Artificial Neural Networks for the analysis of spread⿿mooring configurations for floating production systems

Pina, Aline Aparecida de; Monteiro, Bruno da Fonseca; Albrecht, Carl Horst; Lima, Beatriz Souza Leite Pires de; Jacob, Breno Pinheiro

doi:10.1016/j.apor.2016.06.010

Cited by 23 publications

(4 citation statements)

References 33 publications

(24 reference statements)

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“…The 500 s worth of data in the tension time series is used as the training and verification data of the BPNN, the time interval of the sequence is 1.2 s, and among those training data, 80% were selected for training (333) and 20% for validation (83). For the mooring tension time series prediction in this work, 10 neurons in the hidden layer are implemented using MATLAB language, we perform a preliminary parametric study to define the choice of 10 neurons (depicted in Figure 11), and the final choice is consistent with the results presented in the literature; 43 more neurons in hidden layers could not significantly improve the accuracy of prediction results with increasing computation times. The correlation coefficients of the first set of data between the prediction tension and the actual tension at three stages of training, validation, and testing are shown in Figure 12.…”

Section: Case Studymentioning

confidence: 93%

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Reliability analysis of mooring lines for floating structures using ANN-BN inference

Zhao

Dong

Jiang

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The harsh marine environment is a significant threat to the safety of floating structure systems. To address this, mooring systems have seen widespread application as an important component in the stabilization of floating structures. This article proposes a methodology to assess the reliability of mooring lines under given extreme environmental conditions based on artificial neural network–Bayesian network inference. Different types of artificial neural networks, including radial basis function neural networks and back propagation neural networks, are adopted to predict the extreme response of mooring lines according to a series of measured environmental data. A failure database under extreme sea conditions is then established in accordance with the failure criterion of mooring systems. There is a failure of mooring lines when the maximum tension exceeds the allowable breaking strength. Finally, the reliability analysis of moored floating structures under different load directions is conducted using Bayesian networks. To demonstrate the proposed methodology, the failure probability of a sample semi-submersible platform at a water depth of 1500 m is estimated. This approach utilizes artificial neural networks’ capacity for calculation efficiency and validates artificial neural networks for the response prediction of floating structures. Furthermore, it can also be employed to estimate the failure probability of other complex floating structures.

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Section: Case Studymentioning

confidence: 93%

“…A Nonlinear AutoRegressive with eXogenous (NARX) model was introduced to the NN, which is well suited for simulating nonlinear systems. 43,44 The NARX-BPNN model equation is expressed as follows…”

Section: Methodsmentioning

confidence: 99%

Reliability analysis of mooring lines for floating structures using ANN-BN inference

Zhao

Dong

Jiang

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…Surrogate models built for the assessment of the motions of a moored structure and the tensions in the mooring lines have generally made use of artificial neural networks (de Pina et al 2013(de Pina et al , 2016Sidarta et al 2017). The use of surrogate models for mooring system assessment, has, however, not been undertaken in the context of optimizing the mooring system.…”

Section: Introductionmentioning

confidence: 99%

Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm

Pillai

Thies

Johanning

2018

Engineering Optimization

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This article presents a novel framework for the multi-objective optimization of offshore renewable energy mooring systems using a random forest based surrogate model coupled to a genetic algorithm. This framework is demonstrated for the optimization of the mooring system for a floating offshore wind turbine highlighting how this approach can aid in the strategic design decision making for real-world problems faced by the offshore renewable energy sector. This framework utilizes validated numerical models of the mooring system to train a surrogate model, which leads to a computationally efficient optimization routine, allowing the search space to be more thoroughly searched. Minimizing both the cost and cumulative fatigue damage of the mooring system, this framework presents a range of optimal solutions characterizing how design changes impact the trade-off between these two competing objectives.

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“…Umar and Datta [148] and Murhpy et al [149] represent nonlinear restoring force terms with polynomials, whose coefficeints are identified by fitting the polynomials to the restoring force versus displacement curves obtained from numerical calculations. The dynamics of the mooring system have also been replicated by Volterra models [150,151] and neural networks [152][153][154][155][156][157].…”

Section: System Identificationmentioning

confidence: 99%

Mathematical Modelling of Mooring Systems for Wave Energy Converters—A Review

2017

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Mathematical analysis is an essential tool for the successful development and operation of wave energy converters (WECs). Mathematical models of moorings systems are therefore a requisite in the overall techno-economic design and operation of floating WECs. Mooring models (MMs) can be applied to a range of areas, such as WEC simulation, performance evaluation and optimisation, control design and implementation, extreme load calculation, mooring line fatigue life evaluation, mooring design, and array layout optimisation. The mathematical modelling of mooring systems is a venture from physics to numerics, and as such, there are a broad range of details to consider when applying MMs to WEC analysis. A large body of work exists on MMs, developed within other related ocean engineering fields, due to the common requirement of mooring floating bodies, such as vessels and offshore oil and gas platforms. This paper reviews the mathematical modelling of the mooring systems for WECs, detailing the relevant material developed in other offshore industries and presenting the published usage of MMs for WEC analysis.

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Artificial Neural Networks for the analysis of spread⿿mooring configurations for floating production systems

Cited by 23 publications

References 33 publications

Reliability analysis of mooring lines for floating structures using ANN-BN inference

Reliability analysis of mooring lines for floating structures using ANN-BN inference

Mooring system design optimization using a surrogate assisted multi-objective genetic algorithm

Mathematical Modelling of Mooring Systems for Wave Energy Converters—A Review

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