A Small Sample Prediction Method for Engineering p-S-N Curve

Abstract

Based on Non-intrusive Polynomial Chaos method, a small sample prediction method for engineering p-S-N curve that has a medium fatigue life is proposed. Parameters in Basquin model are calculated through optimization method based on small sample of observed fatigue life. We establish NIPC polynomials and obtain big sample parameters, obtaining probabilistic properties of parameters with the big sample EDF method. Then the relationship between statistics and stress level are fitted with least squares method. Some new samples are introduced to improve the accuracy of the method. The statistics are updated by Bayesian method. Samples parameters under any stress level are obtained to calculate corresponding fatigue life. Probabilistic properties of fatigue life are predicted, and the p-S-N curve is established. Test observations of aluminium alloy T-2024 are all located in the interval of 95% quantile, showing that the method can effectively predict probabilistic properties of medium fatigue life.

Keywords

• non-intrusive polynomial chaos
• small sample
• p-s-n curve
• probabilistic properties
• fatigue life
• least squares method
• bayesian update
• test observation
• aluminium alloy