Prediction of Fracture Failure of Steel Pipes With Sharp Corrosion Pits Using Time-Dependent Reliability Method With Lognormal Process

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Guoyang Fu ◽  
Wei Yang ◽  
Wenni Deng ◽  
Chun-Qing Li ◽  
Sujeeva Setunge
Keyword(s):  
Fracture Toughness ◽  
Limit State ◽  
Probability Of Failure ◽  
Considerable Effect ◽  
Time Dependent ◽  
Pipe Failure ◽  
Load Effect ◽  
Steel Pipes ◽  
Reliability Method ◽  
Corrosion Pits

This paper presents a reliability-based methodology for assessing fracture failures of steel pipes with sharp corrosion pits. Based on newly developed models of elastic fracture toughness, the simple criterion of stress intensity factor (SIF) is used to establish the limit state functions for pipes with sharp corrosion pits in the longitudinal and circumferential directions. A stochastic model of load effect is developed and a time-dependent reliability method based on first passage probability for nonstationary lognormal processes is employed to quantify the probability of failure and predict the remaining service life. After applying the methodology to a case study, sensitivity analysis is carried out to identify the most influential variables on the probability of failure. It is found in the paper that the correlation coefficient has a considerable effect on probability of failure of steel pipes with sharp corrosion pits and that the larger the mode I fracture toughness is, the smaller the probability of pipe failure is.

Advanced Probabilistic Fracture Mechanics Using the R6 Procedure

2010 ◽  
Author(s):  
David W. Beardsmore ◽  
Karen Stone ◽  
Huaguo Teng
Keyword(s):  
Fracture Toughness ◽  
Monte Carlo ◽  
Fracture Mechanics ◽  
Monte Carlo Methods ◽  
Probability Of Failure ◽  
Defect Size ◽  
Probabilistic Fracture Mechanics ◽  
Safety Margins ◽  
Reliability Method ◽  
Failure Assessment

Deterministic Fracture Mechanics (DFM) assessments of structural components (e.g. pressure vessels and piping used in the nuclear industry) containing defects can usually be carried out using the R6 procedure. The aim of such an assessment is to demonstrate that there are sufficient safety margins on the applied loads, defect size and fracture toughness for the safe continual operation of the component. To ensure a conservative assessment is made, a lower-bound fracture toughness, and upper-bound defect sizes and applied loads are used. In some cases, this approach will be too conservative and will provide insufficient safety margins. Probabilistic Fracture Mechanics (PFM) allow a way forward in such cases by allowing for the inherent scatter in material properties, defect size and applied loads explicitly. Basic Monte Carlo Methods (MCM) allow an estimate of the probability of failure to be calculated by carrying out a large number of fracture mechanics assessments, each using a random sample of the different random variables (loads, defect size, fracture toughness etc). The probability of failure is obtained by counting the proportion of simulations which lead to assessment points that lie outside the R6 failure assessment curve. This approach can give good results for probabilities greater than 10−5. However, for smaller probabilities, the calculation may be inefficient and a very large number of assessments may be necessary to obtain an accurate result, which may be prohibitive. Engineering Reliability Methods (ERM), such as the First Order Reliability method (FORM) and the Second Order Reliability Method (SORM), can be used to estimate the probability of failure in such cases, but these methods can be difficult to implement, do not always give the correct result, and are not always robust enough for general use. Advanced Monte Carlo Methods (AMCM) combine the two approaches to provide an accurate and efficient calculation of probability of failure in all cases. These methods aim to carry out Importance Sampling so that only assessment points that lie close to or outside the failure assessment curve are calculated. Two methods are described in this paper: (1) orthogonal sampling, and (2) spherical sampling. The power behind these methods is demonstrated by carrying out calculations of probability of failure for semi-elliptical, surface breaking, circumferential cracks in the inside of a pressure vessel. The results are compared with the results of Basic Monte Carlo and Engineering Reliability calculations. The calculations use the R6 assessment procedure.

In-Plane Creep Stability Design of Concrete Filled Steel Tubular Arches Using Inverse Reliability Method

2013 ◽  
Vol 351-352 ◽  
pp. 1601-1604
Author(s):  
Wei Jiang ◽  
Da Gang Lu
Keyword(s):  
Limit State ◽  
Time Dependent ◽  
Safety Factors ◽  
State Equations ◽  
Stability Range ◽  
Good Efficiency ◽  
Reliability Indices ◽  
Inverse Form ◽  
Dependent Behavior ◽  
Reliability Method

An inverse first order reliability method (FORM) is presented to solve the safety factors for the in-plane creep stability of concrete filled steel tubular (CFST) arches. In the inverse analysis, the safety factors with or without considering the time-dependent behavior of concrete are introduced into limit state equations for the in-plane stability design of CFST arches. For different target reliability indices and steel ratios, the time-independent and time-dependent safety factors are solved. The results show that the inverse FORM is of good efficiency and applicability. The target reliability indices have little effect on the safety factors for the creep stability of CFST arches. The effects of steel ratios are significant which should be considered in design. For the commonly used steel ratios of CFST arches, the in-plane safety factors for creep stability range from 1.17 to 1.43.

Reliability-Based Topology Optimization Using Mean-Value Second-Order Saddlepoint Approximation

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Dimitrios I. Papadimitriou ◽  
Zissimos P. Mourelatos
Keyword(s):  
Topology Optimization ◽  
Limit State ◽  
Random Variables ◽  
Saddlepoint Approximation ◽  
Probability Of Failure ◽  
Mean Value ◽  
Second Order ◽  
State Function ◽  
Sensitivity Derivatives ◽  
Reliability Method

A reliability-based topology optimization (RBTO) approach is presented using a new mean-value second-order saddlepoint approximation (MVSOSA) method to calculate the probability of failure. The topology optimizer uses a discrete adjoint formulation. MVSOSA is based on a second-order Taylor expansion of the limit state function at the mean values of the random variables. The first- and second-order sensitivity derivatives of the limit state cumulant generating function (CGF), with respect to the random variables in MVSOSA, are computed using direct-differentiation of the structural equations. Third-order sensitivity derivatives, including the sensitivities of the saddlepoint, are calculated using the adjoint approach. The accuracy of the proposed MVSOSA reliability method is demonstrated using a nonlinear mathematical example. Comparison with Monte Carlo simulation (MCS) shows that MVSOSA is more accurate than mean-value first-order saddlepoint approximation (MVFOSA) and more accurate than mean-value second-order second-moment (MVSOSM) method. Finally, the proposed RBTO-MVSOSA method for minimizing a compliance-based probability of failure is demonstrated using two two-dimensional beam structures under random loading. The density-based topology optimization based on the solid isotropic material with penalization (SIMP) method is utilized.

Time-Dependent System Reliability Analysis Using Random Field Discretization

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Zhen Hu ◽  
Sankaran Mahadevan
Keyword(s):  
Random Field ◽  
Reliability Analysis ◽  
System Reliability ◽  
Probability Of Failure ◽  
Two Dimensions ◽  
Time Dependent ◽  
Time Interval ◽  
System Reliability Analysis ◽  
Reliability Method ◽  
Time Dependent System

This paper proposes a novel and efficient methodology for time-dependent system reliability analysis of systems with multiple limit-state functions of random variables, stochastic processes, and time. Since there are correlations and variations between components and over time, the overall system is formulated as a random field with two dimensions: component index and time. To overcome the difficulties in modeling the two-dimensional random field, an equivalent Gaussian random field is constructed based on the probability equivalency between the two random fields. The first-order reliability method (FORM) is employed to obtain important features of the equivalent random field. By generating samples from the equivalent random field, the time-dependent system reliability is estimated from Boolean functions defined according to the system topology. Using one system reliability analysis, the proposed method can get not only the entire time-dependent system probability of failure curve up to a time interval of interest but also two other important outputs, namely, the time-dependent probability of failure of individual components and dominant failure sequences. Three examples featuring series, parallel, and combined systems are used to demonstrate the effectiveness of the proposed method.

Application of Partial Safety Factors for Fitness-for-Service Assessment of Pressure Equipment With Local Metal Loss

2017 ◽  
Author(s):  
Takuyo Kaida ◽  
Shinsuke Sakai
Keyword(s):  
Reliability Analysis ◽  
Limit State ◽  
Probability Of Failure ◽  
Pressure Vessels ◽  
Metal Loss ◽  
State Function ◽  
Safety Factors ◽  
Loss Assessment ◽  
Reliability Method ◽  
Partial Safety Factors

Reliability analysis considering data uncertainties can be used to make a rational decision as to whether to run or repair a pressure equipment that contains a flaw. Especially, partial safety factors (PSF) method is one of the most useful reliability analysis procedure and considered in a Level 3 assessment of a crack-like flaw in API 579-1/ASME FFS-1:2016. High Pressure Institute of Japan (HPI) formed a committee to develop a HPI FFS standard including PSF method. To apply the PSF method effectively, the safety factors for each dominant variable should be prepared before the assessment. In this paper, PSF for metal loss assessment of typical pressure vessels are derived based on first order reliability method (FORM). First, a limit state function and stochastic properties of random variables are defined. The properties of a typical pressure vessel are based on actual data of towers in petroleum and petrochemical plants. Second, probability of failure in several cases are studied by Hasofer-Lind method. Finally, PSF’s in each target probability of failure are proposed. HPI published a new technical report, HPIS Z 109 TR:2016, that provide metal loss assessment procedures based on FORM and the proposed PSF’s described in this paper.

Reliability Analysis of Pipelines During Laying, Considering Ultimate Strength Under Combined Loads

1999 ◽  
Vol 122 (1) ◽  
pp. 40-46 ◽  
Author(s):  
Ragnar T. Igland ◽  
Torgeir Moan
Keyword(s):  
Structural Reliability ◽  
Limit State ◽  
Wave Spectrum ◽  
Probability Of Failure ◽  
Ultimate Limit State ◽  
Minor Importance ◽  
Target Level ◽  
Peak Period ◽  
System Effect ◽  
Load Effect

Structural reliability methods are applied to establish a measure of safety for pipelines during laying, and especially to calibrate semi-probabilistic ultimate limit state criteria based on measures of uncertainty, method of reliability, and a given target level. Ultimate collapse of thick tubes under combined external pressure, tension, and bending loads are studied applying the finite element method. Nonlinear effects of large deformations, effects of initial ovality, residual stresses, strain-hardening, yield anisotropy, and loading paths were accounted for in the analysis. A set of interaction equations is proposed. Load effects in the pipelines during installation by the S-lay method are studied. The effects of uncertainties in yield stress, mass, stiffness of the stinger, response amplitude operator and peak period for the wave spectrum were accounted for in the analysis. The major factors affecting strain concentration due to concrete coating are taken into account. A combination of design point calculation and importance sampling procedure is used to calculate the probability of failure. The study includes calibration of partial safety factors for the design format selected. The most important random variable is the model uncertainty for bending capacity, while the uncertainty of the load effect has minor importance for the probability of failure. The system effect is taken into account considering the correlation along the pipeline. The probability of failure is referred both to the total laying period as well as a 3-h period demonstrating that the target level needs to be defined in view of the reference time period. [S0892-7219(00)01501-6]

Time-Dependent Reliability Analysis by a Sampling Approach to Extreme Values of Stochastic Processes

2012 ◽  
Author(s):  
Zhen Hu ◽  
Xiaoping Du
Keyword(s):  
Stochastic Processes ◽  
Reliability Analysis ◽  
Extreme Values ◽  
Limit State ◽  
Time Dependent ◽  
State Function ◽  
Limit State Function ◽  
Reliability Method ◽  
Time Invariant ◽  
State Functions

Maintaining high accuracy and efficiency is a challenging issue in time-dependent reliability analysis. In this work, an accurate and efficient method is proposed for limit-state functions with the following features: The limit-state function is implicit with respect to time, and its input contains stochastic processes; the stochastic processes include only general strength and stress variables, or the limit-state function is monotonic to these stochastic processes. The new method employs random sampling approaches to estimate the distributions of the extreme values of the stochastic processes. The extreme values are then used to replace the corresponding stochastic processes, and consequently the time-dependent reliability analysis is converted into its time-invariant counterpart. The commonly used time-invariant reliability method, the First Order Reliability Method, is then applied for the time-variant reliability analysis. The results show that the proposed method significantly improves the accuracy and efficiency of time-dependent reliability analysis.

Time-Dependent System Reliability Analysis With Second-Order Reliability Method

2020 ◽  
Vol 143 (3) ◽  
Author(s):  
Hao Wu ◽  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failures. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method using the envelope method and second-order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the second-order component reliability method with an improve envelope approach, which produces a component reliability index. The covariance between component responses is estimated with the first-order approximations, which are available from the second-order approximations of the component reliability analysis. Then, the joint distribution of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

Time-Dependent System Reliability Analysis With Second Order Reliability Method

2020 ◽  
Author(s):  
Hao Wu ◽  
Xiaoping Du
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Second Order ◽  
Time Dependent ◽  
Component Reliability ◽  
Reliability Method ◽  
Series Systems ◽  
State Functions ◽  
Time Dependent System

Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failure. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method that uses the envelop method and second order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the existing second order component reliability method, which produces component reliability indexes. The covariance between components responses are estimated with the first order approximations, which are available from the second order approximations of the component reliability analysis. Then the joint probability of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.

A Random Field Method for Time-Dependent Reliability Analysis With Random and Interval Variables

2016 ◽  
Author(s):  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Random Field ◽  
Limit State ◽  
Gaussian Random Field ◽  
Field Method ◽  
Time Dependent ◽  
State Function ◽  
Good Efficiency ◽  
First Order ◽  
Interval Variables ◽  
Reliability Method

In many engineering applications, both random and interval variables exist. Some of the random variables may also vary over time. As a result, the reliability of a component not only decreases with time but also resides in an interval. Evaluating the time-dependent reliability bounds is a challenging task because of the intensive computational demand. This research develops a method that treats a time-dependent random response as a random field with respect to both intervals and time. Consequently, random field methodologies can be used to estimate the worse-case time-dependent reliability. The method employs the first-order reliability method, which results in a Gaussian random field for the response with respect to intervals and time. The Kriging method and Monte Carlo simulation are then used to estimate the worse-case reliability without calling the original limit-state function. Good efficiency and accuracy are demonstrated through examples.

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