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.
Ocean thermal energy conversion: The promise of a clean future
Due to the world's heavy dependence on fossil fuels for electricity, pollution and global warming is on the rise. However, numerous countries are still relying on diesel generators as their main source of energy. There is a lack of practical alternative energy source that can meet the global energy demand without posing any threat to the natural environment. Ocean Thermal Energy Conversion (OTEC) is a concept that has the potential to address this growing issue. It is basically a mechanism that exploits the temperature difference between warm surface seawater and cold deep ocean water, to produce electricity. Although OTEC has a low energy density, the thermal energy in the ocean is vastly abundant. OTEC development has been dormant for a long time since it was first proposed in 1881. However, it has now regained recognition worldwide as a realistic solution to our world energy issue. Instead of having a great potential for power generation, it also carries the ability to produce high value products from the large volume of Deep Sea Water (DSW) that can be released as byproducts of OTEC plant operation. These products can hold a very profitable place in industries such as pharmaceutical, food, cosmetics and mineral water production. Further research on ocean current and DSW properties can someday lead to the commercial used of OTEC. This paper is a review of the basic concept, present status and future prospects of OTEC around the world.