Mahdi Norouzi

EFFICIENT METHOD FOR RELIABILITY ASSESSMENT UNDER HIGH-CYCLE FATIGUE

Fatigue causes about 90% of service failures in machines. Fatigue analysis involves significant randomness in the loads, material properties and geometry. Designers often use Monte Carlo simulation to estimate fatigue reliability under dynamic, random loads such as those due to ocean waves. Monte Carlo simulation is computationally expensive because it requires calculation of the stresses for thousands of simulated time histories of the loads. This paper presents and demonstrates a method to estimate efficiently the fatigue life of a structure subjected to a dynamic load, which is represented by a stationary, Gaussian random process, for many different spectra of the excitation. The method requires only one Monte Carlo simulation for one power spectral density function of the excitation.

Efficient Estimation of First Excursion Failure of Dynamic Systems by Probabilistic Re-analysis

In design of real-life systems, such as an offshore wind turbine, there are significant uncertainties in the excitation. Therefore, it is necessary to evaluate the reliability of a system for different probability distributions of the input variables that are consistent with the available evidence. This is usually accomplished by Monte Carlo simulation, which is computationally expensive or even impractical for large-scale systems. This paper presents a methodology to assess efficiently the probability of first excursion failure of structures under random, dynamic loads, which are represented by stochastic processes, for different power spectra. We achieve that by reweighting the results calculated in one simulation. We demonstrate the efficacy of the proposed method on two examples. The first involves a linear, one degree of freedom beam under random, dynamic loads. The second example involves an offshore wind turbine under dynamic wind and wave loads. The probability of failure for loads generated by a sampling spectrum is calculated. Then, the probability of failure for different spectra is estimated by using re-analysis. We compare the results with those from Monte Carlo simulation to validate the method and demonstrate its efficiency.