Numerical simulation of a random sea: a common error and its effect upon wave group statistics

Tucker, M. J.; Challenor, Peter; Carter, D.J.T.

doi:10.1016/0141-1187(84)90050-6

Cited by 160 publications

(89 citation statements)

References 6 publications

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“…Eq. (16) implies that the relationship between the inertia-induced component of the quasi-static response and nodal water particle accelerations is not linear. The reason for nonlinearity is that in calculating the nonlinear quasi-static response, the effect of load intermittency on members in the splash zone has also been included.…”

Section: Simulation Of Linear Quasi-static Re-sponsementioning

confidence: 99%

“…The theoretical probability distribution of the linear quasi-static response extreme values during period T (based on the assumption that the linear response is a Gaussian random process) is derived from the following relationship [16]. (21) where denotes the linear response extreme value, M = T/T z is the expected number of zero-upcrossings of the linear response during period T and is the standard deviation of linear response.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Rank all the simulated extreme values from smallest to largest. Then use the following plotting position equation for the Gumbel distribution [16], to estimate the value of the probability distribution for each of the ranked extreme values. (13) where r max denotes the response extreme value, q n is the nth smallest simulated extreme value, and finally N is the total number of simulated extreme values.…”

Section: Short-term Probability Distribution Of Response Extreme Valuesmentioning

confidence: 99%

“…Parameters g i and h i are two independent and standardized Gaussian random variables. The NSA technique is more robust and hence was used in this study [15,16].…”

Section: Evaluation Of Response Record By Con-ventional Time Simulatimentioning

confidence: 99%

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Derivation of the Probability Distribution of Extreme Values of Offshore Structural Response by Efficient Time Simulation Method

Husain¹,

Zaki²,

Najafian

2013

TOCIEJ

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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. To this end, the conventional 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. A more efficient method (ETS) was recently introduced which takes advantage of the correlation between the extreme values of surface elevation and their corresponding response extreme values. The method has proved to be very efficient for high-intensity sea states; however, the correlation and hence the efficiency and accuracy of the technique reduces for sea states of lower intensity. In this paper, a more efficient version of the ETS technique is introduced which takes advantage of the correlation between the extreme values of the nonlinear response and their corresponding linear response values.

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Section: Simulation Of Linear Quasi-static Re-sponsementioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Section: Short-term Probability Distribution Of Response Extreme Valuesmentioning

confidence: 99%

“…Parameters g i and h i are two independent and standardized Gaussian random variables. The NSA technique is more robust and hence was used in this study [15,16].…”

Section: Evaluation Of Response Record By Con-ventional Time Simulatimentioning

confidence: 99%

See 2 more Smart Citations

Derivation of the Probability Distribution of Extreme Values of Offshore Structural Response by Efficient Time Simulation Method

Husain¹,

Zaki²,

Najafian

2013

TOCIEJ

View full text Add to dashboard Cite

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“…Modal amplitudes are drawn from a Rayleigh distribution with variance derived from the observed (or theoretical) density spectrum, and random phases are added (see Tucker et al, 1984).…”

Section: Deterministic Models: Time-domain and Spectral-domainmentioning

confidence: 99%

Wave modelling – The state of the art

Cavaleri¹,

Alves²,

Ardhuin³

et al. 2007

Progress in Oceanography

454

177

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This paper is the product of the wave modelling community and it tries to make a picture of the present situation in this branch of science, exploring the previous and the most recent results and looking ahead towards the solution of the problems we presently face. Both theory and applications are considered.The many faces of the subject imply separate discussions. This is reflected into the single sections, seven of them, each dealing with a specific topic, the whole providing a broad and solid overview of the present state of the art. After an introduction framing the problem and the approach we followed, we deal in sequence with the following subjects: (Section) 2, generation by wind; 3, non-linear interactions in deep water; 4, white-capping dissipation; 5, non-linear interactions in shallow water; 6, dissipation at the sea bottom; 7, wave propagation; 8, numerics. The two final sections, 9 and 10, summarize the present situation from a general point of view and try to look at the future developments. Keywords

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Selected References and Bibiliography

Sundar

2015

Ocean Wave Mechanics

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Numerical simulation of a random sea: a common error and its effect upon wave group statistics

Cited by 160 publications

References 6 publications

Derivation of the Probability Distribution of Extreme Values of Offshore Structural Response by Efficient Time Simulation Method

Derivation of the Probability Distribution of Extreme Values of Offshore Structural Response by Efficient Time Simulation Method

Wave modelling – The state of the art

Selected References and Bibiliography

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