Η διδακτορική διατριβή της κας Ιωάννας Κορομηλά έχει ως βασικό σκοπό την πρόταση μιας νέας μεθόδου εκτίμησης διακινδύνευσης πυρκαγιάς, με βάση τις αρχές ποσοτικής διακινδύνευσης (ευρέως γνωστή ως QRA) όπως αυτές ορίζονται στα πλαίσια εναλλακτικής σχεδίασης του Διεθνούς Ναυτιλιακού Οργανισμού ΙΜΟ. Η μέθοδος αυτή θα υποστηρίζει τον σχεδιασμό επιβατηγών πλοίων, παρέχοντας φυσικά τη δυνατότητα μελλοντικής εφαρμογής της και σε άλλους τύπους πλοίων. Το προτεινόμενο μοντέλο θα υποστηρίζει την σύγκριση μεταξύ διαφορετικών εναλλακτικών σχεδιαστικών λύσεων, προσδιορίζοντας με μία τιμή το επίπεδο ασφάλειας, ώστε να υποβοηθήσει στη διαδικασία λήψης αποφάσεων. Πιο συγκεκριμένα θα αναπτυχθεί τόσο το εννοιολογικό όσο και το μαθηματικό πλαίσιο για τον υπολογισμό της διακινδύνευσης πυρκαγιάς, καθώς επίσης θα αναδειχθεί η χρησιμότητα των προηγμένων εργαλείων προσομοίωσης πυρκαγιάς και εκκένωσης (συμπεριλαμβανομένων του Fire Dynamics Simulator και PathFinder). Το μαθηματικό μοντέλο θα χρησιμοποιηθεί για την επίδειξη της μεθόδου σε μέρος κρουαζιερόπλοιου.

Η διδακτορική διατριβή της κας. Κορομηλά διεξάγεται στη Σχολή Ναυπηγών Μηχανολόγων Μηχανικών του Εθνικού Μετσόβιου Πολυτεχνείου και εποπτεύεται από τον καθηγητή Κ. Σπύρου, τη Δρ. Ζωή Νικολιανίτη και τον καθηγητή Γ. Αθανασούλη.

This project aims to develop novel tools for energy disaggregation and monitoring of device health status. These tools will perform analysis of complex energy load time-series using real-time pattern recognition/matchmaking and hardware accelerated algorithms, and will transmit the recognised events to a main server.

During its first 20 months, the project has successfully achieved the milestones set in the proposal. Following the initial training of the Fellow -also involving the review of cutting edge methodologies and current trends-, a prototype was developed for High frequency sampling of energy data. Deployment of the prototype to a commercial building has so far provided over 1Tb of energy consumption and ground truth data.

Publications:

1. Kotsilitis S., Marcoulaki E., Kalligeros E., Mousmoulas Y., 2018. Energy efficiency and predictive maintenance applications using smart energy measuring devices. In S. Haugen, A. Barros, C. van Gulijk, T. Kongsvik & J.E. Vinnem (eds.) “Safety and Reliability – Safe Societies in a Changing World”, CRC Press, ISBN 978-0-8153-8682, pp. 987-994, https://www.taylorfrancis.com/books/9781351174657

2. Kotsilitis S., Marcoulaki E., Kalligeros E. & Mousmoulas Y., 2018. Distributed edge computing paradigm with dedicated devices for energy efficiency and predictive maintenance applications. In “Industrial Internet of Things and Smart Manufacturing”, Springer Series on Lecture Notes on Data Engineering and Communications Technologies (NDECT), in press (ISBN: 978-1-912532-06-3)

3. Kotsilitis S., Marcoulaki E., Kalligeros E., 2019. High Frequency Energy Disaggregation Sampling and Analysis towards Predictive Maintenance Applications. In M. Beer & E. Zio (eds.) “Proceedings of the 29th European Safety and Reliability Conference”, Research Publishing, Singapore, pp. 1214-1222, ISBN: 978-981-11-2724-3; https://doi.org/10.3850/978-981-11-2724-3.

Hellenic Seawaters, with the Aegean Archipelago consisting their center, form one of the most important maritime links of the Mediterranean Sea. At the same time, they are also characterized by an extremely rich and unique marine environment with a large coastline, thousands of islands and intense activity of ships. The coexistence of so many important countries around the Mediterranean along with the geomorphology of their coasts has resulted in increased maritime traffic through specific routes in the Aegean Sea.

These routes represent a unique example of an area where a serious marine accident with high environmental impact may happen. Hence, the need to reduce the possibility of maritime accidents in the Aegean is of vital importance, as a possible accident would affect all the social, economic, environmental and cultural sectors of Greece and the wider region of the Eastern Mediterranean basin. There is therefore a pressing need to identify ships with a potentially high accident risk crossing the Aegean Sea by developing a suitable model and highlighting areas with increased environmental risk in the area.

The current PhD thesis stems from the above-mentioned need and its goal is the monitoring and reduction of ecological risk in Greek national waters and especially in the Aegean Sea, due to a marine accident. The probability of a vessel accident occurrence is due to internal (vessel) parameters (age and kind of the vessel etc), and external (dynamical) parameters (weather etc). The former present a static character because they cannot change during the travel of a vessel. Instead the latter have a dynamic character because they continuously change. Another important aspect of the current research is the development of a consequences model which will refer to the estimated cleanup cost after a certain accident occurs. The model developed is based on Bayesian Networks and Fuzzy Logic.