| Data-driven ship digital twin for estimating the speed loss caused by the marine fouling A Coraddu, L Oneto, F Baldi, F Cipollini, M Atlar, S Savio Ocean Engineering 186, 106063, 2019 | 251 | 2019 |
| Machine learning approaches for improving condition-based maintenance of naval propulsion plants A Coraddu, L Oneto, A Ghio, S Savio, D Anguita, M Figari Proceedings of the Institution of Mechanical Engineers, Part M: Journal of …, 2016 | 221 | 2016 |
| Vessels fuel consumption forecast and trim optimisation: A data analytics perspective A Coraddu, L Oneto, F Baldi, D Anguita Ocean Engineering 130, 351-370, 2017 | 203 | 2017 |
| Determining the most influential human factors in maritime accidents: A data-driven approach A Coraddu, L Oneto, BN de Maya, R Kurt Ocean Engineering 211, 107588, 2020 | 100 | 2020 |
| Condition-based maintenance of naval propulsion systems with supervised data analysis F Cipollini, L Oneto, A Coraddu, AJ Murphy, D Anguita Ocean Engineering 149, 268-278, 2018 | 99 | 2018 |
| Analysis of twin screw ships' asymmetric propeller behaviour by means of free running model tests A Coraddu, G Dubbioso, S Mauro, M Viviani Ocean Engineering 68, 47-64, 2013 | 88 | 2013 |
| Condition-based maintenance of naval propulsion systems: Data analysis with minimal feedback F Cipollini, L Oneto, A Coraddu, AJ Murphy, D Anguita Reliability Engineering & System Safety 177, 12-23, 2018 | 84 | 2018 |
| Time-dependent biofouling growth model for predicting the effects of biofouling on ship resistance and powering D Uzun, YK Demirel, A Coraddu, O Turan Ocean Engineering 191, 106432, 2019 | 64 | 2019 |
| Numerical investigation on ship energy efficiency by Monte Carlo simulation A Coraddu, M Figari, S Savio Proceedings of the institution of mechanical engineers, part M: journal of …, 2014 | 55 | 2014 |
| Ship efficiency forecast based on sensors data collection: Improving numerical models through data analytics A Coraddu, L Oneto, F Baldi, D Anguita OCEANS 2015-Genova, 1-10, 2015 | 46 | 2015 |
| Marine dual fuel engines monitoring in the wild through weakly supervised data analytics A Coraddu, L Oneto, D Ilardi, S Stoumpos, G Theotokatos Engineering Applications of Artificial Intelligence 100, 104179, 2021 | 45 | 2021 |
| A novelty detection approach to diagnosing hull and propeller fouling A Coraddu, S Lim, L Oneto, K Pazouki, R Norman, AJ Murphy Ocean Engineering 176, 65-73, 2019 | 44 | 2019 |
| Predicting the cavitating marine propeller noise at design stage: A deep learning based approach L Miglianti, F Cipollini, L Oneto, G Tani, S Gaggero, A Coraddu, M Viviani Ocean Engineering 209, 107481, 2020 | 43 | 2020 |
| A review of operations and maintenance modelling with considerations for novel wind turbine concepts J McMorland, C Flannigan, J Carroll, M Collu, D McMillan, W Leithead, ... Renewable and Sustainable Energy Reviews 165, 112581, 2022 | 42 | 2022 |
| Recent advances in understanding the flow over bluff bodies with different geometries at moderate Reynolds numbers MR Lekkala, M Latheef, JH Jung, A Coraddu, H Zhu, N Srinil, BH Lee Ocean Engineering 261, 111611, 2022 | 39 | 2022 |
| Numerical methods for monitoring and evaluating the biofouling state and effects on vessels’ hull and propeller performance: A review I Valchev, A Coraddu, M Kalikatzarakis, R Geertsma, L Oneto Ocean Engineering 251, 110883, 2022 | 35 | 2022 |
| Optimisation-based system designs for deep offshore wind farms including power to gas technologies F Baldi, A Coraddu, M Kalikatzarakis, D Jeleňová, M Collu, J Race, ... Applied Energy 310, 118540, 2022 | 35 | 2022 |
| Unsupervised deep learning for induction motor bearings monitoring F Cipollini, L Oneto, A Coraddu, S Savio Data-Enabled Discovery and Applications 3, 1-13, 2019 | 33 | 2019 |
| Multidisciplinary design analysis and optimization of floating offshore wind turbine substructures: A review A Ojo, M Collu, A Coraddu Ocean Engineering 266, 112727, 2022 | 32 | 2022 |
| Vessels fuel consumption: A data analytics perspective to sustainability A Coraddu, L Oneto, F Baldi, D Anguita Soft computing for sustainability science, 11-48, 2017 | 31 | 2017 |
Luca OnetoDIBRIS - University of GenoaVerified email at unige.it
