A ship digital twin for safe and sustainable ship operations
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Date
2023-10-20
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en
Pages
5
71 - 74
71 - 74
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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.Description
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digital twins, ship motions, ship fuel consumption, big data science, deep learning
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Citation
Zhang, M, Hirdaris, S & Tsoulakos, N 2023, ' A ship digital twin for safe and sustainable ship operations ', BUILding a DIgital Twin, Rome, Italy, 19/10/2023 - 20/10/2023 pp. 71 - 74 .