Vessel hydrodynamic model tuning by Discrete Bayesian updating using simulated onboard sensor data

Han, Xu; Leira, Bernt J.; Sævik, Svein

doi:10.1016/j.oceaneng.2020.108407

Cited by 26 publications

(2 citation statements)

References 15 publications

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“…It is unclear whether the authors used a physics-, a statisticsbased modeling approach or a combination of them. In contrast, the seakeeping theory is used in (Han et al, 2021) since, as pointed out by the authors, a machine learning approach would require a large amount of data. The model update, i.e., Component 3, was not implemented because the paper aim was to update the model parameters using simulated onboard sensor data.…”

Section: Resultsmentioning

confidence: 99%

Digital Twins for Marine Operations: A Brief Review on Their Implementation

Zocco¹,

Wang²,

Van³

2023

Preprint

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While the concept of a digital twin to support maritime operations is gaining attention for predictive maintenance, real-time monitoring, control, and overall process optimization, clarity on its implementation is missing in the literature. Therefore, in this review we show how different authors implemented their digital twins, discuss our findings, and finally give insights on future research directions.

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Section: Resultsmentioning

confidence: 99%

Digital Twins for Marine Operations: A Brief Review on Their Implementation

Zocco¹,

Wang²,

Van³

2023

Preprint

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“…Notably, its advantages include suitability for small and incomplete datasets, potential for structured learning, integration of diverse information sources, explicit treatment of uncertainty, and support for decision analysis and prompt responses. Leveraging these advantages, various Bayesian models have been demonstrated [28][29][30] for parameter identification and prediction of ship motions and maneuverability, contributing to the prediction of vessel hydrodynamics. Additionally, Bayesian networks find application in risk assessment [31][32][33][34], accident scenario analysis [35][36][37], reliability analysis [38,39], and fuel consumption analysis [40,41] within the maritime domain.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Ship Main Particulars for Harbor Tugboats Using a Bayesian Network Model and Non-Linear Regression

Karaçay,

Karatuğ,

Uyanık

et al. 2024

Applied Sciences

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Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.

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