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Prediction of Offshore Structural Response Extreme Values by Modified Finite-Memory Nonlinear System Modeling

[+] Author Affiliations
N. I. Mohd Zaki, M. K. Abu Husain, N. A. Mukhlas

Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

G. Najafian

University of Liverpool, Liverpool, UK

Paper No. OMAE2016-54204, pp. V003T02A039; 7 pages
doi:10.1115/OMAE2016-54204
From:
  • ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 3: Structures, Safety and Reliability
  • Busan, South Korea, June 19–24, 2016
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-4994-1
  • Copyright © 2016 by ASME

abstract

Offshore structures are exposed to random wave loading in the ocean environment, and hence the probability distribution of the extreme values of their response to wave loading is of great value in the design of these structures. Due to nonlinearity of the drag component of Morison’s wave loading and also due to intermittency of wave loading on members in the splash zone, the response is often non-Gaussian; therefore, simple techniques for derivation of the probability distribution of extreme responses are not available. However, it has recently been shown that the short-term response of an offshore structure exposed to Morison wave loading can be approximated by the response of an equivalent finite-memory nonlinear system (FMNS). Previous investigation has shown that the developed FMNS models perform better for high Hs values and that their performance for low Hs value is not particularly good. In this paper, MFMNS technique, a modified version of FMNS models is discussed. The improvement in MFMNS model is simply achieved by dividing the structure into two zones (Zones 1 and 2) so that the horizontal distance between the nodes in each zone is relatively small compared to the wavelengths. It is shown that MFMNS technique can be used to determine the short-term probability distribution of the extreme responses accurately with great efficiency.

Copyright © 2016 by ASME

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