Stress corrosion cracking (SCC) is an important degradation mechanism to be considered for failure assessment of nuclear piping components made of austenitic steels. In this paper, an attempt has been made to compute the failure probabilities of a piping component against SCC with time using Monte Carlo simulation (MCS) technique. The initiation and propagation stages of stress corrosion cracks are modelled using the general methodology recommended in PRAISE modified by using the recommendations given by ASM for more rational modelling of stress field around cracks for estimating their growth with time. Degree of sensitization, applied stress, time to initiation of SCC, initial crack length, and initiation crack growth velocity are considered as random variables. An attempt has been made to study the stochastic propagation of stress corrosion cracks with time, using MCS technique. The trend of the distribution of crack depths at the initial stages obtained from simulation are compared and is found to be in satisfactory agreement with the relevant experimental observations reported in the literature. The failure probabilities are computed using two different failure criteria, namely (a) based on net-section stress and detectable leak rate as recommended in PRAISE and (b) based on R6 approach (using R6-option 1 curve as the failure assessment diagram). The procedure presented in the paper is general and the usefulness of the same is demonstrated through an example problem.
Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.
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