Fatigue assessment of ship structures based on equivalent wave probability (EWP) concept (1st report): Proposal of EWP concept and its verification by 8600TEU container ship's onboard hull monitoring

A long-term fatigue assessment method based on EWP concept is proposed. ‘Equivalent wave probability (EWP)’ is the fictitious (H_S, T_m)'s joint probability distribution function (JPDF), for which the frequency distribution of the stress variance R², f(R²), calculated by spectral fatigue assessment agrees with the observed one. By choosing probability function p(R²) to fit f(R²), the R²'s statistical model (R2SM) which represents the relation between the EWP parameters and R²'s population parameters is developed, and the Bayesian inference, which can estimate the EWP parameters from the measured R² data is developed. The EWP at the reference position (RP) can be determined by Bayesian inference from the measured R² through the R2SM at RP. To accurately estimate the measured f(R²) at the target position (TP) from the EWP at RP, an R2SM correction factor at TP, denoted by α_TP, is introduced in the process of assimilating R2SM. The resulting R2SM, which has been assimilated by Bayesian inference using measured data, is referred to as data-assimilated R2SM (DAR2SM). The fatigue assessment using EWP at RP as the input of DAR2SM at TP is called Bayes-EWP-DAR2SM analysis. The validity of Bayes-EWP-DAR2SM analysis is verified by using the long-term (about four years) multi(12)-position hull monitoring (HM) data of an 8,600TEU container ship. The fatigue damages estimated by Bayes-EWP-DAR2SM based solely on the stress history of a single sensor are in agreement with measurements with sufficient accuracy, independent of the chosen data assimilation period. This demonstrates that the multi-position fatigue assessment solely through HM at one RP based on EWP concept is realized.     

考虑电堆寿命的氢燃料电池汽车能量管理策略研究

提出了一种在满足动力性需求并且以氢燃料电池堆作为主要能源的前提下，能有效延长电堆使用寿命的能量管理策略。提出将需求功率 SG滤波后再进行规则控制的能量管理策略，将多种循环工况的结果进行手动优化后作为训练数据集，设计三输入一输出的自适应神经模糊推理系统控制器，根据其输出结果再进行一次滤波最终形成基于自适应神经模糊推理系统优化的能量管理策略。使用CLTC-P循环工况对能量管理策略进行仿真验证，结果表明，基于自适应神经模糊推理系统优化的能量管理策略能有效延长氢燃料电池剩余使用寿命，相比滤波加规则策略剩余使用寿命增加了33%，并能保持动力电池SOC处于适宜水平。