Prediction of transmission coefficient of double-row perforated cylinder breakwater based on SSA-CNN model
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摘要: 双排开孔圆筒防波堤是一种新型环境友好型防波堤，对其消浪特性的研究具有重要工程意义。随着人工智能的发展，基于机器学习技术求解防波堤水动力学问题成了一种新的研究范式。本文提出基于麻雀搜索算法（Sparrow Search Algorithm, SSA）优化卷积神经网络（Convolutional Neural Network, CNN）模型，实现对双排开孔圆筒防波堤透射系数的智能优化预测。结果表明：（1）确定波高、波周期、波长、波速、排间距、开孔率、水深为影响透射系数的关键因子；（2）当SSA-CNN模型的种群数量为10时，对波浪透射系数预测的R2值达到0.9909，平均相对误差相比单一的CNN模型降低了5.07%。研究成果为利用神经网络研究波浪透射问题提供了一种新的优化预测模型。Abstract: The double-row perforated cylinder breakwater is a new type of environment-friendly breakwater, and the research on its wave absorbing characteristics is of great engineering significance. With the development of artificial intelligence, solving the water dynamics problem of breakwater based on machine learning technology has become a new research paradigm. This paper proposes a Convolutional Neural Network (CNN) model based on Sparrow Search Algorithm (SSA) to achieve intelligent optimization prediction of transmission coefficient of double-row perforated cylindrical breakwater. The results show that: (1) wave height, wave period, wavelength, wave velocity, row spacing, hole rate and water depth are identified as the key factors affecting the transmission coefficient. (2) When the population size of the SSA-CNN model is 10, the R2 value of the wave transmission coefficient prediction reaches 0.9909, and the average relative error is reduced by 22.24% compared with the single CNN model. The research results provide a new optimal prediction model for the study of wave transmission by using neural networks.
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图 5 SSA-CNN和CNN模型的预测值和真实值结果对比
Fig. 5 Comparison of predicted values and real values of SSA-CNN and CNN models
图 11 预测值与CFD计算值对比
Fig. 11 Comparison between predicted values and CFD calculation values
表 1 数值模拟工况
Tab. 1 Numerical simulation working condition
工况 波高，H/m 0.06 0.07 0.09 0.11 波周期，T/s 1.4 1.5 1.6 1.8 水深，d/m 0.5 排间距，B/m 1.0 1.2 1.4 1.8 开孔率，e 23.11% 34.67% 46.22% 圆筒直径，D/m 0.2 开孔直径，D1/m 0.04 
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表 2 SSA-CNN算法中的超参数
Tab. 2 Hyperparameters in the SSA-CNN algorithm
参数 超参数 初始化范围 x1 批大小 1～130 x2 学习率 0.001～0.01 x3 conv_1 核大小 1～5 x4 conv_1 核数量 1～40 x5 conv_2核大小 1～5 x6 conv_2核数量 1～40 x7 全连接层神经元数量 1～10 x8 正则化系数 0.0001～0.1
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表 3 互信息值
Tab. 3 Mutual information value
序号 参数 MI 1 H 1.194 2 T 0.592 3 L 0.592 4 u 0.592 5 d 0 6 B 0.586 7 e 0.473 8 D1 0 9 D 0
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表 4 模型预测效果对比
Tab. 4 Comparison of model prediction effect
模型 R2 MAE MSE RMSE MRE CNN 0.8724 0.0276 0.0012 0.0345 0.0687 SSA-CNN 0.9909 0.0071 0 0.0092 0.0180
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表 5 平均相对误差结果分析
Tab. 5 Analysis of mean relative error results
序号 种群数量 平均相对误差 1 5 3.19% 2 10 0.71% 3 15 1.33% 4 20 1.04% 5 25 0.82% 6 30 0.98%
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