Wave runup prediction for a semi-submersible based on temporal convolutional neural network  

作　　者：Yan Li Tao Peng Longfei Xiao Handi Wei Xu Li 

机构地区：[1]State key laboratory of Ocean Engineering,Shanghai Jiao Tong University,Shanghai 200240,China [2]SJTU-Sanya Yazhou Bay Institute of Deepsea Science and Technology,Hainan 572024,China

出　　处：《Journal of Ocean Engineering and Science》2024年第6期528-540,共13页海洋工程与科学（英文）

基　　金：support of the National Natural Science Foundation of China(Grant Nos.52031006,51879158);Shanghai Sailing Program,China(Grant No.20YF1419800).

摘　　要：Nonlinear wave runup could result in serious wave impact on the local structures of offshore platforms in rough seas.The reliable and efficient wave runup prediction is beneficial to provide essential information for the design and operation of offshore platforms.This work aims to develop a novel data-driven method to achieve the nonlinear mapping underlying the wave-structure interactions.The Temporal Convolution Network(TCN)model was employed to predict the wave runup along the column of a semi-submersible in head seas.The incident wave and vertical motions including heave,roll,and pitch were fed into the TCN model to predict the wave runup.Experimental datasets were provided for training and test.Tak-ing both temporal and spatial dependency into consideration,the input tensor space was optimized from the perspective of physical meaning and practicality.Sensitivity analyses were conducted to obtain the optimum length of time window and evaluate the relative importance of input variables to wave runup prediction.Moreover,the effects of characteristics and size of the training dataset on the model perfor-mance were investigated to provide guidelines for training dataset construction.Finally,upon validation,the generated TCN model showed a strong ability to provide stable and accurate wave runup results un-der various wave conditions,and it is a potential alternative tool to achieve efficient but low-cost wave runup prediction.

关 键 词：Wave runup Temporal Convolution Network(TCN) SEMI-SUBMERSIBLE Sensitivity analysis 

分 类 号：P731.22[天文地球—海洋科学] TP183[自动化与计算机技术—控制理论与控制工程]