Dynamic reliability analysis of a floating offshore wind turbine under wind-wave joint excitations via probability density evolution method

The probability density evolution method (PDEM) has been used to research the stochastic earthquake response and reliability analysis of large-scale aqueducts structures, with the changeable water level in tub and under the condition of the interaction between water and boundary of the tub. The results show that，the probability distribution of stochastic seismic responses about aqueduct structure is rules distribution, the response and failure probability of large-scale aqueduct structures under the stochastic earthquake are increased as the rising of water level, however, with the water level unchangeably, the seismic response is distinctness smaller while the seismic reliability much higher after considering the water sloshing effect on the aqueduct structures. Within the calculation of seismic resistance about the large aqueduct structures, the impact should be considered including the changeable water level and water sloshing.

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Time-variant seismic reliability analysis of slopes with combined uncertainties based on probability density evolution method

10.1109/ichceswidr54323.2021.9656212 ◽

2021 ◽

Author(s):

Hongqiang Hu ◽

Gang Gan

Keyword(s):

Probability Density ◽

Reliability Analysis ◽

Density Evolution ◽

Seismic Reliability ◽

Probability Density Evolution Method ◽

Probability Density Evolution

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Structural optimization considering dynamic reliability constraints via probability density evolution method and change of probability measure

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-020-02621-4 ◽

2020 ◽

Vol 62 (5) ◽

pp. 2499-2516

Author(s):

Jianbing Chen ◽

Jiashu Yang ◽

Hector Jensen

Keyword(s):

Probability Measure ◽

Structural Optimization ◽

Probability Density ◽

Density Evolution ◽

Dynamic Reliability ◽

Probability Density Evolution Method ◽

Probability Density Evolution ◽

Change Of Probability Measure

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Failure Analysis of Wind Turbines by Probability Density Evolution Method

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.569-570.579 ◽

2013 ◽

Vol 569-570 ◽

pp. 579-586 ◽

Cited By ~ 1

Author(s):

M.T. Sichani ◽

S.R.K. Nielsen ◽

W.F. Liu ◽

J.B. Chen ◽

J. Li ◽

...

Keyword(s):

Wind Turbine ◽

Probability Density ◽

Failure Probability ◽

Extreme Values ◽

Practical Interest ◽

Extreme Value ◽

Density Evolution ◽

Response Process ◽

Probability Density Evolution Method ◽

Probability Density Evolution

The aim of this study is to present an efficient and accurate method for estimation of the failure probability of wind turbine structures which work under turbulent wind load. The classical method for this is to fit one of the extreme value probability distribution functions to extracted maxima of the response of wind turbine. However this approach may contain high amount of uncertainty due to arbitrariness of the data and the distributions chosen. Therefore less uncertain methods are meaningful in this direction. The most natural approach in this respect is the Monte Carlo (MC) simulation. This however has no practical interest due to its excessive computational load. This problem can alternatively be tackled if the evolution of the probability density function (PDF) of the response process can be realized. The evolutionary PDF can then be integrated on the boundaries of the problem, i.e. the exceedance threshold of the response, which results in the accurate values of the failure probability. For this reason we propose to use the probability density evolution method (PDEM). PDEM can alternatively be used to obtain distribution of the extreme values of the response process by simulation. This approach requires less computational effort than integrating the evolution of the PDF; but may have less accuracy. In this paper we present the results of failure probability estimation by the PDEM. The results will then be compared to the extrapolated values from the extreme value distribution fits to the samples response values.

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