Hydrodynamic Parameter Identification of Deep-Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter,Advanced Theory and Simulations

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Hydrodynamic Parameter Identification of Deep-Sea Mining Vehicle during Deployment and Retrieval Using a Nonlinear Filter
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2024-07-18 , DOI: 10.1002/adts.202400066
Yingjie Guan ₁ , Hongmao Qin _{1,

2} , Manjiang Hu _{1,

2} , Qingjia Cui _{1,

2} , Hao Zheng ₃ , Rongjun Ding _{1,

2}

Affiliation

The aim of this paper is to propose a novel method for identifying the hydrodynamic parameters of a deep-sea mining vehicle during deployment and retrieval. The proposed approach combines numerical simulation with a nonlinear filter. Initially, a dedicated hydrodynamic model for the deployment and retrieval of the mining vehicle is constructed. The identification process commences with simulations based on computational fluid dynamics (CFD). This approach utilizes CFD to simulate the motion of the deep-sea mining vehicle during deployment and retrieval, employing an implicit solution approach to analyze its motion in Heave and Yaw degrees of freedom under periodic external forces. Consequently, this provides hydrodynamic performance data. Subsequently, the unscented Kalman filter (UKF) estimator is applied to optimally solve an augmented matrix that incorporates both motion data and hydrodynamic parameters, yielding numerical values for the hydrodynamic parameters. Simulation results demonstrate that, in comparison to motion performance obtained by the CFD method, the hydrodynamic model derived from UKF enables an effective prediction of the motion of the deep-sea mining vehicle, with prediction errors consistently below 6%.

中文翻译：

基于非线性滤波器的深海采掘车部署和回收过程水动力参数辨识

本文的目的是提出一种在部署和回收过程中识别深海采矿船水动力参数的新方法。所提出的方法将数值模拟与非线性滤波器相结合。最初，构建了一个用于部署和回收矿用车辆的专用流体动力学模型。识别过程从基于计算流体动力学（CFD）的模拟开始。这种方法利用 CFD 来模拟深海采矿车辆在部署和回收过程中的运动，采用隐式求解方法来分析其在周期性外力下的升沉和偏航自由度的运动。因此，这提供了流体动力学性能数据。随后，应用无迹卡尔曼滤波器（UKF）估计器对包含运动数据和流体动力学参数的增广矩阵进行最佳求解，从而产生流体动力学参数的数值。仿真结果表明，与CFD方法获得的运动性能相比，UKF衍生的流体动力学模型能够有效预测深海采矿车辆的运动，预测误差始终低于6%。

更新日期：2024-07-18

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