On the temperature independence of statistical model parameters for cleavage fracture in ferritic steels

Nonlinear fatigue damage accumulation and life prediction of metals: A comparative study

“…In general, fatigue life prediction of engineering components in practice needs to account for the effects of load interaction, load sequence, and/or memory effects . It is worth pointing out that there are still no clear and widely accepted explanations that can be used for fatigue design or assessment under complex loading events . In this section, five recent fatigue damage models for addressing the load interaction effect are briefly reviewed.…”

“…[22][23][24] It is worth pointing out that there are still no clear and widely accepted explanations that can be used for fatigue design or assessment under complex loading events. [25][26][27] In this section, five recent fatigue damage models for addressing the load interaction effect are briefly reviewed.…”

Structural reliability analysis and uncertainties‐based collaborative design and optimization of turbine blades using surrogate model

“…To improve the performance of blades, it is necessary to analyze the coupling mechanism between shockwave and boundary layer . Many valuable studies have been proposed to perform this analysis by means of numerical simulations combining with physics of failure modeling . Among them, Wang et al utilized a reliability index strategy and proposed an uncertainty‐based optimization model for the structure design problem with bounded uncertain information.…”

“…9 Many valuable studies have been proposed to perform Nomenclature: X R , continuous random variable; M XR ξ ð Þ, the moment generating function of X R ; K XR ξ ð Þ, the cumulant generating function of X R ; f XR x R ð Þ, the probability density function of X R ; e ξ, the saddlepoint; ϕ(•), the standard normal probability density function of X R ; Φ(•), the standard normal cumulant generating function of X R ; X, the vector of local design variables; X S , the vector of shared design variables; Y ji , the vector of coupling variables which are the output of discipline j; Y ij , the vector of coupling variables which are the output of discipline i; J, the compatibility constraint; Pr(•), the reliability constraints this analysis by means of numerical simulations combining with physics of failure modeling. [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Among them, Wang et al 10 utilized a reliability index strategy and proposed an uncertainty-based optimization model for the structure design problem with bounded uncertain information. Correia et al 11 proposed a generalization probabilistic fatigue model which can include multiaxial loading conditions and mean-stress effects.…”

Storage reliability assessment for the stored equipment under periodical inspection

“…Also, some storage reliability models are studied on basis of conducting storage reliability tests which need rich sample and sufficient data. 10,11 As the research continues, new methods and theories are introduced into storage reliability assessment, such as the neural network, 12 Bayesian statistics, [13][14][15] fuzzy theory, 16 interval analysis, 17 and multi-information fusion analysis theory 18 are introduced, and these methods are verified much more suitable for the storage and small-sample systems under multi-source stresses. However, the aforementioned methods are a little more complex and their models contain many unknown parameters which are difficult to be accepted by engineering applications.…”