ANN-based surrogate models for the analysis of mooring lines and risers

Pina, Aloísio Carlos de; Pina, Aline Aparecida de; Albrecht, Carl Horst; Lima, Beatriz Souza Leite Pires de; Jacob, Breno Pinheiro

doi:10.1016/j.apor.2013.03.003

Cited by 69 publications

(19 citation statements)

References 27 publications

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“…(2) and (3), expressions for the local gradients ı j and ı o corresponding to the hidden and output layers respectively may be derived, following a reasoning similar to the presented in [25][26][27], particularized for the use of the logistic activation function of Eq. (1).…”

Section: Training and Validationmentioning

confidence: 99%

“…In [24] an "exogenous" model was presented where time series of line tensions are estimated using the time series of known platform motions, with an ANN trained by a short initial window of the tensions that result from a FE simulation with these motions prescribed at the top of the line. Subsequently, in [25] a NARX model (Nonlinear AutoRegressive with eXogenous inputs) was proposed to obtain enhanced estimations, by relating the present value of the desired tension time series not only to present and past values of the platform motions (i.e. the exogenous series), but also to past values of the desired series itself.…”

Section: Introductionmentioning

confidence: 99%

“…In those works, given a specific configuration for the mooring system of a turret-moored FPSO in the context of a response-based design, an ANN was generated to predict extreme values for the offsets under different metocean conditions. Still considering a specific configuration for the system, the use of meta-models has been presented in [24][25][26] to obtain full time-series of mooring line or riser tensions. In [24] an "exogenous" model was presented where time series of line tensions are estimated using the time series of known platform motions, with an ANN trained by a short initial window of the tensions that result from a FE simulation with these motions prescribed at the top of the line.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Pina¹,

Monteiro²,

Albrecht³

et al. 2016

Applied Ocean Research

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Section: Training and Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Pina¹,

Monteiro²,

Albrecht³

et al. 2016

Applied Ocean Research

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“…ANN was used to predict the extreme values of a turret-moored FPSO under different metocean condition with specific configuration for the mooring system. The use of meta-models has been presented in [44][45][46] to obtain full timeseries of mooring line or riser tensions. Pina [44] use Neural Network with an "exogenous" model, he presented estimation of line tension of moorings in time series using NARX, and then compare it results with FE simulation.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dynamic responses of floating structures using NARX with mirroring technique

et al. 2018

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Displacements, velocities and accelerations of Six Degree of freedom of a single floating structure was predicted using Time Series NARX feedback neural Networks. The nonlinear autoregressive network with exogenous inputs (NARX) is a recurrent dynamic network, with feedback connections enclosing several layers of the network is based on the linear ARX model, which is commonly used in time-series modelling is used in this study. Time series data of displacements of a single floating structure was used for training and testing the ANN model. In the training stage, this time series data of environment parameters was used as input and dynamic responses was used as target. Benchmarking result and error prediction was compared between two techniques of Neural Network training. The prediction result of the model responses can be concluded that NARX with mirroring technique increase the accuracy and can be used to predict time series of dynamic responses of floating structures.

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“…One of the most promising alternatives to evaluate dynamic responses of marine structures with a significant reduction in computational time is the utilization of hybrid methods combining FEA with Artificial Neural Networks (ANNs) [1][2][3][4][5][6][7]. The basic idea of these methods is to employ the remarkable capacity of learning, generalization, and prediction of neural networks to replace the onerous numerical integration of a time domain dynamic analysis by finite elements method.…”

Section: Introductionmentioning

confidence: 99%

Neural Networks Applied to the Wave-Induced Fatigue Analysis of Steel Risers

Cortina

Sousa

Sagrilo

2018

Mathematical Problems in Engineering

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Time domain stochastic wave dynamic analyses of offshore structures are computationally expensive. Considering the wave-induced fatigue assessment for such structures, the combination of many environmental loading cases and the need of long time-series responses make the computational cost even more critical. In order to reduce the computational burden related to the wave-induced fatigue analysis of Steel Catenary Risers (SCRs), this work presents the application of a recently developed hybrid methodology that combines dynamic Finite Element Analysis (FEA) and Artificial Neural Networks (ANN). The methodology is named hybrid once it requires short time series of structure responses (obtained by FEA) and imposed motions (evaluated analytically) to train an ANN. Subsequently, the ANN is employed to predict the remaining response time series using the prescribed motions imposed at the top of the structure by the floater unit. In this particular work, the methodology is applied aiming to predict the tension and bending moments’ time series at structural elements located at the top region and at the touchdown zone (TDZ) of a metallic riser. With the predicted responses (tensions and moments), the stress time series are determined for eight points along the pipe cross sections, and stress cycles are identified using a Rainflow algorithm. Fatigue damage is then evaluated using SN curves and the Miner-Palmgren damage accumulation rule. The methodology is applied to a SCR connected to a semisubmersible platform in a water depth of 910 m. The obtained results are compared to those from a full FEA in order to evaluate the accuracy and computer efficiency of the hybrid methodology.

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ANN-based surrogate models for the analysis of mooring lines and risers

Cited by 69 publications

References 27 publications

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

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

Prediction of dynamic responses of floating structures using NARX with mirroring technique

Neural Networks Applied to the Wave-Induced Fatigue Analysis of Steel Risers

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