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基于卷积神经网络的涵洞式直立堤波浪透射预测

赵西增 徐天宇 谢玉林 吕超凡 姚炎明 解静 常江

赵西增, 徐天宇, 谢玉林, 吕超凡, 姚炎明, 解静, 常江. 基于卷积神经网络的涵洞式直立堤波浪透射预测[J]. 力学学报, 2021, 53(2): 330-338. doi: 10.6052/0459-1879-20-235
引用本文: 赵西增, 徐天宇, 谢玉林, 吕超凡, 姚炎明, 解静, 常江. 基于卷积神经网络的涵洞式直立堤波浪透射预测[J]. 力学学报, 2021, 53(2): 330-338. doi: 10.6052/0459-1879-20-235
Zhao Xizeng, Xu Tianyu, Xie Yulin, Lü Chaofan, Yao Yanming, Xie Jing, Chang Jiang. PREDICTION OF WAVE TRANSMISSION OF CULVERT BREAKWATER BASED ON CNN[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(2): 330-338. doi: 10.6052/0459-1879-20-235
Citation: Zhao Xizeng, Xu Tianyu, Xie Yulin, Lü Chaofan, Yao Yanming, Xie Jing, Chang Jiang. PREDICTION OF WAVE TRANSMISSION OF CULVERT BREAKWATER BASED ON CNN[J]. Chinese Journal of Theoretical and Applied Mechanics, 2021, 53(2): 330-338. doi: 10.6052/0459-1879-20-235

基于卷积神经网络的涵洞式直立堤波浪透射预测

doi: 10.6052/0459-1879-20-235
基金项目: 1) 国家自然科学基金(51679212)
详细信息
    作者简介:

    2) 姚炎明, 副教授, 主要研究方向: 海洋动力环境的数值模拟. E-mail: hotfireyao@163.com

    通讯作者:

    姚炎明

  • 中图分类号: TV13

PREDICTION OF WAVE TRANSMISSION OF CULVERT BREAKWATER BASED ON CNN

  • 摘要: 涵洞式直立堤是一种具有特殊用途的海岸工程结构物,对其透浪特性的研究具有重要工程意义. 然而,目前众多学者对于涵洞式直立堤波浪透射问题的研究主要以理论分析、实验模拟及数值计算为主.随着机器学习技术的发展, 传统水动力学问题迎来了新的求解理念.机器学习算法可根据训练数据集自主学习相应的规律,以数据映射的方式建立水动力学特征预测模型,在实际应用中无需对流体运动控制方程进行求解, 具有较高的计算效率. 因此,本文基于卷积神经网络(convolutional neural network, CNN),对不同开孔条件下的涵洞式直立堤透浪特征进行预测.首先利用模型试验验证计算流体力学(computational fluid dynamics, CFD)模型的有效性,然后基于CFD模型生成相应的训练数据集, 通过训练卷积神经网络模型,建立相应的波浪透射结果之间的数据映射关系,实现在新的工况下对波浪透射系数以及透射波波形等特征的快速预测. 结果表明,经过训练的卷积神经网络可在极短时间内计算得到相应的结果, 并具有较高的准确性.研究成果可为波浪与海岸结构物相互作用的问题提供新的求解理念.

     

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出版历程
  • 收稿日期:  2020-06-30
  • 刊出日期:  2021-02-10

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