Application of system identification techniques in efficient modelling of offshore structural response. Part II: Model validation

Najafian, G.

doi:10.1016/j.apor.2007.08.003

Cited by 14 publications

(7 citation statements)

References 3 publications

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“…This is particularly the case when the cylinder surfaces are also assumed to be rough (i.e. higher C d and lower C m values), which could lead to kurtosis values as high as 7 even for dynamic cases [38]. Therefore, the application of alternative methods of moments could be beneficial for these high-kurtosis cases.…”

Section: Discussion and Further Workmentioning

confidence: 97%

Comparison of three different methods of moments for derivation of probability distribution parameters

Najafian

2010

Applied Ocean Research

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Section: Discussion and Further Workmentioning

confidence: 97%

Comparison of three different methods of moments for derivation of probability distribution parameters

Najafian

2010

Applied Ocean Research

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“…The major obstacle in establishing the probabilistic properties of the response due to wave and current loading is the nonlinearity of the drag component of Morison's wave loading (Morison et al 1950) and/or the structural system which leads to a non-Gaussian distribution for response (Borgman 1967;Tickell 1977;Burrows 1979;Eatock and Rajagopalan 1981;Najafian 2007bNajafian , 2007c. The problem is further compounded by current and by intermittent loading on members in the splash zone, which have a significant ef- * Corresponding author.…”

Section: Introductionmentioning

confidence: 97%

Short-term probability distribution of the extreme values of offshore structural response by an efficient time simulation technique

Husain

Zaki

Mallahzadeh

et al. 2014

Ships and Offshore Structures

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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 required for their safe and economical design. Wave loading on slender members of bottom-supported jacket structures is frequently calculated by Morison's equation. Due to nonlinearity of the drag component of Morison 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 their extreme response probability distributions are not available. To this end, the conventional time simulation technique (CTS) is frequently used for predicting the probability distribution of the extreme values of response. However, this technique suffers from excessive sampling variability and hence a large number of simulated response extreme values (hundreds of simulated response records) are required to reduce the sampling variability to acceptable levels. In this paper, a more efficient version of the time simulation technique (ETS) is introduced to derive the probability distribution of response extreme values from a much smaller sample of simulated extreme values. The ETS procedure is found to be many times more efficient than the CTS method.

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“…The following is meant to briefly explain how a finite-memory nonlinear system can be used to establish a simple relationship between the output and input of a jacket structure. For a more complete description refer to [31,32]. The structure of Finite-memory nonlinear system 1.…”

Section: Finite-memory Nonlinear Systemsmentioning

confidence: 99%

“…The responses chosen for investigation were the base shear and overturning moment of the test structure. For a more complete description of the test structure refer to [32]. The foregoing test structures were subjected to various unidirectional sea-states simulated from Pierson-Moskowitz (P-M) frequency spectrum.…”

Section: Test Structure and Responsesmentioning

confidence: 99%

“…A new technique has recently been introduced [31][32][33] by modeling the offshore structural system exposed to Morison wave loading by an equivalent finite-memory nonlinear system (FMNS). Time simulation technique can then be applied to the equivalent FMNS models to determine the probability distribution of response extreme values with great efficiency (about 35 times more efficient than the conventional time simulation procedure).…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Distribution of the Extreme Values of Offshore Structural Response by Modified Finite-Memory Nonlinear System Modeling

Zaki

Husain

Wang

et al. 2013

Volume 2A: Structures, Safety and Reliability

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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 nonGaussian; 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 shows that the developed FMNS models perform better for high H s values and their performance for low H s value is not particularly good. In this paper, the modified version of FMNS models is referred to as MFMNS models is discussed. The improvement of MFMNS model is simply 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 wavelength. The modified version of MFMNS is used to determine the short-term probability distribution of the response extreme values with great efficiency.

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Application of system identification techniques in efficient modelling of offshore structural response. Part II: Model validation

Cited by 14 publications

References 3 publications

Comparison of three different methods of moments for derivation of probability distribution parameters

Comparison of three different methods of moments for derivation of probability distribution parameters

Short-term probability distribution of the extreme values of offshore structural response by an efficient time simulation technique

Short-Term Distribution of the Extreme Values of Offshore Structural Response by Modified Finite-Memory Nonlinear System Modeling

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