Lifetime reliability based design of an offshore vessel mooring

Grime, Andrew J.; Langley, R.S.

doi:10.1016/j.apor.2008.08.006

Cited by 18 publications

(8 citation statements)

References 27 publications

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“…where (h i , t i ) is the discrete sample of the random environmental variables, which follow a joint PDF using random number generation techniques; m is the total number of generated random variables. Equation (16) can be used to obtain an appropriate result compared with the exact solution using equation (1), in which the accuracy of the calculation depends on the size of the discrete number m. In other words, a large number of discrete environmental parameters is required; simultaneously, numerous nonlinear time-domain dynamic analyses for the floating-structure mooring system should be performed to determine the short-term extreme distribution; the entire computational task is not efficient and expensive. Nevertheless, an alternative approach exists to evaluate the long term extreme response instead of computing short-term distribution parameters for each given environmental variable (h i , t i ).…”

Section: Integration Of Long-term Responsementioning

confidence: 99%

“…In this scenario, the ULS response level which corresponded to a 100-year return period was investigated. When the marginal distribution of H s , T z and T z |H s was fitted, the environmental contour corresponding to a target return period N = 100 years ( β = − Φ −1 [1/( N × 365 × 8)] = 4.4983) could be obtained as shown in Figure 10. The principles to determine the environmental contour differed: the Rosenblatt transformation was based on the joint distribution of H s and T z , while the Nataf transformation used the marginal distribution of H s , T z , and the Pearson’s correlation coefficient of the variables in the transformed space.…”

Section: Numerical Examplementioning

confidence: 99%

“…Predicting extreme responses induced by environmental loading such as wind, wave, current, and ice is crucial to the mooring system design of floating structures. [1][2][3][4][5][6] These environmental conditions in the long term are non-stationary and extremely volatile. In practice, the environmental conditions for different locations are often modeled as a battery of short-term sea states that can be simulated as a statistically stationary process.…”

Section: Introductionmentioning

confidence: 99%

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Long-term extreme response analysis for semi-submersible platform mooring systems

Zhao

Dong

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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The accurate assessment of long-term extreme responses of floating-structure mooring system designs is important because of small failure probabilities caused by long-term and complex ocean conditions. The most accurate assessment would involve considering all conceivable sea states in which each sea state is regarded as a stochastic process and performing nonlinear time-domain numerical simulations of mooring systems to estimate the extreme response from a long-term analysis. This procedure would be computationally intensive because of the numerous short-term sea states involved. Here, a more feasible approach to evaluate the long-term extreme response is presented through immediate integration combined with Monte Carlo simulations. A parameter fitting procedure of the short-term extreme response distribution under irregular wave conditions is employed to solve the long-term response integration. Case studies were conducted on a semi-submersible platform using environmental data measurements of the Gulf of Mexico and a joint distribution model of the environmental parameters was considered. This approach was observed to be effective and the results were compared with those of traditional methodologies (univariate extreme value design and environmental contour methods). The differences were reflected using a reliability analysis of mooring lines, which indicated that the design standards must be stricter when using long-term analysis.

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Section: Integration Of Long-term Responsementioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Long-term extreme response analysis for semi-submersible platform mooring systems

Zhao

Dong

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…The reliability of individual components and individual lines in a mooring system was discussed by Vazquez-Hernandez et al [3] and MontesIturrizaga et al [4] concerning the design criteria and a method to quantify the probability of a component failure in an intact system. Grimea and Langley [5] used a simplified model to compare the probability of failure of mooring systems under different extreme environmental loads. Ku et al [6] performed a limited number of Monte Carlo simulations to estimate the probability of mooring system failure with various system properties.…”

Section: Introductionmentioning

confidence: 99%

Progressive reliability method and its application to offshore mooring systems

Mousavi

Gardoni

Maadooliat

2013

Engineering Structures

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Assessing the reliability of complex systems (e.g. structures) is essential for a reliability-based optimal design that balances safety and costs of such systems. This paper proposes the Progressive Reliability Method (PRM) for the quantification of the reliability of complex systems. The proposed method is a closed-form solution for calculating the probability of failure. The new method is flexible to the definition of "failure" (i.e., can consider serviceability and ultimate-strength failures) and uses the rules of probability theory to estimate the failure probability of the system or its components. The method is first discussed in general and then illustrated in two examples, including a case study to find the safest configuration and orientation of a 12-line offshore mooring system. The PRM results are compared with results of a similar assessment based on the Monte Carlo simulations. It is shown in the example of twocomponent that using PRM, the importance of system components to system safety can be quantified and compared as input information for maintenance planning.

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“…Till now, there are many researchers study the long-term extreme response problems: In the early 1993, Farnes and Moan (1993) employed Response Surface Approach to study the nonlinear flexible riser system. Grime and Langley (2008) considered the pseudo-asymptotic integration method in frequency domain; Vazquez-Hernandez (2011) used the Monte Carlo based approach with an interpolation scheme and Sagrilo et al (2011) proposed a combination of the Inverse First Order Reliability Method (IFORM) and an Importance Sampling Monte Carlo Simulation (ISMCS) approach. However, only few attempts are reported for the long-term assessment of fatigue damage in recent years.…”

Section: Introductionmentioning

confidence: 99%

Long-term fatigue assessment of deepwater risers in time domain

Gao¹

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With the increasing demand for oil and gas, offshore industry moved offshore structures towards deeper waters and severe environmental conditions. Offshore structures are subjected to many environmental loads during their service life, such as waves, winds, currents and ice.Such environmental conditions are non-stationary processes. However, in common practice, it is always assumed that these environmental actions are statistically stationary for short-term sea state, typically for three hours. Riser system is one of the most critical offshore structure component and is exposed to a large number of sea states during its life span, especially in deep water environment. Therefore, only stationary analysis for the riser system is not sufficient, and a global dynamic analysis is indispensable to obtain its response due to the fluid-structure interactions. During the global dynamic analysis, extreme response and fatigue life are two important design concerns. Extreme response predictions can be obtained based on either shortterm or long-term approaches. However, fatigue predictions can only be based on long-term methods due to their accumulative characteristics.The main objective of this study is to investigate various existing methods for their accuracy, limitations and efficiency, and to develop advanced methods in calculating the long-term fatigue damage of deep-water risers as well as in constructing a suitable statistical model for ocean environmental random variables.The traditional way of solving long-term fatigue problem is using a wave scatter diagram. By lumping several sea states into a small number of bins, one sea state is selected as a representation of each block to ensure that the damage quantity yielded by this sea state is not lower than the original sea states. It is apparent that such block method has a lack of accuracy.Therefore, there is a need for a fast and precise method for long-term fatigue analysis especially in time domain. The first part of the author's research is to develop a new efficient approach for long-term riser fatigue analysis in time domain. This new approach allows one to simulate the wave amplitudes from a distribution different from the original one. Then, the results for many sea states can be obtained by simply altering the importance sampling weighting function.This proposed approach is enhanced by a succession of additional techniques to reduce the sampling variability. The study of this proposed new efficient approach is presented in Chapter 3. IIIThe stochastic nature of environmental loads such as wave and wind applied on the structure is quite complicated. In the random process related structural analysis, a suitable statistical environmental condition model is essential and should be carefully developed for the prediction of long-term performance of offshore structures. As a common practice, a conditional joint distribution model is used for the statistical environmental condition, which is popularly used in the industry. However, in such model, only a linear relati...

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Lifetime reliability based design of an offshore vessel mooring

Cited by 18 publications

References 27 publications

Long-term extreme response analysis for semi-submersible platform mooring systems

Long-term extreme response analysis for semi-submersible platform mooring systems

Progressive reliability method and its application to offshore mooring systems

Long-term fatigue assessment of deepwater risers in time domain

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