A Methodology for Fatigue Life Estimation of Linear Vibratory Systems under Non-Gaussian Loads

Tsianika, Vasiliki; Geroulas, Vasileios; Mourelatos, Zissimos P.; Baseski, Igor

doi:10.4271/2017-01-0197

Cited by 6 publications

References 22 publications

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Digital generation of non‐Gaussian random vibration signals in railway transportation and package response analysis

Zhu

2018

Concurrency and Computation

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SummaryThis paper presents two methods for generation artificial acceleration time series focused on the simulation of non‐Gaussian spiky fluctuation features. The random acceleration time series were recorded in railway boxcar; the simulations were performed under the PSD and higher statistical moments (skewness and kurtosis) restrictions. The first method is based on discrete Fourier transform method with EARPG(1) model, which, in nature, is a Fourier representation of time series with special selection of random Fourier phases. The synthetic time series generation are presented for a given target PSD property, skewness, and kurtosis. In the second method, we characterized the stationary and non‐Gaussian process using polynomial chaos expansion and Karhunen‐Loeve expansion approach based on the target PSD, skewness, and kurtosis. The accuracy of the methods are verified by moments and PSD comparison. The package is simplified to be a SDOF system; the acceleration response characteristics of the system are analyzed; the results can be applied in packaging system behaviors evaluation and packaging design optimization.

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Digital generation of non‐Gaussian random vibration signals in railway transportation and package response analysis

Zhu

2018

Concurrency and Computation

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Stationary non-Gaussian random vibration control: A review

Zheng

Chen

2021

Chinese Journal of Aeronautics

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Reliability Analysis of Nonlinear Vibratory Systems Under Non-Gaussian Loads

Geroulas

Mourelatos

Tsianika

et al. 2017

Journal of Mechanical Design

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A general methodology is presented for time-dependent reliability and random vibrations of nonlinear vibratory systems with random parameters excited by non-Gaussian loads. The approach is based on polynomial chaos expansion (PCE), Karhunen–Loeve (KL) expansion, and quasi Monte Carlo (QMC). The latter is used to estimate multidimensional integrals efficiently. The input random processes are first characterized using their first four moments (mean, standard deviation, skewness, and kurtosis coefficients) and a correlation structure in order to generate sample realizations (trajectories). Characterization means the development of a stochastic metamodel. The input random variables and processes are expressed in terms of independent standard normal variables in N dimensions. The N-dimensional input space is space filled with M points. The system differential equations of motion (EOM) are time integrated for each of the M points, and QMC estimates the four moments and correlation structure of the output efficiently. The proposed PCE–KL–QMC approach is then used to characterize the output process. Finally, classical MC simulation estimates the time-dependent probability of failure using the developed stochastic metamodel of the output process. The proposed methodology is demonstrated with a Duffing oscillator example under non-Gaussian load.

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A Methodology for Fatigue Life Estimation of Linear Vibratory Systems under Non-Gaussian Loads

Cited by 6 publications

References 22 publications

Digital generation of non‐Gaussian random vibration signals in railway transportation and package response analysis

Digital generation of non‐Gaussian random vibration signals in railway transportation and package response analysis

Stationary non-Gaussian random vibration control: A review

Reliability Analysis of Nonlinear Vibratory Systems Under Non-Gaussian Loads

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