Evaluation of RAOs of a semi-submersible platform using field measurements: A full-scale model in Caspian sea environmental conditions

The present research investigates the motion response of a semi-submersible platform using measured field data. The Iran-Amirkabir semi-submersible platform (SSP) was considered as full-scale model in operation conditions. First, 14 accelerometer sensors were installed on the platform's hull, and measurements were made in 5 different scenarios in the Caspian Sea environmental conditions. Then, the Response Amplitude Operators (RAOs) for six degrees of motion were extracted using measured accelerator data. The Surge, Sway, and Heave RAOs processed by the Kalman estimation filter, Pitch, Roll, and Yaw RAOs were calculated from the Sensor Array method. Moreover, the boundary element method in ANSYS/AQWA software for the Iran-Amirkabir semi-submersible model was developed, and the RAO results were validated against the extracted field measurements. The obtained results from the Sensor Array method and the Kalman estimation filter are compared with the numerical simulations, which show Root Mean Square Errors of less than 3% and 4%, respectively for rotational and translational movements. The Mean Square Errors between both methods were also close to zero. Therefore, the proposed methods predicted activities of the studied SSP with relatively good accuracy by field data collected in the Caspian Sea.     

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。