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Information Journal Paper

Title

THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS

Pages

  1-13

Abstract

 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) modeling 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 200,000tdw tanker demonstrates that the approach can successfully predict the vessel's responses due to arbitrary loads.

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    APA: Copy

    MAZAHERI, SAEID. (2006). THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS. JOURNAL OF MARINE ENGINEERING, 3(4), 1-13. SID. https://sid.ir/paper/60927/en

    Vancouver: Copy

    MAZAHERI SAEID. THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS. JOURNAL OF MARINE ENGINEERING[Internet]. 2006;3(4):1-13. Available from: https://sid.ir/paper/60927/en

    IEEE: Copy

    SAEID MAZAHERI, “THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS,” JOURNAL OF MARINE ENGINEERING, vol. 3, no. 4, pp. 1–13, 2006, [Online]. Available: https://sid.ir/paper/60927/en

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