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.
- 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
Naval Architecture, Ocean and Marine Engineering
Henry Dyer Building
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