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中文核心期刊
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

PREDICTION OF WAVE TRANSMISSION OF CULVERT BREAKWATER BASED ON CNN

  • Culvert breakwater is a common coastal engineering structure. At the same time, the wave energy can also be transmitted into the harbor through the culvert, which will affect the hydrodynamic characteristics and mooring stability of the harbor. The study of its wave transmission characteristics is closely related to the safety of relevant production equipment and the corresponding engineering economic cost. However, many scholars mainly focus on theoretical analysis, experimental simulation and numerical calculation for wave transmission of culvert type vertical breakwater. With the development of machine learning technology, the traditional hydrodynamic problems ushered in a new solution concept which has attracted many attentions in the field of physics and engineering. At present, many scholars have applied machine learning algorithm to wave related problems. The machine learning algorithm can autonomously learn the corresponding laws according to the training data set, and establish the prediction model of hydrodynamic characteristics by data mapping. In practical application, it does not need to solve the fluid motion control equation, and has high computational efficiency. In this paper, based on the convolutional neural network (CNN), the wave transmission characteristics of the culvert breakwater under different incident and different opening conditions are predicted. The corresponding training data set is generated by a CFD model for convolution neural network training. The CFD results are compared with physical results for validation. After the data mapping relationship between different working conditions and the corresponding wave transmission results are established, the wave transmission coefficient and wave characteristics of transmission wave under the new working conditions can be predicted rapidly. The results show that the trained convolutional neural network can calculate the corresponding results within 10 milliseconds with a relatively high accuracy. This study can provide a new idea for solving the problem of interaction between waves and coastal structures, and is of importance in engineering application.
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