Estimating the traction factor and designing the deck gear for the anchor handling tug

Carral, Luis; Naya, Salvador; Álvarez-Feal, Carlos; Pardo, Miguel Lamas; Tarrío-Saavedra, Javier

doi:10.1177/1475090216677130

Cited by 6 publications

(6 citation statements)

References 13 publications

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“…GAM modelling is an extension of the Generalized Linear Models [ 40 ] that are extensively used in many science an technology domains [ 41 – 44 ]. The response variable can be Gaussian, Gamma, Poison, or Binomial distributed and it is expressed as a function of one or more explanatory variables (quantitative and qualitative).…”

Section: Statistical Modellingmentioning

confidence: 99%

Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications

et al. 2018

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This methodology permits to simulate the performance of different Poly(D,L-lactide-co-glycolide) copolymer formulations (PDLGA) in the human body, to identify the more influencing variables on hydrolytic degradation and, thus, to estimate biopolymer degradation level. The PDLGA characteristic degradation trends, caused by hydrolysis processes, have been studied to define their future biomedical applications as dental scaffolds. For this purpose, the mass loss, pH, glass transition temperature (Tg) and absorbed water mass of the different biopolymers have been obtained from samples into a phosphate-buffered saline solution (PBS) with initial pH of 7.4, at 37°C (human body conditions). The mass loss has been defined as the variable that characterize the biopolymer degradation level. Its dependence relationship with respect to time, pH and biopolymer formulation has been modelled using statistical learning tools. Namely, generalized additive models (GAM) and nonlinear mixed-effects regression with logistic and asymptotic functions have been applied. GAM model provides information about the relevant variables and the parametric functions that relate mass loss, pH and time. Mixed effects are introduced to model and estimate the degradation properties, and to compare the PDLGA biopolymer populations. The degradation path for each polymer formulation has been estimated and compared with respect to the others for helping to use the proper polymer for each specific medical application, performing selection criteria. It was found that the mass loss differences in PDLGA copolymers are strongly related with the way the pH decay versus time, due to carboxylic acid groups formation. This may occur in those environments in which the degradation products remain relatively confined with the non degraded mass. This is the case emulated with the present experimental procedure. The results show that PDLGA polymers degradation degree, in terms of half life and degradation rate, is increasing when acid termination is included, when DL-lactide molar ratio is reduced, decreasing the midpoint viscosity, or when glycolide is not included.

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Section: Statistical Modellingmentioning

confidence: 99%

Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications

et al. 2018

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“…The transfer functions were calculated and analyzed using statistical correlation studies and multivariate linear regression techniques [22]. This methodology has recently been applied to various different engineering domains, including naval and oceanic engineering [23][24][25]. In the case of ocean engineering, following a similar methodology, Carral-Couce et al [23] developed nonlinear and multivariate linear regression models to estimate the traction of towing and anchor-handling winches.…”

Section: Statistical Response Variables and Predictorsmentioning

confidence: 99%

“…This methodology has recently been applied to various different engineering domains, including naval and oceanic engineering [23][24][25]. In the case of ocean engineering, following a similar methodology, Carral-Couce et al [23] developed nonlinear and multivariate linear regression models to estimate the traction of towing and anchor-handling winches. Additionally, the transit time to cross the Panama Canal's new locks was estimated using multivariate linear regression [24], and the effect of vessel dimensions, type, and fishing ground were also modeled to estimate net drum and winch traction for trawler design tasks [25].…”

Section: Statistical Response Variables and Predictorsmentioning

confidence: 99%

“…The predictors of a multivariate linear model should be uncorrelated in order to obtain reliable model parameter estimations, and, hence, accurate and precise predictions [23][24][25]28]. Indeed, the existence of multicollinearity leads to estimates of model parameters being highly dependent on sample data, preventing an analysis of the effect of each predictor or covariate on the response, and limiting the model's ability to generate accurate predictions.…”

Section: Correlation Analysismentioning

confidence: 99%

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Application of an Analytic Methodology to Estimate the Movements of Moored Vessels Based on Forecast Data

Sande

Figuero

Tarrío-Saavedra

et al. 2019

Water

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A port’s operating capacity and the economic performance of its concessions are intimately related to the quality of its operational conditions. This paper presents an analytical methodology for estimating the movements of a moored vessel based on field measurements and forecast data, specifically including ship dimensions and meteorological and maritime conditions. The methodology was tested and validated in the Outer Port of Punta Langosteira, A Coruña, Spain. It was determined that the significant wave height outside the port, and the ratio of the vessel’s length divided by its beam (L/B), are the variables that most influence movements. Furthermore, heave and surge are the movements with a better value of the coefficient of determination (R2 values of 0.71 and 0.67, respectively), the sway (R2 = 0.30) and roll (R2 = 0.27) being the worst when using the available forecast variables of the Outer Port of Punta Langosteira. Despite their low R2 values, sway and roll models are able to estimate the main trends of these movements. The obtained estimators provide good predictions with assumable error values (root mean square error—RMSE and mean absolute error—MAE), showing their potential application as a predictive tool. Finally, as a consequence, the A Coruña Port Authority has included the results of the methodology in its port management system allowing them to predict moored vessel behavior in the port.

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“…For these offshore mooring works, the type of vessel used is the "Anchor Handling Tug Supply (AHTS) Vessel". As the name suggests, the main missions of these vessels are [4]- [7]: -Tow the platform into position ("Tug") [8] -Manage the deployment of the platform mooring lines and anchors to position the platform in its location ( Fig. 1) ("Anchor Handling") [9].…”

Section: The Ahts Vessel and The Aht Winchmentioning

confidence: 99%

A Review of the Drive Options for Offshore Anchor Handling Winches

Pardo

Carral

Castro-Santos

et al. 2017

Brod

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This article discusses the state of the art for driving technologies of anchor handling winches for offshore operations. These systems are necessary for anchor handling operations associated with installation of many types of moored floating offshore structures. To perform these operations, it is necessary to use specialized vessels known as AHT (Anchor Handling Tug) or AHTS (Anchor Handling Tug Supply) vessel, which incorporate specific equipment such as towing winches, but with higher performance than conventional tugs both in size and in technical performance. There are nowadays two main options for driving the Anchor Handling/Towing winches: hydraulic with low or high pressure motors, and electrical with AC motors driven by Variable Frequency Drives. Additionally, a new technology is appearing in the industrial sector which is starting to be applied in the marine sector, and in some cases for anchor handing winches: the permanent magnet motor. The aim of this paper is to discuss these driving technologies and their importance in the future of this part of the marine sector.

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Estimating the traction factor and designing the deck gear for the anchor handling tug

Cited by 6 publications

References 13 publications

Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications

Statistical degradation modelling of Poly(D,L-lactide-co-glycolide) copolymers for bioscaffold applications

Application of an Analytic Methodology to Estimate the Movements of Moored Vessels Based on Forecast Data

A Review of the Drive Options for Offshore Anchor Handling Winches

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