This work investigates the variance of fatigue damage in stationary random loadings with non-Gaussian probability distribution and narrow-band power spectral density. It presents an approach exploiting a non-linear time-invariant transformation that links Gaussian and non-Gaussian domains and that is calibrated on the skewness and kurtosis values of the non-Gaussian process. The transformation allows determining the joint probability distribution of two peaks and the cycle amplitude distribution in the non-Gaussian process, from which the variance of damage is calculated. Monte Carlo numerical simulations are finally discussed to demonstrate the correctness of the proposed model and to investigate the sensitivity of the damage variance to several parameters (S-N inverse slope, skewness and kurtosis of non-Gaussian loading).
More on variance of fatigue damage in non-Gaussian random loadings - Effect of skewness and kurtosis
Benasciutti D.
2020-01-01
Abstract
This work investigates the variance of fatigue damage in stationary random loadings with non-Gaussian probability distribution and narrow-band power spectral density. It presents an approach exploiting a non-linear time-invariant transformation that links Gaussian and non-Gaussian domains and that is calibrated on the skewness and kurtosis values of the non-Gaussian process. The transformation allows determining the joint probability distribution of two peaks and the cycle amplitude distribution in the non-Gaussian process, from which the variance of damage is calculated. Monte Carlo numerical simulations are finally discussed to demonstrate the correctness of the proposed model and to investigate the sensitivity of the damage variance to several parameters (S-N inverse slope, skewness and kurtosis of non-Gaussian loading).File | Dimensione | Formato | |
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2020_MARQUES BENA_More on the variance non-Gaussian random loadings_ProStrInt_2020.pdf
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