On the modeling of car passenger ferryship design parameters with respect to selected sea-keeping qualities and additional resistance in waves

Cepowski, Tomasz

doi:10.2478/v10012-008-0026-4

Cited by 4 publications

(5 citation statements)

References 3 publications

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“…The process of dimension reduction revealed the most relevant variables to predict aWRMS and MSI, including vessel journey duration, vessel speed, tidal range, current speed, significant wave height, and current direction. Some identified variables are typically present in the parameters used when exploring comfort or the incidence of motion sickness, including journey duration due to the negative relationship between duration and magnitudes of acceleration [36,37], the relationship between vessel speed and magnitudes of acceleration [38], and significant wave height and MSI [34,[39][40][41]. While other variables such as current speed, current direction, and tidal range are not typically present in parameters used in studies evaluating comfort and MSI, the dimensionality reduction process identified these parameters as relevant to the variance in predicting aWRMS and MSI.…”

Section: Welfare Modellingmentioning

confidence: 99%

Assessing the Welfare of Technicians during Transits to Offshore Wind Farms

Uzuegbunam,

Forster,

Williams

2023

Vibration

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Available decision-support tools rarely account for the welfare of technicians in maintenance scheduling for offshore wind farms. This creates uncertainties, especially since current operational limits might make a wind farm accessible but the vibrations from transits might be unacceptable to technicians. We explore technician exposure to vibration in transit based on the levels of discomfort and the likelihood of seasickness occurring on crew transfer vessels (CTVs). Vessel motion monitoring systems deployed on CTVs operating in the North Sea and sea-state data are used in a machine learning (ML) process to model the welfare of technicians based on operational limits applied to modelled proxy variables including composite weighted RMS acceleration (aWRMS) and motion sickness incidence (MSI). The model results revealed poor to moderate performance in predicting the proxies based on selected model evaluation criteria, raising the possibility of more data and relevant variables being needed to improve model performance. Therefore, this research presents a framework for an ML approach towards accounting for the wellbeing of technicians in sailing decisions once the highlighted limitations can be addressed.

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Section: Welfare Modellingmentioning

confidence: 99%

Assessing the Welfare of Technicians during Transits to Offshore Wind Farms

Uzuegbunam,

Forster,

Williams

2023

Vibration

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“…The multi-criteria optimization was performed by using Pareto method. Such approach made it possible to present solutions in such a form as to allow decision makers (ship owners, designers) to select the most favorable solutions in each individual case [16].…”

Section: Introductionmentioning

confidence: 99%

Designing Back Propagation Neural Network for Ship Seakeeping Investigations

Khosravi¹,

Ghassemi²

2014

AJME

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In recent years, there has been more attention to predict the behavior of vessel in the sea (sea keeping).The more the deeps of vessel increases in the high speed and light vessels, the more calculations are necessary. In this paper, a BP (back propagation) neural network is presented that keeps sea keeping indexes under the categories of input and output of the network. Evaluation is based on a corvette model, and the stability parameter of wave has been evaluated by using MATLAB software. Comparison between the network output values and the expected values represent the amount of error, which is negligible, indicating that assessing the wave's stability values is possible via using (BP) back propagation neural network.

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“…: (7) where: (8) (9) (10) (11) where: Table 2 presents statistical parameters of the developed approximation with respect to the reference data, broken down by:…”

Section: Approximating the Added Wave Resistance Coefficientmentioning

confidence: 99%

“…They enable to calculate the added resistance from an irregular wave. In turn, the methods presented in [5,6,8] omit the effect of ship speed on the wave resistance and enable to calculate the resistance contribution only from a so-called statistical wave.…”

Section: )mentioning

confidence: 99%