Dr Andrea Coraddu


Naval Architecture, Ocean and Marine Engineering

Personal statement

Dr Coraddu has been Assistant Professor in the Department of Naval Architecture, Ocean & Marine Engineering at the University of Strathclyde since October 2018. His relevant professional and academic experiences include working as Teaching Associate at the University of Strathclyde, Research Associate at the School of Marine Science and Technology at Newcastle University, Research Engineer as part of the DAMEN R&D department based in Singapore, and serving as Postdoctoral Research Fellow at the University of Genoa, where he was awarded a Laurea and a PhD in Naval Architecture and Marine Engineering.


Dr Coraddu’s research focuses on modelling, optimisation and analysis of ship power plants and propulsion systems for efficiency improvement and reduction of environmental footprint. His primary research involves taking advantage of on-board data availability in assessing vessel performance, energy optimisation, and real-time monitoring of the primary systems. Utilising the latest learning algorithms and theoretical results in machine learning, Dr Coraddu is developing data-driven approaches to investigate the behaviour of complex on-board systems and their mutual interaction.



Life cycle assessment of an antifouling coating based on time-dependent biofouling model
Uzun Dogancan, Demirel Yigit Kemal, Coraddu Andrea, Turan Osman
18th Conference on Computer Applications and Information Technology in the Maritime Industries (2019)
Unintrusive monitoring of induction motors bearings via deep learning on stator currents
Cipollini Francesca, Oneto Luca, Coraddu Andrea, Savio Stefano, Anguita Davide
Procedia Computer Science Vol 144, pp. 42-51 (2018)
Condition-based maintenance of naval propulsion systems : data analysis with minimal feedback
Cipollini Francesca, Oneto Luca, Coraddu Andrea, Murphy Alan John, Anguita Davide
Reliability Engineering and System Safety Vol 177, pp. 12-23 (2018)
Air quality simulations and forecasting of along-route ship emissions in realistic meteo-marine scenarios
Orlandi Andrea, Guarnieri Francesca, Busillo Caterina, Calastrini Francesca, Coraddu Andrea
Technology and Science for the Ships of the Future 19th International Conference on Ship and Maritime Research, NAV 2018 NAV International Conference on Ship and Shipping Research, pp. 452-461 (2018)
Condition-based maintenance of naval propulsion systems with supervised data analysis
Cipollini Francesca, Oneto Luca, Coraddu Andrea, Murphy Alan John, Anguita Davide
Ocean Engineering Vol 149, pp. 268-278 (2018)
Marine safety and data analytics : vessel crash stop maneuvering performance prediction
Oneto Luca, Coraddu Andrea, Sanetti Paolo, Karpenko Olena, Cipollini Francesca, Cleophas Toine, Anguita Davide
Artificial Neural Networks and Machine Learning – ICANN 2017 26th International Conference on Artificial Neural Networks, ICANN 2017 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol 10614 LNCS, pp. 385-393 (2017)

more publications

Professional activities

Artificial Intelligence and Big Data - A Marine Engineering Perspective
Piri Reis University, Istanbul, Turkey
Visiting lecturer
Istanbul Technical University
Visiting lecturer
3rd International Symposium on Naval Architecture and Maritime
Machine Learning Solutions for Marine Applications. A Data-Driven Research Agenda
Aalto University (Finland)
Visiting researcher

more professional activities


KTP - Datum Electronics
Theotokatos, Gerasimos (Principal Investigator) Coraddu, Andrea (Co-investigator) Lazakis, Iraklis (Co-investigator)
05-Jan-2019 - 04-Jan-2021
Cross-disciplinary advanced Vibration Laboratory (£32K EPSRC Capital Award for ECRs, £11K Faculty of Engineering Strategic Research Funding)
Tubaldi, Enrico (Principal Investigator) Coraddu, Andrea (Principal Investigator) Jones, Catherine (Principal Investigator) Cartmell, Matthew (Principal Investigator)
Structural Health Monitoring (SHM) is an emerging technology for damage identification of aerospace, civil and mechanical engineering infrastructure, with significant potential for life-safety and economic benefits. Vibration-based SHM entails measuring the response of structural systems to dynamic excitations through appropriate sensors, and intelligently analysing the measured response to identify damage occurrence or degradation. This project supports the development and build of a vibration laboratory (VibLab) across the Faculty of Engineering, a new inter-disciplinary facility for there is a strong need, but is currently missing at Strathclyde. The laboratory will benefit the short- and long-term career development plans of Early Career Researchers (ECRs), enhancing their capabilities in the field of SHM. It will also strengthen the connections across departments, and contribute to maximise external funding income and attract new industrial and academic partners. The facility is a joint initiative between the departments of Civil and Environmental Engineering, Electronic and Electrical Engineering, Mechanical and Aerospace Engineering and Naval Architecture and Ocean and Marine Engineering.

£32K EPSRC Capital Award for ECRs, £11K Faculty of Engineering Strategic Research Funding, £20k total combined departmental funding.
Ships diesel engine performance modelling with combined physical and machine learning approach
Coraddu, Andrea (Principal Investigator) Oneto, Luca (Principal Investigator) Geertsma, Rinze (Principal Investigator)
The proposed research will investigate a novel approach of predicting various diesel engine performance parameters using the physical models from Delft University and the State of the Netherlands in combination with Machine Learning (ML) algorithms from Strathclyde University - NAOME, Genoa University and Damen Schelde Naval Shipbuilding. A dataset of the Holland class Oceangoing Patrol Vessels (OPV’s) from the State of the Netherlands will be used to train the machine learning algorithms and establish its performance. Moreover, the research will analyse which performance parameters can be predicted well with a physical modelling approach and which ones with a combined physical and ML approach. The research is expected to predict parameters such as engine efficiency, engine thermal loading, temperature before the turbine and exhaust valve temperature. Finally, the research will discuss how the proposed models can be used to reduce the maintenance effort on diesel engines in future using these techniques and how to integrate these into ship control systems
01-Jan-2018 - 01-Jan-2021

more projects


Naval Architecture, Ocean and Marine Engineering
Henry Dyer Building

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