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Publications
Xu, S., Ji, C-Y. and Guedes Soares, C. (2021), “Short-term extreme mooring tension and uncertainty analysis by a modified ACER method with Adaptive Markov Chain Monte Carlo simulations”, Ocean Engineering, Vol. 236, 109445.
A modified ACER method (MACER) is proposed to study the short-term extreme mooring tension in experimental and numerical results, and an adaptive Markov Chain Monte Carlo (MCMC) algorithm based on Bayesian inference is proposed to fit the parameters of the MACER function. To study the feasibility of the MACER and ACER methods, two typical cases are considered. One case is the taut mooring line of a deepwater semi-submersible. The other case is the slack mooring line for a point absorber, in which it is found that the mooring tensions vary dramatically and snap loads frequently occur Both the ACER and MACER methods are applied to model the 3-h extreme dynamic mooring tension, and the estimated values are compared with the measured 3-h maximum dynamic tension. Fifty fully coupled dynamic analysis are carried out by using the calibrated numerical model. The most probable maximum (MPM) mooring tension is then estimated by using the Gumbel model to fit the 50 independent maximum mooring tensions. The ACER and MACER methods are implemented to estimate 3-h extreme mooring tension based on fifty samples and the duration of each sample is 20 minutes. The estimated extreme semi-submersible mooring tensions are compared with the MPM value.
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