An Approach for a Statistical Evaluation of Uncertainty in Assessing Fatigue Usage Including Environmental Effects

Abstract

The assessment for adequacy in managing the effects of fatigue in the ASME Code Class-1 (pressure boundary) components is based on a calculated measure of the projected fatigue damage. This measure is the highest cumulative usage factor (CUF) in a given component under a specified set of cyclic loadings and their expected number of repetitions. The Code based calculation of CUF and its adjustments for potential environmentally assisted fatigue (EAF) damage accumulation utilize a multitude of inputs, and conservative assumptions and applied margins. To support the extended service life beyond the original design, or longer life of new designs, changes in inputs and/or conservative assumptions used in these deterministically calculated CUFs are often made to meet a deterministic performance criterion. This makes the impact of uncertainty in the inputs and/or changes in the conservative adjustments difficult to assess. This paper presents a generic, engineering approach for estimation of the uncertainty distribution of CUF based on the expected statistical characteristics of input variables used in the calculation of EAF-based CUF. The approach does not involve Monte Carlo sampling. The proposed statistical approach analytically combines variances of the inputs leading to an acceptable estimation of the total variance of the CUF. The approach does not require specification of full probability distribution(s) for the input variables, nor is the dependence between variables a critical issue from the analytical point of view. Feasibility and limitations of the approach are discussed in relation to the NB-3200 and NB-3600 procedures of the ASME Code and the current Fen-based augmentation for environmental effects. This approach is further examined in the framework of stress–strength interference methodology to account for the uncertainty in the fatigue performance criterion which can lead to a rational deterministic safety factor interpretation and its relation to a quantifiable measure of the probability of exceeding the fatigue performance criterion.