |
dc.rights | Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/gr/ | * |
dc.subject | Machine Learning Disruption | en |
dc.subject | Plasma | en |
dc.subject | Tokamak | en |
dc.title | Using machine learning algorithms to detect plasma disruptions in fusion reactors | en |
heal.type | masterThesis | |
heal.secondaryTitle | Χρήση Αλγορίθμων Μηχανικής Μάθησης για την Ανίχνευση Διασπάσεων Πλάσματος σε Αντιδραστήρες Σύντηξης | el |
heal.classification | Plasma Physics, Artificial Intelligence | el |
heal.language | en | |
heal.access | free | |
heal.recordProvider | ntua | el |
heal.publicationDate | 2022-10-01 | |
heal.abstract | The aim of this thesis is to investigate how Machine Learning algorithms can be used to predict disruptions in fusion reactors. These events are called such because they disrupt a tokamak’s ability to confine plasma and cause a great deal of damage to the structure and equipment. Tokamaks are the leading design principle in fusion reactor design, so is ITER, the largest soon-to-be-built reactor ever conceived; thus, phenomena such as these must be prevented to the best of our abilities, to avoid compromising the future of energy production. Using bolometer data from the JET Tokamak reactor, we feed a 3-tier (Convolutional, LSTM and Linear Layers) Machine Learning model and iterate over sets of hyperparameters. Comparing different combinations of hyperparameters gives a qualitative perspective on the optimal configuration for our model and results are graded according to their f1-scores. Future improvements and optimizations are also suggested. | en |
heal.advisorName | Κομίνης, Ιωάννης | el |
heal.committeeMemberName | Κόκκορης, Μιχαήλ | el |
heal.committeeMemberName | Αναγνωστόπουλος, Κωνσταντίνος | el |
heal.academicPublisher | Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Εφαρμοσμένων Μαθηματικών και Φυσικών Επιστημών. Τομέας Φυσικής | el |
heal.academicPublisherID | ntua | |
heal.numberOfPages | 96 σ. | el |
heal.fullTextAvailability | false |
Οι παρακάτω άδειες σχετίζονται με αυτό το τεκμήριο: