Reliability Assessment of Marine Drilling Risers With Correlated Random Variables

2016 ◽  
Author(s):  
Piyali Sengupta ◽  
Ying Min Low ◽  
Xiaodong Zhang ◽  
Peter Francis Bernad Adaikalaraj ◽  
Chan Ghee Koh
Keyword(s):  
Life Cycle ◽  
Computational Model ◽  
Reliability Analysis ◽  
Deep Water ◽  
Environmental Conditions ◽  
Life Cycle Cost ◽  
Structural Reliability ◽  
Random Variables ◽  
Density Functions ◽  
Total Life

Marine drilling risers are integral parts of the deep water offshore oil and gas industry. They are required to be designed for safe operations during their service lives with appropriate degree of reliability. With limited experience present in ultra-deep water, drilling risers are subjected to a range of uncertainties arising from untested environmental conditions. However, the current industry practice is limited to deterministic design of drilling risers which cannot account for uncertainties present in real life scenario. Under uncertain environmental conditions, deterministic methods may lead to undesired consequences, i.e. over conservative or unsafe design and misguided estimates of operability and down time of ultra-deep water drilling risers affecting the total life cycle cost. Thus, structural reliability analysis is particularly useful for prediction of the probabilities of downtime and disconnection of drilling risers incorporating the environmental uncertainties. In addition, structural reliability analysis can be used to reduce the total life cycle cost of ultra-deep water drilling risers. In reliability analysis, many studies use uncorrelated random variables to represent uncertainties for simplification. Nevertheless, uncertainties in environmental conditions may be strongly correlated (for example wind and wave loads). If the correlation is not accounted for, it may lead to erroneous probability estimates. Thus, a joint environmental model is proposed in this paper using the conditional modeling approach where a joint density function is defined in terms of a marginal distribution and a series of conditional density functions. The joint density functions of environmental conditions are constructed in the current study using the recorded metocean data for Gulf of Mexico available from National Oceanic and Atmospheric Administration (NOAA) website. Then a computational model of connected ultra-deep water drilling riser system is constructed in ORCAFLEX to conduct time domain dynamic analysis. Thereafter, the correlated random variables in combination with the drilling riser computational model are utilized for conducting Monte Carlo Simulation (MCS) to evaluate the probabilities of downtime and disconnection. MCS is a widely accepted and robust approach and generally used as a benchmark to verify the accuracy of other reliability methods. But, in presence of large number of random variables representing environmental uncertainties, MCS is computationally demanding especially for the large number of simulations required to estimate small failure probabilities associated with extreme values. To this end, probability density functions of drilling riser responses are evaluated using Shifted Generalized Lognormal Distribution (SGLD) and Generalized Extreme-Value (GEV) Distribution both of which show similar accuracy (compared to MCS results) at a fraction of computing time (around 1/500 times).

scholarly journals Hermite polynomial normal transformation for structural reliability analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jinsheng Wang ◽  
Muhannad Aldosary ◽  
Song Cen ◽  
Chenfeng Li
Keyword(s):  
Reliability Analysis ◽  
Hermite Polynomial ◽  
Structural Reliability ◽  
Hermite Polynomials ◽  
Random Variables ◽  
Transformation Method ◽  
Statistical Moments ◽  
Content Type ◽  
Structural Reliability Analysis ◽  
Normal Transformation

Purpose Normal transformation is often required in structural reliability analysis to convert the non-normal random variables into independent standard normal variables. The existing normal transformation techniques, for example, Rosenblatt transformation and Nataf transformation, usually require the joint probability density function (PDF) and/or marginal PDFs of non-normal random variables. In practical problems, however, the joint PDF and marginal PDFs are often unknown due to the lack of data while the statistical information is much easier to be expressed in terms of statistical moments and correlation coefficients. This study aims to address this issue, by presenting an alternative normal transformation method that does not require PDFs of the input random variables. Design/methodology/approach The new approach, namely, the Hermite polynomial normal transformation, expresses the normal transformation function in terms of Hermite polynomials and it works with both uncorrelated and correlated random variables. Its application in structural reliability analysis using different methods is thoroughly investigated via a number of carefully designed comparison studies. Findings Comprehensive comparisons are conducted to examine the performance of the proposed Hermite polynomial normal transformation scheme. The results show that the presented approach has comparable accuracy to previous methods and can be obtained in closed-form. Moreover, the new scheme only requires the first four statistical moments and/or the correlation coefficients between random variables, which greatly widen the applicability of normal transformations in practical problems. Originality/value This study interprets the classical polynomial normal transformation method in terms of Hermite polynomials, namely, Hermite polynomial normal transformation, to convert uncorrelated/correlated random variables into standard normal random variables. The new scheme only requires the first four statistical moments to operate, making it particularly suitable for problems that are constraint by limited data. Besides, the extension to correlated cases can easily be achieved with the introducing of the Hermite polynomials. Compared to existing methods, the new scheme is cheap to compute and delivers comparable accuracy.

Adjusting Environmental Contours for Specified Expected Number of Unwanted Events

2020 ◽  
Author(s):  
Erik Vanem
Keyword(s):  
Reliability Analysis ◽  
Return Period ◽  
Environmental Conditions ◽  
Structural Reliability ◽  
Probability Of Failure ◽  
Expected Number ◽  
Probability Of Exceedance ◽  
Underlying Distribution ◽  
Extreme Loads ◽  
Time Period

Abstract Environmental contours are applied in probabilistic structural reliability analysis to identify extreme environmental conditions that may give rise to extreme loads and responses. Typically, they are constructed to correspond to a certain return period and a probability of exceedance with regards to the environmental conditions that can again be related to the probability of failure of a structure. Thus, they describe events with a certain probability of being exceeded one or more times during a certain time period, which can be found from a certain percentile of the underlying distribution. In this paper, various ways of adjusting such environmental contours to account for the expected number of exceedances within a certain time period are discussed. Depending on how such criteria are defined, one may get more lenient or more stringent criteria compared to the classical return period.

Structural Reliability Analysis Using Fourier Orthogonal Neural Network Method

2013 ◽  
Vol 838-841 ◽  
pp. 360-363 ◽  
Author(s):  
Li Rong Sha ◽  
Yue Yang
Keyword(s):  
Neural Network ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Failure Probability ◽  
Structural Reliability ◽  
Random Variables ◽  
Structural Reliability Analysis ◽  
Surface Method ◽  
Structural Responses

In order to predict the failure probability of a complicated structure, the structural responses usually need to be predicted by a numerical procedure, such as FEM method. The response surface method could be used to reduce the computational effort required for reliability analysis. However the conventional response surface method is still time consuming when the number of random variables is large. In this paper, a Fourier orthogonal neural network (FONN)-based response surface method is proposed. In this method, the relationship between the random variables and structural responses is established using FONN models. Then the FONN model is connected to the first order and second moment method (FORM) to predict the failure probability. Numerical example result shows that the proposed approach is efficient and accurate, and is applicable to structural reliability analysis.

Reliability analysis and life cycle cost optimization: a case study from Indian industry

2016 ◽  
Vol 33 (3) ◽  
pp. 414-429 ◽  
Author(s):  
Laxman Yadu Waghmode ◽  
Rajkumar Bhimgonda Patil
Keyword(s):  
Life Cycle ◽  
Reliability Analysis ◽  
Life Cycle Cost ◽  
Cost Optimization ◽  
Control Panel ◽  
Life Cycle Cost Analysis ◽  
Content Type ◽  
Cutting Machine ◽  
Band Saw

Purpose – Reliability analysis is required to identify the components or subsystems with low reliability for a given designed performance. Life cycle cost analysis helps understand the cost implications over the entire life span of a product. The purpose of this paper is to present a case study describing reliability analysis and life cycle cost optimization of a band saw cutting machine manufactured and used in India. Design/methodology/approach – The data required for reliability analysis is collected from the manufacturer and users of band saw cutting machine. The parameters of failure distribution have been estimated by using ReliaSoft’s Weibull++6 software. The life cycle cost is divided into various cost elements such as acquisition cost, operation cost, failure cost, support cost and net salvage value. Findings – The results of the analysis show that the components such as band wheel bearing, guide roller bearing, limit switch, carbide pad, hydraulic cylinder oil seal, control panel dial, control panel and solenoid valve are critical from reliability and life cycle cost analysis perspective. Originality/value – With certain design changes it is found that the reliability of the system is increased by 15.85 percent while the life cycle cost is reduced by 22.09 percent. The study also shows that the reliability analysis is useful for deciding maintenance intervals.

Comparative Assessment of Low-Level Radioactive Waste Life-Cycle Disposal Costs of U.S. Commercial Facilities

2001 ◽  
Author(s):  
E. J. Bentz ◽  
C. B. Bentz ◽  
T. D. O’Hora
Keyword(s):  
Life Cycle ◽  
Radioactive Waste ◽  
Life Cycle Cost ◽  
Regulatory Compliance ◽  
Comparative Assessment ◽  
Life Cycle Costs ◽  
Low Level ◽  
Low Level Radioactive Waste ◽  
The U.S ◽  
Total Life

Abstract This paper provides a comparative assessment of low-level radioactive waste (LLW) life-cycle costs for U.S. commercial disposal facilities. This assessment includes both currently operational facilities and planned commercial facilities. After identifying the individual facility’s operational period, current or planned capacity, and historical disposal volumes (where applicable), the paper describes the respective facilities’ waste acceptance criteria, anticipated waste characteristics, and disposal technologies employed. A brief identification of key components of cost categories that constitute life-cycle cost for the disposal facilities is provided, as well as an identification of factors that affect life-cycle cost. A more specific comparison of certain life-cycle cost components for the disposal facilities is provided, with regard to U.S. LLW disposal volumes and characteristics. Similarities and differences in total life-cycle cost and life-cycle category-specific costs among the U.S. facilities are presented and discussed. The data presented reveals that: • No new LLW commercial disposal facilities have been sited in the U.S. since 1988, and that siting of LLW disposal facilities in the U.S. has become increasingly difficult and contentious, necessitating long lead times and significant up-front costs — without any certainty of success. • Overall, life-cycle costs for LLW disposal at U.S. commercial facilities have increased significantly over time, reflecting increased regulatory compliance requirements, state-imposed access fees and taxes, local community hosting incentive costs, and cost escalation inherent in delays in establishing facilities or modifying existing licensed facilities. • Life-cycle costs are also significantly affected by the nature of the engineered isolation technology employed, reflecting the geologic characteristics of the siting location and the activity levels of the wastes accepted. • Since many of the newly-planned facilities anticipate receiving lower total volumes with an increasingly greater percentage of higher activity wastes (than historical volumes disposed) and are to be sited in more ecologically sensitive geologic regions, they will require more comprehensive — and hence more expensive — engineered isolation technologies. As a result, currently planned facilities are anticipated to experience significantly higher total life-cycle costs than existing operational facilities.

STRUCTURAL RELIABILITY ANALYSIS OF STEEL TRUSS ELEMENTS ON BUCKLING USING PBOX APPROACH

2021 ◽  
pp. 45-53
Author(s):  
A.A. Solovyova ◽  
◽  
S.A. Solovyov ◽  
Keyword(s):  
Reliability Analysis ◽  
Structural Reliability ◽  
Statistical Data ◽  
Limit State ◽  
Random Variables ◽  
Distribution Functions ◽  
Structural Safety ◽  
Random Load ◽  
Load Models ◽  
Steel Trusses

Abstract. The reliability of load-bearing structural elements is one of the indicators of structural safety. The article presents methods for steel trusses bars reliability analysis according to the buckling criterion using p-boxes. A p-box consists of two boundary probability distribution functions that form the area of possible distribution functions. Such model used for modeling random variables in conditions of incomplete statistical data by quantity or quality. An algorithm for summing p-boxes of random load models is demonstrated on the example of a probabilistic estimate of the force in the truss bar. The result of reliability analysis using p-boxes is presented in interval form. The use of p-boxes makes it possible to obtain a more cautious assessment of reliability in case of incomplete statistical data. To increase the informativity of the reliability analysis result, it is necessary to obtain more statistical data about random variables in design mathematical models of limit state, which will allow forming p-boxes with narrower boundary distribution functions.

scholarly journals Life Cycle Cost Comparison Study of PV and Concrete Rooftop in Jakarta

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Religiana Hendarti
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Department Of Energy ◽  
Cost Comparison ◽  
Electricity Production ◽  
Solar Panels ◽  
The Us ◽  
Different Types ◽  
Building Life Cycle ◽  
Total Life

This paper presents a comparative study of “life cycle cost” or LCC of a building school rooftop element in Jakarta. The simulation applied two different types of roof: a concrete roof and a PV rooftop. The aim of this study is to investigate the electricity production of the solar panels, the saving to investment ratio or SIR, and the total life cycle cost of each rooftop element. To accommodate those objectives, the calculation utilized a software called “Building Life Cycle Cost (BLCC) version 5” which is a product of the US Department of Energy. The simulation results showed that the LCC can be improved by 27.6%, and the “discounted payback” is reached at year 15. Indeed, this indicates that a roof made of solar panels is promising to replace the existing concrete roof.

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