Application of probabilistic modelling to the lifetime management of nuclear boilers in the creep regime: Part 1

A review of probabilistic structural integrity assessment in the nuclear sector and possible future directions

“…• Ten publications from EASICS Project Team in the public domain. 15,38,[52][53][54][55][56][57][58][59] Note the latter set of publications has been omitted from all data sets and charts except where making a F I G U R E 8 A digital twin framework proposed by Rolls-Royce for structural integrity and lifetime of AMR components. 13 The structural reliability model and underpinning structural degradation models provide the digital twin that is updated with lifecycle data [Colour figure can be viewed at wileyonlinelibrary.com] comparison at a high level between UK and international publications over the last 20 years.…”

Data‐driven prediction of the probability of creep–fatigue crack initiation in 316H stainless steel

“…Stainless steel 316H is frequently used in boiler components, for example in pipes and their bifurcations and supports. [1][2][3][4][5][6] The probability of crack initiation in AGR components with different geometries can be predicted by physically-based numerical simulations and Monte Carlo (MC) analyses, see example by Kroese et al 7 The underlying deterministic approach is used to define the performance function in MC probabilistic assessment. Nevertheless, building MC probabilistic models and its Abbreviations: AGR, advanced gas-cooled reactor; AKNN-MCS, active learning-based K-nearest neighbors-Monte Carlo simulation; BRG, Bayesian ridge regression; CART, classification and regression tree; CoV, Coefficient of Variation; DCNNM, distributed-coordinated neural network metamodel; DCTKS, decomposed collaborative time-variant Kriging surrogate; DCWNNR, distributed collaborative wavelet neural network regression; DT, decision tree regression; EDF, Electricité De France; EN, elastic-net regression; GTB, gradient tree boosting regression; HAZ, heataffected zone; HTBASSs, high temperature behavior of austenitic stainless steels; KNN, K-nearest neighbor regression; LCF, low cycle fatigue; LHS, Latin hypercube sampling; LR, ordinary least squares linear regression; LS, Lasso regression; MAE, mean absolute error; MC, Monte Carlo; MLP, multilayer perceptron regression; PDF, probability density function; R 2 , R-square; RF, random forest regression; RG, ridge regression; RMSE, root mean square error; SGD, stochastic gradient descent regression; SVM, support vector machine; SVR, support vector regression; TIG, tungsten inert gas; WSEF, weld strain enhancement factor.…”

Comprehensive Margin Assessment of the ASME Section III, Division 5, Class A Primary Load Design Rules

“…There are very few methods currently available for high temperature statistical design against creep [2][3][4]. As such, this report describes a novel method developed at Argonne National Laboratory to determine the probability distribution p f given the component geometry, statistics describing the expected component loads, material minimum creep rate data, and material stress rupture data.…”