Probabilistic Analysis and Risk Assessment of Deep Water Composite Production Riser Against Fatigue Limit State

2015 ◽  
Author(s):  
Manander Singh ◽  
Suhail Ahmad
Keyword(s):  
Risk Assessment ◽  
Deep Water ◽  
Probabilistic Analysis ◽  
Limit State ◽  
Reliability Assessment ◽  
Random Variables ◽  
State Function ◽  
Limit State Function ◽  
Wide Range ◽  
Reliability Method

Deep water composite risers are subjected to randomly fluctuating loads, induced by wind and waves in the presence of fluctuating axial tension which may be critical in deep sea conditions. Therefore, risers experience the extreme bending and randomly fluctuating stresses throughout their service life. Cumulative fatigue damage is a critical assessment of riser life in the presence of large dynamic stresses. Probabilistic analysis and risk assessment of composite risers for cumulative fatigue is a vital design requirement for its satisfactory service and survival for stipulated period. Without addressing the reliability assessment, composite risers may not be recommended for deep water oil and gas exploration and production. Hence, the reliability assessment is a critical issue that is to be addressed for the safety of the deep water composite riser. It is studied for the entire system for all possible sea states occurring in the exploration region. Unlike conventional risers, the wall structure of a composite riser is more complicated. Therefore, multiple failure mechanisms are used jointly to assess the safety of the composite riser. Fatigue reliability is a challenging task due to complex nature of dynamic response and associated uncertainties caused by the material and external loads. The present study is focused on reliability assessment using stochastic finite element analysis. Response time histories for random sea plus current have been obtained. Requisite numbers of sea states are considered for the simulation of a wide range of off-shore environment and estimation of accumulated damage. By using the S-N data, damage fractions are calculated then summed linearly using Miner-Palmgren rule. The total damage has been obtained by summing the accumulated damages over all the sea states under consideration. Non-linear limit state function is derived based upon the above given approach to calculate the fatigue life. Important uncertainties associated with random variables are considered while deriving the limit state function. Numerical methods, such as Monte Carlo simulation and Advanced First Order Reliability Method, are used for the calculation of the reliability. The sensitivities of various random variables on overall probability of failure have been studied and design points have been located on failure surface. Probabilities of failure for important parameters are investigated.

scholarly journals Hybrid Reliability Analysis for Spacecraft Docking Lock with Random and Interval Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianguo Zhang ◽  
Jiwei Qiu ◽  
Pidong Wang
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
Simple Problem ◽  
State Function ◽  
Limit State Function ◽  
Interval Variables ◽  
Reliability Method ◽  
Hybrid Reliability

This paper presents a novel procedure based on first-order reliability method (FORM) for structural reliability analysis with hybrid variables, that is, random and interval variables. This method can significantly improve the computational efficiency for the abovementioned hybrid reliability analysis (HRA), while generally providing sufficient precision. In the proposed procedure, the hybrid problem is reduced to standard reliability problem with the polar coordinates, where an n-dimensional limit-state function is defined only in terms of two random variables. Firstly, the linear Taylor series is used to approximate the limit-state function around the design point. Subsequently, with the approximation of the n-dimensional limit-state function, the new bidimensional limit state is established by the polar coordinate transformation. And the probability density functions (PDFs) of the two variables can be obtained by the PDFs of random variables and bounds of interval variables. Then, the interval of failure probability is efficiently calculated by the integral method. At last, one simple problem with explicit expressions and one engineering application of spacecraft docking lock are employed to demonstrate the effectiveness of the proposed methods.

Fatigue Reliability Assessment of Coupled Spar-Mooring System

2011 ◽  
Author(s):  
Mohammed Jameel ◽  
Suhail Ahmad
Keyword(s):  
Limit State ◽  
Reliability Assessment ◽  
Coupled Analysis ◽  
State Function ◽  
Mooring System ◽  
Limit State Function ◽  
Mooring Lines ◽  
Reliability Indices ◽  
Current Limit ◽  
Reliability Method

Spar platform is a compliant floating structure used for exploration of oil and gas from deep sea. To ensure safe operations, reliability against mooring line failure is a major concern in design. Furthermore, the mooring lines have high investment costs and are normally not accessible for in-service inspection. The common approach for solving the dynamics of Spar system is to employ a decoupled quasi-static approach which ignores the platform and mooring lines interaction. Coupled analysis, used presently, considers the mooring lines and platform in an integrated single model. Hence, it effectively captures the damping effect due to Spar and mooring lines coupling. Finite element code ABAQUS is used to obtain the response of Spar-mooring system under long crested random sea with current. Limit state function is derived based on failure due to fatigue for probabilistic reliability assessment. Random variables, participating actively in the limit state function are identified and statistically modeled. The most probable points or the design points are found to be an effective parameter for estimating partial factors of safety for load and resistance variables. First Order Reliability Method (FORM) is used to calculate probability of failure and reliability indices. The results are later checked against Monte carlo simulation. Reliabilities of segmental length of mooring and of full length are determined as they may significantly differ if the mooring properties change along the length. Reliability indices of annual and life time sea states are calculated.

Reliability Methods for Bimodal Distribution With First-Order Approximation1

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Zhangli Hu ◽  
Xiaoping Du
Keyword(s):  
Probability Density ◽  
Order Approximation ◽  
Limit State ◽  
Random Variables ◽  
Random Variable ◽  
State Function ◽  
First Order ◽  
Reliability Method ◽  
Reliability Methods ◽  
Basic Random Variable

In traditional reliability problems, the distribution of a basic random variable is usually unimodal; in other words, the probability density of the basic random variable has only one peak. In real applications, some basic random variables may follow bimodal distributions with two peaks in their probability density. When binomial variables are involved, traditional reliability methods, such as the first-order second moment (FOSM) method and the first-order reliability method (FORM), will not be accurate. This study investigates the accuracy of using the saddlepoint approximation (SPA) for bimodal variables and then employs SPA-based reliability methods with first-order approximation to predict the reliability. A limit-state function is at first approximated with the first-order Taylor expansion so that it becomes a linear combination of the basic random variables, some of which are bimodally distributed. The SPA is then applied to estimate the reliability. Examples show that the SPA-based reliability methods are more accurate than FOSM and FORM.

Second-Order Reliability Method With First-Order Efficiency

2010 ◽  
Author(s):  
Xiaoping Du ◽  
Junfu Zhang
Keyword(s):  
Limit State ◽  
Saddlepoint Approximation ◽  
Quadratic Function ◽  
Second Order ◽  
State Function ◽  
Limit State Function ◽  
First Order ◽  
Reliability Method ◽  
Order Of Magnitude ◽  
Univariate Function

The widely used First Order Reliability Method (FORM) is efficient, but may not be accurate for nonlinear limit-state functions. The Second Order Reliability Method (SORM) is more accurate but less efficient. To maintain both high accuracy and efficiency, we propose a new second order reliability analysis method with first order efficiency. The method first performs the FORM and identifies the Most Probable Point (MPP). Then the associated limit-state function is decomposed into additive univariate functions at the MPP. Each univariate function is further approximated as a quadratic function, which is created with the gradient information at the MPP and one more point near the MPP. The cumulant generating function of the approximated limit-state function is then available so that saddlepoint approximation can be easily applied for computing the probability of failure. The accuracy of the new method is comparable to that of the SORM, and its efficiency is in the same order of magnitude as the FORM.

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.

scholarly journals A New SORM Method for Structural Reliability with Hybrid Uncertain Variables

2020 ◽  
Vol 11 (1) ◽  
pp. 346
Author(s):  
Pidong Wang ◽  
Lechang Yang ◽  
Ning Zhao ◽  
Lefei Li ◽  
Dan Wang
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
State Function ◽  
Strong Nonlinearity ◽  
Limit State Function ◽  
Practical Applications ◽  
Uncertain Variables ◽  
Practical Engineering

(1) Background: in practical applications, probabilistic and non-probabilistic information often simultaneously exit. For a complex system with a nonlinear limit-state function, the analysis and evaluation of the reliability are imperative yet challenging tasks. (2) Methods: an improved second-order method is proposed for reliability analysis in the presence of both random and interval variables, where a novel polar transformation is employed. This method enables a unified reliability analysis taking both random variables and bounded intervals into account, simplifying the calculation by transforming a high-dimension limit-state function into a bivariate state function. The obtained nonlinear probability density functions of two variables in the function inherit the statistic characteristics of interval and random variables. The proposed method does not require any strong assumptions and so it can be used in various practical engineering applications. (3) Results: the proposed method is validated via two numerical examples. A comparative study towards a contemporary algorithm in state-of-the-art literature is carried out to demonstrate the benefits of our method. (4) Conclusions: the proposed method outperforms existing methods both in efficiency and accuracy, especially for cases with strong nonlinearity.

Influence Research of Correlation of Random Variables on Structural Reliability

2011 ◽  
Vol 243-249 ◽  
pp. 245-250
Author(s):  
Yan Feng Fang ◽  
Li Yan Chen ◽  
Hua Xi Gao
Keyword(s):  
Monte Carlo ◽  
Correlation Coefficient ◽  
Structural Reliability ◽  
Limit State ◽  
Random Variables ◽  
State Equation ◽  
Maximum Intensity ◽  
State Function ◽  
Sampling Techniques ◽  
Limit State Function

In this paper, the influence of correlation of variables on structural reliability is discussed. Using importance, condition and duality sampling techniques of Monte Carlo method, accepted accuracy can be obtained. For the limit state function, the correlation of random variables will influence structural reliability, and the influence can be described. For the case of positive correlation, reliability will increase as the the correlation coefficient raise. For the case of negative correlation, reliability will drop as the correlation coefficient raise. The level of influence depends on the slope of limit state equation in standardized coordinate. When k=1, the influence attains maximum intensity for both cases.

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.

Inversion analysis to determine design parameters for reliability assessment in pavement structures

2014 ◽  
Vol 41 (10) ◽  
pp. 845-855 ◽  
Author(s):  
Sungho Mun
Keyword(s):  
Reliability Index ◽  
Limit State ◽  
Reliability Assessment ◽  
Pavement Performance ◽  
Design Parameters ◽  
State Function ◽  
Limit State Function ◽  
Performance Function ◽  
Pavement Structures ◽  
Inversion Analysis

Reliability assessment has been used to evaluate the performance of pavement structures. However, probabilistic inversion analysis of pavement structure design has not yet been tested to determine the design parameters of the pavement performance function, given a specified reliability index. In this study, a limit state function numerical calculation and the inversion technique of the Nelder–Mead simplex algorithm were used to determine the design parameters for the pavement performance function. The method of moments was used to develop the forward limit state function, which was then compared to Monte Carlo simulations; the comparison indicated good agreement between the two methods. Additionally, several cases were studied to determine the design parameters of the pavement performance function for the reliability index specified in this study. The case studies indicated that the structure number significantly affected the pavement performance function.

Reliability Analysis of Pressure Vessels in Lubricant Process Unit for Risk Based Inspection

2008 ◽  
Author(s):  
Chi-Hui Chien ◽  
Chun-Hung Chen
Keyword(s):  
Reliability Analysis ◽  
Pressure Vessel ◽  
Safety Concern ◽  
Limit State ◽  
Reliability Assessment ◽  
Pressure Vessels ◽  
State Function ◽  
Process Unit ◽  
Limit State Function ◽  
Failure Probabilities

As a safety concern to a pressurized system, to monitor the corrosion rate of each pressure vessel in order to make the repair decision at the right time based on the required thickness to withstand the maximum allowable working pressure (MAWP), is important to the plant owner. A plant inspector will normally assess the risk by evaluating the probability of failure of each pressure vessel during service hours with inspection and maintenance planning. Therefore, a scheme of reliability assessment to the pressure vessels should be established. The objective of this study is to discuss the failure probabilities of the pressure vessels in a lubricant unit in order to provide the input information for Risk Based Inspection (RBI) assessments. The reliability assessment of a pressure vessel involves the estimation of the failure pressure and evaluation of the limit state function. Based on the formula for calculating required thickness of a pressure vessel component, and due to the presence of non-linearity in the limit state function and the non-normal distributed variables, the first order second moment method (FOSM) was adopted for carrying out the reliability analysis. The uncertainties of the random variables in the limit state function were modeled by using normal and non-normal probabilistic distributions. As the heat exchanger is an important pressure vessel to a pressurized system, the failure probabilities together with the ranking categories of the heat exchangers in a lubricant unit are chosen as a case study to be discussed and presented in this paper.

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