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 a 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 min. The estimated extreme semi-submersible mooring tensions are compared with the MPM value.

提出了一种改进的ACER方法(MACER)来研究实验和数值结果中的短期极端系泊张力，并提出了一种基于贝叶斯推理的自适应马尔可夫链蒙特卡罗(MCMC)算法来拟合MACER函数的参数。为了研究 MACER 和 ACER 方法的可行性，考虑了两个典型案例。一个案例是深水半潜器的拉紧系泊绳。另一种情况是点吸收器的松弛系泊绳，其中发现系泊张力变化很大，并且频繁发生突然载荷。 ACER 和 MACER 方法都被用于模拟 3-h 极端动态系泊张力，并且估计值与测得的 3 小时最大动态张力进行比较。使用校准后的数值模型进行了50次全耦合动力分析。然后通过使用 Gumbel 模型来估计最可能的最大 (MPM) 系泊张力以拟合 50 个独立的最大系泊张力。使用 ACER 和 MACER 方法来估计基于 50 个样本的 3 小时极端系泊张力，每个样本的持续时间为 20 分钟。将估计的极端半潜式系泊张力与 MPM 值进行比较。