Determination of Minimum Number of Specimens in S-N Testing

2002 ◽  
Vol 124 (4) ◽  
pp. 421-427 ◽  
Author(s):  
Prakash Chandra Gope
Keyword(s):  
Distribution Function ◽  
Fatigue Life ◽  
Weibull Distribution ◽  
Functional Relationship ◽  
Minimum Sample Size ◽  
Life Data ◽  
Minimum Number ◽  
Log Normal ◽  
Minimum Number Of Specimens

A method for determination of minimum sample size required to estimate the fatigue life has been presented. No functional relationship between stress and fatigue life other than log normal and Weibull distribution function of fatigue life has been assumed. The method is based on the analysis of the variance of error which arises due to scattered nature of the fatigue life data. An example of the application of the presented method is also given.

Statistical Inferences of Lift-Off Velocity Distribution Function Form for Saltating Particles

2010 ◽  
Vol 108-111 ◽  
pp. 783-788
Author(s):  
Jian Jun Wu ◽  
Li Hong He
Keyword(s):  
Distribution Function ◽  
Weibull Distribution ◽  
Velocity Distribution ◽  
Goodness Of Fit ◽  
Velocity Distribution Function ◽  
Forward Direction ◽  
Distribution Functions ◽  
Velocity Distribution Functions ◽  
Lift Off ◽  
Log Normal

The lift-off velocity distribution of saltating particles, which have been proposed to characterize the dislodgement state of saltating particles, is one of the key issues in the theoretical study of windblown sand transportation. But there were various statistical relations in the early researches. In this paper, the Kolmogorov-Smirnov test for goodness-of-fit is adopted to make an inference of the most probable form of lift-off velocity distribution functions for saltating particles on the basis of the experimental data. The statistical results show that the distribution function of vertical lift-off velocities conforms better to Weibull distribution function than to the normal, log-normal, gamma and exponential ones; while, the distribution function of the absolute values of horizontal lift-off velocities is best described by log-normal distribution in forward direction and Weibull distribution in backward direction, respectively. Finally, two more examples prove to support the above conclusions.

Fatigue Life Modeling Using Nitinol Data

2020 ◽  
Author(s):  
D. Gary Harlow
Keyword(s):  
Distribution Function ◽  
Fatigue Life ◽  
Weibull Distribution ◽  
Parameter Distribution ◽  
Confidence Bounds ◽  
Accelerated Life ◽  
Life Model ◽  
Distribution Parameters ◽  
Physical Attributes ◽  
Accelerated Life Model

Abstract Nitinol is a shape memory alloy that has become very popular for medical applications. Specifically, Nitinol tubing is used as peripheral stents, cardio stents and other medical implant devices. Medical implants subjected to blood pressure experience approximately 40 million cycles per year, which necessitates a life expectancy of at least 600 million cycles. Consequently, modeling the fatigue life of Nitinol is critical because failure could result in severe consequences, if not death. The purpose of this work is to model a rather robust set of fatigue data for Nitinol tubes. A phenomenological distribution function for fatigue life is considered. The form of the distribution function includes a kernel that incorporates time dependent loading, mechanical breakdown, and statistical behavior. The approach is a form of the classical accelerated life model in which covariates are included to account for physical attributes that directly influence lifetime. Because of the generality in the formalism, the model is applicable for a variety of loading conditions. The modeling for the Nitinol data is a combination of traditional stress–life methods with a Weibull distribution function. The proposed approach incorporates stress dependencies in the distribution parameters. Also, the Weibull distribution is assumed to be in the form of a three–parameter distribution rather than the more frequently used two–parameter. To assess the validity of the proposed methodology confidence bounds will be estimated for the data.

scholarly journals Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution

Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 560
Author(s):  
Wenbin Cai ◽  
Wen Li ◽  
Jinze Xu
Keyword(s):  
Fatigue Life ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Fatigue Limit ◽  
High Strength ◽  
Model Based ◽  
Sucker Rod ◽  
Number Of Cycles ◽  
Log Normal ◽  
Log Normal Distribution

During the oil production process, sucker rods are subjected to cyclic alternating load. After a certain number of cycles, a sucker rod can experience fatigue failure. The number of cycles is called fatigue life (N), and the accurate relationship between maximum stress (S) and fatigue life (N) under a certain reliability (P), namely the P-S-N curve, is an important basis for the reliability analysis and fatigue life prediction of sucker rods. The Basquin model, based on log-normal distribution, is widely used for fitting the P-S-N curves of sucker rods. Due to the limitation of this model, it is difficult to extrapolate the conclusion obtained from a finite fatigue region to the high-cycle or ultra-high-cycle fatigue region, which makes it impossible to estimate the fatigue limit of the sucker rod. Compared to the log-normal distribution, Weibull distribution causes the sucker rod to have a minimum safety life, namely the safety life at 100% survival rate, which complies with the fatigue characteristics of the sucker rod and is more in line with the actual situation. In this study, the fatigue data for ultra-high-strength HL and HY grade sucker rods were obtained through experimental fatigue tests. A new fatigue life model was established and the P-S-N curves of two types of ultra-high strength sucker rods were obtained. For HL- and HY-type ultra-high strength sucker rods, the average error between the fitting result and fatigue test value is 1.25% and 4.39%, respectively. Compared to the S-N curve fitting result obtained from the Basquin model commonly used for sucker rods, the new model based on three-parameter Weibull distribution provides better fitting precision and can estimate fatigue limit more accurately, so this model is more suitable for estimating fatigue life and can better guide the design of ultra-high strength sucker rod strings.

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