Seakeeping Analysis of a Turret-Moored FPSO by Using Artificial Neural Networks

2003 ◽  
Author(s):  
S. Mazaheri ◽  
E. Mesbahi ◽  
M. J. Downie ◽  
A. Incecik
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Methodology ◽  
Wind Wave ◽  
Offshore Structures ◽  
Environmental Data ◽  
Seakeeping Analysis ◽  
Artificial Neural ◽  
Problem Comparison

Floating offshore structures, particularly floating oil production, storage and offloading systems (FPSOs) are still in great demand, both in small and large reservoirs, for deployment in deep water. The prediction of such vessels’ responses to her environmental loading over her lifetime is now often undertaken using response-based design methodology, although the approach is still in its early stages of development. Determining the vessel’s responses to hydrodynamic loads induced by long term sea environments is essential for implementing this approach effectively. However, it is often not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem. Comparison of results obtained from a conventional mathematical model with those of the ANN-based technique for the case of a 200,000 tdw tanker demonstrates that the approach can successfully predict the vessel’s responses due to arbitrary loads.

On the Use of Artificial Neural Networks for Estimating the Long-Term Mooring Lines Response Considering Wind Sea and Swell

2020 ◽  
Author(s):  
Gabriel Mattos Gonzalez ◽  
Marcos Queija de Siqueira ◽  
Marina Leivas Simão ◽  
Paulo Maurício Videiro ◽  
Luis Volnei Sudati Sagrilo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Environmental Conditions ◽  
Offshore Structures ◽  
Short Term ◽  
Statistical Parameters ◽  
Artificial Neural ◽  
Wind Sea ◽  
Term Response

Abstract Nowadays, artificial intelligence algorithms are regaining visibility mainly due to the increase in computational capability. Among those, artificial neural networks (ANN) are very useful for the regression of highly nonlinear phenomena, such as the dynamic response of offshore structures. Due to the escalating demand in the oil and gas industry, offshore fields have been explored in deeper waters, which leads to more severe environmental conditions. A reliable and efficient evaluation of the long-term response of mooring systems, a crucial element of floating offshore structures, is then imperative. The estimation of the mooring long-term response is usually obtained numerically through the convolution of the short-term responses, based on short-term stationary environmental conditions (typically 3-h). Each of these short-term responses is obtained through a time-domain dynamic structural analysis from which statistical parameters of interest are calculated, such as the mean of the tension maxima sample or the maxima frequency. Such analyses tend to be quite time-consuming and a reliable estimator of these short-term statistical parameters may be of great help. In this paper, an ANN is trained to predict the short-term extreme peak response statistical parameters. The used training datasets include the wave significant height and spectral peak period for both wind sea and swell waves, generated by Importance Sampling Monte Carlo Simulation (ISMCS) method. Fixed directions of wind sea and swell are considered. It is shown that the ANN successfully predicts the short-term response statistical parameters for both cases, which are later used for the evaluation of the long-term N-year mooring response.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

Ion-gating synaptic transistors with long-term synaptic weight modulation

2021 ◽  
Vol 9 (16) ◽  
pp. 5396-5402
Author(s):  
Youngjun Park ◽  
Min-Kyu Kim ◽  
Jang-Sik Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Synaptic Weight ◽  
Recognition Accuracy ◽  
Artificial Neural ◽  
High Recognition Accuracy

This paper presents synaptic transistors that show long-term synaptic weight modulation via injection of ions. Linear and symmetric weight update is achieved, which enables high recognition accuracy in artificial neural networks.

scholarly journals Application of Artificial Neural Networks to Predict Beach Nourishment Volume Requirements

2021 ◽  
Vol 9 (8) ◽  
pp. 786
Author(s):  
Damjan Bujak ◽  
Tonko Bogovac ◽  
Dalibor Carević ◽  
Suzana Ilic ◽  
Goran Lončar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Spatial Variability ◽  
Coastal Management ◽  
Wind Wave ◽  
Predictive Ability ◽  
Test Set ◽  
Adriatic Coast ◽  
Important Input ◽  
Artificial Neural

The volume of material required for the construction of new and expansion of existing beach sites is an important parameter for coastal management. This information may play a crucial role when deciding which beach sites to develop. This work examines whether artificial neural networks (ANNs) can predict the spatial variability of nourishment requirements on the Croatian coast. We use survey data of the nourishment volume requirements and gravel diameter from 2016 to 2020, fetch length, beach area and orientation derived from national maps which vary from location to location due to a complex coastal configuration on the East Adriatic coast, and wind, tide, and rainfall data from nearby meteorological/oceanographic stations to train and test ANNs. The results reported here confirm that an ANN can adequately predict the spatial variability of observed nourishment volumes (R and MSE for the test set equal 0.87 and 2.24 × 104, respectively). The contributions of different parameters to the ANN’s predictive ability were examined. Apart from the most obvious parameters like the beach length and the beach areas, the fetch length proved to be the most important input contribution to ANN’s predictive ability, followed by the beach orientation. Fetch length and beach orientation are parameters governing the wind wave height and direction and hence are proxies for forcing.

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

2020 ◽  
pp. 147509022092520
Author(s):  
Yuliang Zhao ◽  
Sheng Dong ◽  
Fengyuan Jiang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Environmental Data ◽  
Mooring Lines ◽  
Widespread Application ◽  
Floating Structures ◽  
Extreme Response ◽  
Artificial Neural

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.

On the Analysis of Turbulent Flow Signals by Artificial Neural Networks and Adaptive Techniques

2007 ◽  
Author(s):  
F. Lo´pez Pen˜a ◽  
F. Bellas ◽  
R. J. Duro ◽  
P. Farin˜as
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Irregular Sampling ◽  
Linear Filter ◽  
Signal Characteristics ◽  
Prediction Mode ◽  
Sampled Signal ◽  
Artificial Neural ◽  
Long Term Prediction

Artificial Neural Networks (ANNs) and evolution are applied to the analysis of turbulent signals. In a first instance, a new trainable delay based artificial neural network is used to analyze Hot Wire Anemometer (HW) signals obtained at different positions within the wake of a circular cylinder with Reynolds number values ranging from 2000 to 8000. Results show that these networks are capable of performing accurate short term predictions of the turbulent signal. In addition, the ANNs can be set in a long term prediction mode resulting in a sort of non linear filter able to extract the features having to do with the larger eddies and coherent structures. In a second stage these networks are used to reconstruct a regularly sampled signal straight from the irregularly sampled one provided by a Laser Doppler Anemometer (LDA). The irregular sampling dynamics of the LDA signals is governed by the arrival of the seeding particles, superimposing the already complex turbulent signal characteristics. To cope with this complexity, an evolutionary based strategy is used to perform an adaptive and continuous online training of the ANNs. This approach permits obtaining a regularly sampled signal not by interpolating the original one, as it is often done, but by modeling it.

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