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Abstract
This paper presents a novel digital twin that can predict ship motions and fuel consumption in real operational conditions. The analysis is based on two optimal Deep Learning Models (DLM) namely (a) a transformer neural network used for the analysis of ship motions and (b) a Long Short-Term Memory (LSTM) network for the
prediction of ship fuel consumption. Comparisons of results against sea trial data suggest that subject to further testing and validation DLM could be used as part of a digital twin framework for safe and sustainable ship operations.
prediction of ship fuel consumption. Comparisons of results against sea trial data suggest that subject to further testing and validation DLM could be used as part of a digital twin framework for safe and sustainable ship operations.
| Original language | English |
|---|---|
| Pages | 71 - 74 |
| Number of pages | 5 |
| Publication status | Published - 20 Oct 2023 |
| MoE publication type | Not Eligible |
| Event | BUILding a DIgital Twin: requirements, methods, and applications - CNR Headquarters, Piazzale Aldo Moro, 7, 00185 , Rome, Italy Duration: 19 Oct 2023 → 20 Oct 2023 http://inm.cnr.it/buildit2023/ |
Workshop
| Workshop | BUILding a DIgital Twin |
|---|---|
| Abbreviated title | BUILD IT |
| Country/Territory | Italy |
| City | Rome |
| Period | 19/10/2023 → 20/10/2023 |
| Internet address |
Keywords
- digital twins
- ship motions
- ship fuel consumption
- big data science
- deep learning
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RETROFIT55: Retrofit solutions to achieve 55% ghg reduction by 2030
Remes, H. (Principal investigator)
01/01/2023 → 31/12/2025
Project: EU: Framework programmes funding