Title: Data Assimilation-based Simultaneous Phase-Resolved Ocean Wave and Ship Motion Forecast Show Chinese title

Title: 基于数据同化的相位分辨海洋波浪和船舶运动联合预报

Abstract: This paper presents a data-assimilation (DA)-based approach to forecast the phase-resolved wave evolution process and ship motion, which is developed by coupling the high-order spectral method (HOS), ensemble Kalman filter (EnKF), and a Cummins-equation-based ship model (CMI). With the developed EnKF-HOS-CMI method, the observation data for wave, ship, or both can be incorporated into the model, therefore producing the optimal analysis results. The developed method is validated and tested based on a synthetic problem on the motions of an irregular wave field and a box-shaped free-floating ship. We show that the EnKF-HOS-CMI method achieves much higher accuracy in the long-term simulation of nonlinear phase-resolved wave field and ship motion in comparison with the HOS-CMI method. Also, the ship parameters are estimated accurately by using a parameter-augmented state space in EnKF. Show Chinese abstract

Abstract: 本文提出了一种基于数据同化（DA）的方法来预测相位分辨的波演化过程和船舶运动，该方法通过耦合高阶谱方法（HOS）、集合卡尔曼滤波器（EnKF）以及基于Cummins方程的船舶模型（CMI）来实现。使用所开发的EnKF-HOS-CMI方法，可以将波、船舶或两者的观测数据纳入模型，从而产生最优分析结果。该方法通过一个合成问题，即不规则波场和自由漂浮的箱形船的运动来验证和测试。结果显示，与HOS-CMI方法相比，EnKF-HOS-CMI方法在非线性相位分辨波场和船舶运动的长期模拟中具有更高的精度。此外，通过在EnKF中使用参数增强的状态空间，船舶参数得到了准确估计。