The main purpose of Ms. Ioanna Koromilas’ phD dissertation is to propose a general risk model for assessing a passenger ship’s fire safety level at the design stage. It will be based on the principles of quantitative risk assessment (QRA), as set out in the alternative design requirements of the International Maritime Organization (IMO). This method will support the design of passenger ships, whilst allowing its future application to other ship types. The proposed model will be able to determine the safety level of a ship by enhancing the decision-making process for the evaluation of various alternative designs. More specifically, both the conceptual and mathematical framework for calculating fire risk will be developed, as well as will be employed advanced fire and evacuation simulation tools (including Fire Dynamics Simulator and PathFinder). The mathematical model will be used to demonstrate the method on a cruise ship.

Ms Koromila’s phD is conducted at the School of Naval Architecture and Marine Engineering of the National Technical University of Athens and is supervised by Professor K. Spyrou, Dr Zoe Nivolianitou, and Professor G. Athanasoulis.

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.