Fatigue behavior of unidirectional glass fiber reinforced polyester (GFRP) composites at room temperature under in-phase combined torsion/bending loading was investigated. All fatigue tests were carried out on constant-deflection fatigue machine with frequency of 25 Hz. A 30% reduction from the initial applied moments was taken as a failure criterion in the combined torsion/bending fatigue tests of the composite materials. A series of pure torsional fatigue tests were conducted to construct the failure contour of GFRP composites using different failure theories. The obtained S–N curves from combined torsion/bending tests were compared with both, pure torsion fatigue test results and published results of pure bending fatigue tests of GFRP rods. Pictures by scanning electron microscope were used to closely examine the failure mode of the tested specimens under combined torsion/bending loading.
The results showed that, the unidirectional glass fiber reinforced polyester composites have poor torsional fatigue strength compared with the published results of pure bending fatigue strength. Endurance limit value (calculated from S–N equation at N = 107 cycles) of GFRP specimens tested under combined torsion/bending loading equals 8.5 times the endurance limit of pure torsion fatigue. On the other hand the endurance limit of combined torsion/bending fatigue strength approximately half the fatigue limit of pure bending fatigue strength. The predicted values of combined torsion/bending fatigue strength at different number of cycles, using the published failure theory are in good agreement with the experimental data. For the investigated range of fiber volume fractions (Vf) it was found that higher stress levels are needed to produce fatigue failure after the same number of cycles as Vf increases. 相似文献
The paper is devoted to the identification of stochastic loads applied to a non-linear dynamical system for which experimental
dynamical responses are available. The identification of the stochastic load is performed using a simplified computational
non-linear dynamical model containing both model uncertainties and data uncertainties. Uncertainties are taken into account
in the context of the probability theory. The stochastic load which has to be identified is modelled by a stationary non-Gaussian
stochastic process for which the matrix-valued spectral density function is uncertain and is then modelled by a matrix-valued
random function. The parameters to be identified are the mean value of the random matrix-valued spectral density function
and its dispersion parameter. The identification problem is formulated as two optimization problems using the computational
stochastic model and experimental responses. A validation of the theory proposed is presented in the context of tubes bundles
in Pressurized Water Reactors. 相似文献