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. This paper investigates the suitability of the Gumbel, the Generalized Extreme Value (GEV), and the Generalized Pareto (GP) distributions for modelling of extreme responses by comparing them with empirical distributions derived from extensive Monte Carlo time simulations. It will be shown that none of these distributions can model the extreme values adequately but that a mixed distribution consisting of both GEV and GP distributions seems to be capable of modelling the extreme responses with very good accuracy.
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. 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. To this end, the conventional Monte Carlo time simulation technique is frequently used for predicting the probability distribution of the extreme responses. However, this technique suffers from excessive sampling variability and hence a large number of simulated response records are required to reduce the sampling variability to acceptable levels. This paper takes advantage of the correlation between extreme responses and their corresponding extreme surface elevations to derive the probability distribution of the extreme responses accurately and efficiently, i.e. without the need for extensive simulations.
Accurate estimation of the 100-year responses (derived from the long-term distribution of extreme responses) is required for the safe and economical design of offshore structures. However, 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. To this end, conventional Monte Carlo time simulation technique could be used for predicting the long-term probability distribution of the extreme responses. However, this technique suffers from excessive sampling variability and hence a large number of simulated response records are required to reduce the sampling variability to acceptable levels. This paper takes advantage of the correlation between extreme responses and their corresponding extreme surface elevations to derive the values of the 100-year responses without the need for extensive simulations. It is demonstrated that the technique could be used for both quasi-static and dynamic responses.
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.
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. Monte Carlo time simulation technique can be used to derive the probabilistic properties of offshore structural response, but the procedure is computationally demanding. Finite-memory nonlinear system (FMNS) modeling of the response of an offshore structure exposed to Morison’s wave loading has been used to reduce the computational effort, but the predictions are not always of high accuracy. In this paper, further development of this technique, which leads to more accurate estimates of the probability distribution of the extreme responses, is reported.
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