THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS
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.
Nonlinear response prediction of spar platform using artificial neural network / Md. Alhaz Uddin
Due to global energy demand, offshore industries are moving towards deep and ultra-deep waters for oil and gas exploration in ocean environment. Floating offshore structures such as spar platform is considered to be the most economic and suitable offshore structure in deep water regions. During oil and gas exploration, floating offshore structures may sometimes be affected by critical environmental forces. Quick decision must be taken either to continue or to stop production, on the basis of response prediction of offshore structures under forecasted environmental conditions. Finite Element Method (FEM) is an important technique to predict the response of offshore structures considering all nonlinearities. However, FEM is a highly time-consuming process for predicting the response of platforms and usually used as a final analysis tool. On the other hand, Artificial Neural Networks (ANN) can predict response in rapid mode. ANNs are also capable of providing efficient solutions to problems such as damage detection, time series prediction and control where formal analysis is highly complex. 
This study presents nonlinear response prediction of spar platform for various environmental forces using ANN. The neural network has three layers, namely the input, output, and hidden layer. A hyperbolic tangent function is considered in the present study as an activation function. Environmental forces and structural parameters are used as inputs and FEM-based time history of spar platform responses are used as targets. Feed-forward neural networks with back-propagation algorithm are used to train the network. After training the network, the response of the spar platform is obtained promptly for newly selected environmental forces. The response obtained using ANN is validated by conventional FEM analysis. It has been observed that using completely new environmental forces as input to ANN, the time history response of spar platform can be very accurately predicted. Results show that the ANN approach is very efficient and significantly reduces the time for predicting response time histories.
De-synchronized generator using a synchronous turbo-generator and a matrix converter
The aim of this paper concerns high speed synchronous generators driven directly by gas or steam turbines. After reminding problematics and interest of variable speed in high power plants the authors present a brief state of the art about the abilities of classical static inverters to adapt variable frequency to grid ones. Considering the lack of practical solutions, they propose a structure based upon an original use of a matrix converter. Its principle is validated through first experimental results.