华中科技大学学报（自然科学版）2024，Vol.52Issue(4)：16-21,6.DOI:10.13245/j.hust.240124
基于神经网络的二阶波-流和结构物相互作用
Study on second-order wave-current interactions with a structure based on the neutral network
摘要
Abstract
Wave diffraction by a horizontal semi-circle in a uniform current was evaluated based on the potential flow theory through the time-domain second-order approach.The time histories of first-and second-order wave and hydrodynamic force and their amplitudes or peaks of their superpositions were obtained and taken as the training set.The back propagation neutral network with Levenberg-Marquardt algorithm(LM-BP)was employed to make fast and accurate prediction of the wave and force including their histories and amplitudes or peaks at any wave frequency and current speed.Research results show that higher accurate results on wave and force amplitudes or peaks with small sample can be obtained using a single hidden layer with fewer neurons,while two hidden layers at least with more neurons are required for predicting the histories of first-and second-order waves and forces with very large sample.关键词
时域二阶理论/波-流-体相互作用/BP(逆向传播)神经网络/Levenberg-Marquardt算法/机器学习Key words
time-domain second order theory/wave-current-body interactions/back propagation(BP)neutral network/Levenberg-Marquardt algorithm/machine learning分类
数理科学引用本文复制引用
王赤忠,郑宇谦,葛晗,朱嵘华..基于神经网络的二阶波-流和结构物相互作用[J].华中科技大学学报（自然科学版）,2024,52(4):16-21,6.基金项目
国家自然科学基金资助项目(52171325). （52171325）
