Subset Simulation for Structural Reliability Analysis of Pipeline Corrosion Defects

2020 ◽  
Author(s):  
Daryl Bandstra ◽  
Alex M. Fraser
Keyword(s):  
Monte Carlo ◽  
Structural Reliability ◽  
Oil And Gas ◽  
Probability Of Failure ◽  
Computational Effort ◽  
Probabilistic Methods ◽  
Direct Monte Carlo Simulation ◽  
Metal Loss ◽  
Subset Simulation ◽  
Small Probabilities

Abstract One of the leading threats to the integrity of oil and gas transmission pipeline systems is metal-loss corrosion. This threat is commonly managed by evaluating measurements obtained with in-line inspection tools, which locate and size individual metal-loss defects in order to plan maintenance and repair activities. Both deterministic and probabilistic methods are used in the pipeline industry to evaluate the severity of these defects. Probabilistic evaluations typically utilize structural reliability, which is an approach to designing and assessing structures that focuses on the calculation and prediction of the probability that a structure may fail. In the structural reliability approach, the probability of failure is obtained from a multidimensional integral. The solution to this integral is typically estimated numerically using Direct Monte Carlo (DMC) simulation as DMC is relatively simple and robust. The downside is that DMC requires a significant amount of computational effort to estimate small probabilities. The objective of this paper is to explore the use of a more efficient approach, called Subset Simulation (SS), to estimate the probability of burst failure for a pipeline metal-loss defect. We present comparisons between the probability of failure estimates generated for a sample defect by Direct Monte Carlo simulation and Subset Simulation for differing numbers of simulations. These cases illustrate the decreased computational effort required by Subset Simulation to produce stable probability of failure estimates, particularly for small probabilities. For defects with a burst probability in the range of 10−4 to 10−7, SS is shown to reduce the computational effort (time or cost) by 10 to 1,000 times. By significantly reducing the computational effort required to obtain stable estimates of small failure probabilities, this methodology reduces one of the major barriers to the use of reliability methods for system-wide pipeline reliability assessment.

Predictive Assessment of External Corrosion on Transmission Pipelines

2008 ◽  
Author(s):  
Gae¨l Pognonec ◽  
Vincent Gaschignard ◽  
Philippe Notarianni
Keyword(s):  
Growth Rate ◽  
Oil And Gas ◽  
Electrochemical Corrosion ◽  
Software Tool ◽  
Probability Of Failure ◽  
Entropy Method ◽  
Probabilistic Methods ◽  
Optimal Interval ◽  
Annual Probability ◽  
Cross Entropy Method

Oil and Gas operators have to deal with the ageing process of their transmission pipeline grid. Some of these pipelines can be inspected using In Line Inspection (ILI) tools. In order to maintain an acceptable integrity level, re-inspection operations have to be performed. This process needs to be optimized in terms of resources and cost. Gaz de France R&D Division has developed a methodology which prioritizes rehabilitation operations on a pipeline after in-line inspections, and determines the optimal interval for re-inspection. A reliable help decision software tool which applies the methodology has also been developed. Dealing with defects assimilated to external electrochemical corrosion, the developed methodology is based on: • pigs information in order to assess a probable corrosion growth rate; • probabilistic distribution of input parameters (geometrical characteristics of defects, characteristics of the pipe and corrosion growth rate); • probabilistic methods of calculation : the probability of failure is calculated with the Monte-Carlo method. The convergence of the calculation is accelerated with the Cross Entropy method. The calculation results take the form of three probabilities of failure: • a punctual probability of failure for each defect; • an annual probability of failure for each defect; • an annual probability of failure per kilometer of pipe. To interpret the results, the annual probability of failure per kilometer of pipe is then compared with threshold values.

Probabilistic Lifetime Analysis for Turbine Blades Based on a Combined Direct Monte Carlo and Response Surface Approach

2004 ◽  
Author(s):  
Matthias Voigt ◽  
Roland Mu¨cke ◽  
Konrad Vogeler ◽  
Michael Oevermann
Keyword(s):  
Monte Carlo ◽  
Response Surface ◽  
Turbine Blades ◽  
Probabilistic Approach ◽  
Computational Effort ◽  
Life Assessment ◽  
Direct Monte Carlo Simulation ◽  
Gas Turbine Blades ◽  
Lifetime Analysis ◽  
Analysis Models

The paper addresses a probabilistic approach to lifetime prediction of cooled gas turbine blades. Variations of load and material parameters are taken into account by a combination of a direct Monte Carlo Simulation and a Response Surface Method. The proposed approach allows a reduction in the number of finite element analyses especially for problems with low failure probability. Therefore, the computational effort becomes acceptable even for full-scale 3D and 2D analysis models. Results of a probabilistic life assessment are shown for two cooled turbine blades. The probability of failure and the sensitivity of material and loading parameters are presented.

scholarly journals DETERMINATION OF THE PROBABILITY OF SYSTEM DESTRUCTION "LOOSE BODY - CONSTRUCTION" AGAINST SHIFT

2021 ◽  
Vol 3 (163) ◽  
pp. 2-6
Author(s):  
Khalife Rabih
Keyword(s):  
Monte Carlo ◽  
Statistical Method ◽  
Statistical Tests ◽  
Retaining Wall ◽  
Loose Body ◽  
Probability Of Failure ◽  
Probabilistic Methods ◽  
Building Structures ◽  
Body Structure ◽  
Method Of Calculation

The need for modern science-intensive models for assessing the reliability of building structures, and especially the system "loose body – structure" is now very acute due to the fact that such an assessment has become mandatory in the design. The existing gap is the lack of algorithms for determining the reliability of a complex system and is intended to fill this study. The aim of the article is to develop a method for determining the probability of failure of the system "bulk body – structure" against displacement using the method of statistical tests (Monte Carlo). To determine the probability of stability of the retaining wall against displacement, it is proposed to use the method of statistical tests using the accepted normative method of calculation. According to this method, it is necessary to perform N statistical tests, for each of which we will perform calculations according to the algorithm described in the article. A method for determining the probability of failure of the system "loose body – structure" against the shift by the statistical method of Monte Carlo. A test example was performed in the Mathcad environment. Calculations were performed to determine the probability of failure of the system "loose body – structure" against the shift by the statistical method of Monte Carlo. It was found that the value of the probability of failure of the system "loose body – structure" against the shift over the base service life may be in the range of 1x10-5… 1x10-3. It would also be interesting to use probabilistic methods to develop algorithms for the probability of failure of retaining walls due to loss of bearing capacity of the soil base, strength of the rock base, loss of strength of structural elements and joints, as well as the probability of exceeding the deformation of the base.

scholarly journals Non-deterministic Approach for Reliability Evaluation of Steel Portal Frame

2019 ◽  
Vol 5 (8) ◽  
pp. 1684-1697
Author(s):  
Hawraa Qasim Jebur ◽  
Salah Rohaima Al-Zaidee
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Structural Reliability ◽  
Limit State ◽  
Computational Cost ◽  
Probability Of Failure ◽  
Ultimate Limit State ◽  
Axial Forces ◽  
Portal Frame ◽  
Ultimate Limit

In recent years, more researches on structural reliability theory and methods have been carried out. In this study, a portal steel frame is considered. The reliability analysis for the frame is represented by the probability of failure, P_f, and the reliability index, β, that can be predicted based on the failure of the girders and columns. The probability of failure can be estimated dependent on the probability density function of two random variables, namely Capacity R, and Demand Q. The Monte Carlo simulation approach has been employed to consider the uncertainty the parameters of R, and Q. Matlab functions have been adopted to generate pseudo-random number for considered parameters. Although the Monte Carlo method is active and is widely used in reliability research, it has a disadvantage which represented by the requirement of large sample sizes to estimate the small probabilities of failure. This is leading to computational cost and time. Therefore, an Approximated Monte Carlo simulation method has been adopted for this issue. In this study, four performances have been considered include the serviceability deflection limit state, ultimate limit state for girder, ultimate limit state for the columns, and elastic stability. As the portal frame is a statically indeterminate structure, therefore bending moments, and axial forces cannot be determined based on static alone. A finite element parametric model has been prepared using Abaqus to deal with this aspect. The statistical analysis for the results samples show that all response data have lognormal distribution except of elastic critical buckling load which has a normal distribution.

Determining Fitness for Service of SCRs and Flowlines With Probabilistic Tools

2013 ◽  
Author(s):  
Mark Cerkovnik ◽  
David Saldana ◽  
Tracy Yang
Keyword(s):  
Monte Carlo ◽  
Probability Of Failure ◽  
Probabilistic Methods ◽  
Data Sets ◽  
Large Defect ◽  
Statistical Characterization ◽  
Physical Constraints ◽  
Extreme Loads ◽  
Deepwater Risers

Inspection of deepwater risers for flaws or pits using ILI tools can be challenging. Some lines are designed as “non-piggable”, and it is not unusual for an inspection to be incomplete because of physical constraints. As with any measurement, there will be a degree of error. While deterministic conclusions cannot be reached based on such incomplete data sets, probabilistic methods can be used effectively to make judgments about fitness for service. Commonly, different sections along a riser or flowline experience different fatigue spectra and extreme loads. Applying the loads from the sections with the highest loading to all flaws/pits can be too conservative. It is useful to employ statistical methods to assess the probability that a large defect occurs in a region with critical loads. These methods are especially useful when ILI data are incomplete or when estimates of damage must be made based on lines in similar corrosion environments. Properties and parameters other than inspection findings have an element of uncertainty. Fracture toughness, yield stress, and fatigue crack growth rates will be known in terms of mean and standard deviation. Soil properties may be known in terms of upper and lower bound. Likewise, there will be a range of uncertainty about service history and chemical environment. In such cases where fitness for service is based on the interaction of multiple random variables, Monte Carlo methods are appropriate for determining if the probability of failure is sufficiently low to tolerate. In the case of deepwater risers and flowlines where failure could result in loss of containment of hydrocarbons, permissible failure rates are on the order of 1E−5 to 1E−6 per year. This paper examines a riser and a flowline case study. For each case, a fitness for service analysis is conducted using a Monte Carlo simulation to evaluate the probability of failure based on incomplete ILI data and statistical characterization of other pertinent parameters. The results are compared against the conclusions of deterministic analysis.

Pipeline Integrity Reliability Analysis Levels

2016 ◽  
Author(s):  
Sherif Hassanien ◽  
Len Leblanc ◽  
Javier Cuervo ◽  
Karmun Cheng
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Failure Modes ◽  
Oil And Gas ◽  
Prediction Models ◽  
Metal Loss ◽  
Operating Pressure ◽  
Reliability Engineering ◽  
Reliability Methods ◽  
Types Of Information

Reliability engineering science is a mature discipline that has been used extensively in industries such as aviation, nuclear energy, automobiles, and structures. The application of reliability principles (especially structural reliability) in oil and gas transmission pipelines is still an active area of development. The advent of high resolution in-line inspections tools (ILI) facilitates a formal application/utilization of reliability methods in pipeline integrity in order to safely manage deformation, metal loss, and crack threats. At the same time, the massive amount of ILI data, their associated uncertainties, and the availability/accuracy of failure prediction models present a challenge for operators to effectively implement the use of reliability analysis to check the safety of integrity programs within available timeframes. On the other hand, approximate reliability techniques may affect the analysis in terms of both accuracy and precision. In this paper, a Pipeline Integrity Reliability Analysis (PIRA) approach is presented where the sophistication of the reliability analysis is staged into three levels: PIRA levels I, II and III. The three PIRA levels correspond to different representations of integrity uncertainties, uses of available validated/calibrated data, uses of statistical models for operating pressure and resistance random variables, implementation of reliability methods, and consideration of failure modes. Moreover, PIRA levels allow for improved integration of reliability analysis with the existing timelines/stages of traditional integrity programs, such that integrity data are updated as the integrity program progresses. The proposed integrity reliability approach allows for the delivery of safety checks leveraging all types of information available at any given point in time. In addition, the approach provides a full understanding of the strengths and weaknesses of each PIRA level. Pipeline corrosion case studies are provided herein to illustrate how the PIRA Levels can be applied to integrity programs.

Probabilistic Methods: Risk Based Design and Assessment

2019 ◽  
Author(s):  
H. Cathcart ◽  
G. Horne ◽  
J. Parkinson ◽  
A. Moffat ◽  
M. Joyce
Keyword(s):  
Monte Carlo ◽  
Damage Assessment ◽  
Structural Integrity ◽  
Probability Of Failure ◽  
Probabilistic Methods ◽  
Safety Factors ◽  
System Safety ◽  
Creep Fatigue ◽  
Low Probability ◽  
Integrity Assessments

Abstract Structural integrity assessments typically aim to calculate the integrity of a component under nominal or best estimate conditions. To account for potential variability and uncertainty present in the system, safety factors are often applied to assessment inputs and outputs. This approach does not allow the level of conservatism present to be quantified, often leading to over-conservatism or inadvertent non-conservatism. Probabilistic assessments explicitly calculate the probability of failure based on distributions of the input parameters and hence quantify the margin present in the assessment, leading to a greater understanding of the system. In this study a creep-fatigue damage assessment of a transiently loaded piping component is used as a vehicle to investigate some of the challenges and benefits of probabilistic assessments. A probabilistic assessment of the component life is compared to a lower-bound deterministic calculation to identify the mismatch in margin between the two results. The potential inaccuracies introduced when reducing the computational burden of Monte Carlo simulations with response surface methodologies are explored and tested. Finally, two challenges when attempting to underwrite a very low probability of failure are tackled: the inference of the shape of a distribution’s tails from limited experimental data and the uncertainty of extreme percentiles of finite Monte Carlo samples.

scholarly journals ANÁLISE DE CONFIABILIDADE ESTRUTURAL DE PROBLEMAS BASEADOS NA MECÂNICA DOS SÓLIDOS

2016 ◽  
Vol 12 (2) ◽  
Author(s):  
Vinícius Favaretto Defiltro ◽  
Wellison José Santana Gomes
Keyword(s):  
Monte Carlo ◽  
Reliability Analysis ◽  
Structural Reliability ◽  
Solid Mechanics ◽  
Structural Model ◽  
Simulation Method ◽  
Probability Of Failure ◽  
Structural Safety ◽  
Structural Element ◽  
Structural Problems

RESUMO: A Mecânica dos Sólidos, a partir de hipóteses simplificadoras, fornece modelos de cálculo que podem ser aplicados a vários problemas estruturais e estabelece as bases e o entendimento para o desenvolvimento de teorias e a construção de modelos mais complexos. Entretanto, dentro deste contexto, é comum desprezar as incertezas inerentes às propriedades dos materiais envolvidos, às condições de contorno e à geometria do problema. Neste artigo, ferramentas da Teoria da Confiabilidade Estrutural são aplicadas a problemas estruturais baseados na Mecânica dos Sólidos no intuito de analisá-los considerando algumas das incertezas envolvidas. Para isso, o método de Simulação de Monte Carlo é empregado na análise de confiabilidade de duas vigas. No estudo da primeira estrutura, busca-se investigar a influência da correlação entre variáveis aleatórias na probabilidade de falha do elemento estrutural. Na segunda estrutura, analisa-se o efeito da utilização de materiais com diferentes comportamentos (frágeis ou dúcteis) e, consequentemente, diferentes critérios de ruptura, sobre a probabilidade de falha estimada. Verifica-se que as análises de confiabilidade estrutural podem fornecer muitas informações que estão fora do escopo das soluções determinísticas. Tais informações permitem uma avaliação mais precisa da segurança estrutural e podem também levar a um melhor entendimento do modelo estrutural em questão. ABSTRACT: The Solid Mechanics, from simplifying assumptions, provides calculation models that can be applied to various structural problems and establishes the foundation and the understanding for the development of theories and the construction of more complex models. However, within this context, it is common to despise the uncertainties inherent to the properties of the materials involved, the boundary conditions and the geometry of the problem. In this article, Structural Reliability Theory tools are applied to structural problems based on Solid Mechanics in order to analyze them considering some of the uncertainties involved. For this, the Monte Carlo simulation method is used in the reliability analysis of two beams. In the first structure, study seeks to investigate the influence of correlation between random variables on the probability of failure of the structural element. In the second one, the effect of using materials with different behavior (ductile or brittle) and, consequently, different rupture criteria on the estimated probability of failure is analyzed. Structural reliability analysis can provide information which usually is outside the scope of deterministic solutions. Such information enables a more accurate assessment of structural safety and may lead to a better understanding of the structural model in question.

Probabilistic Reliability Assessment of Slope Stability Problems with Genetic Algorithms

2016 ◽  
Vol 846 ◽  
pp. 385-390
Author(s):  
Y.W. Tun ◽  
D.M. Pedroso ◽  
Alexander Scheuermann
Keyword(s):  
Limit Equilibrium ◽  
Failure Surface ◽  
Limit Equilibrium Method ◽  
Probability Of Failure ◽  
Computational Effort ◽  
Probabilistic Methods ◽  
Reliability Indices ◽  
Material Parameters ◽  
Failure Region ◽  
Critical Failure Surface

Uncertainty estimation and consideration in engineering is an important practice to design reliable structures especially in geotechnical engineering since the level of control with regards to the material parameters is much lower. Research has been conducted in order to assess the reliability of geotechnical works using probabilistic methods where challenges in computing the probability density function and predicting the critical failure region must be first overcome. One method to solve this problem is the Monte Carlo simulation; however it requires a high computational effort. Alternatively, reliability indices such as the Hasofer-Lind (HL) index can approximate the probability of failure Pf with fewer computations. Nonetheless, yet an optimisation problem needs to be solved. In this work, a genetic algorithm is developed to compute the HL index using the limit equilibrium method to search for the critical failure surface. Study cases and the analysis of the Vajont landslide are presented in order to illustrate the method.

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