基于神经网络与CFD相结合的扑翼推进性能优化

宋振宇; 朱建阳; 董璐

doi:10.7638/kqdlxxb-2023.0053

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基于神经网络与CFD相结合的扑翼推进性能优化

研究论文 | 更新时间：2026-05-28

- 基于神经网络与CFD相结合的扑翼推进性能优化
- Optimization of flapping hydrofoil propulsion performance based on combined neural network and CFD
- 空气动力学学报 2024年42卷第5期页码：53-63
- 作者机构：
  
  武汉科技大学冶金装备及其控制教育部重点实验室,武汉,430081
- 作者简介：
  
  宋振宇（1999—），男，硕士研究生，研究方向：水下机器人. Email： laosongchangtan@163.com
  朱建阳（1982—），男，博士，教授，研究方向：低速流动能捕获技术. Email： zhujy@wust.edu.cn
- 基金信息：
  
  国家自然科学基金（51975429）
- DOI：10.7638/kqdlxxb-2023.0053
  中图分类号： V211.41
- 纸质出版：2024
- 稿件说明：
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宋振宇, 朱建阳, 董璐. 基于神经网络与CFD相结合的扑翼推进性能优化[J]. 空气动力学学报, 2024, 42(5): 53−63.

SONG Zhenyu, ZHU Jianyang, DONG Lu. Optimization of flapping hydrofoil propulsion performance based on combined neural network and CFD[J]. 2024, 42(5): 53-63.
宋振宇, 朱建阳, 董璐. 基于神经网络与CFD相结合的扑翼推进性能优化[J]. 空气动力学学报, 2024, 42(5): 53−63. DOI： 10.7638/kqdlxxb-2023.0053

SONG Zhenyu, ZHU Jianyang, DONG Lu. Optimization of flapping hydrofoil propulsion performance based on combined neural network and CFD[J]. 2024, 42(5): 53-63. DOI： 10.7638/kqdlxxb-2023.0053.

摘要

为了提升现有水下扑翼式机器人的推进性能，采用田口实验、神经网络和计算流体力学（Computational Fluid Dynamics

CFD）相结合的方法，系统研究展弦比、升沉幅值、俯仰幅值和扑动频率变化对三维NACA 0012扑翼推进性能的影响。首先采用田口实验确定CFD仿真的参数组合，接着进行CFD仿真并为神经网络优化提供数据集，然后训练神经网络并用训练好的神经网络模型对CFD结果进行预测和验证，最后分析优化后的参数组合获得最优推进性能的机理。研究结果表明：展弦比、升沉幅值、俯仰幅值和扑动频率变化能显著影响扑翼的推进性能，其中扑动频率的影响最大，展弦比的影响最小，神经网络优化后的扑翼最大推进效率可达55.43%。进一步对不同参数组合扑翼周围流场结构分析发现，最优参数组合扑翼表面可以形成稳定的涡流，且涡流在扑动过程中可以长时间附着在扑翼表面，这是最优参数组合扑翼具有更好推进性能的内在原因。

Abstract

In order to improve the propulsion performance of existing underwater hydrofoil robots

the Taguchi experiments

neural networks and CFD are combined to systematically study the effects of aspect ratio

heaving amplitude

pitching amplitude and flapping frequency on the propulsion performance of a three-dimensional NACA 0012 hydrofoil. First

the parameter combinations for CFD simulation are determined by the Taguchi method. Next

CFD simulations are performed and the results are used as the training set of the neural network. Then

the neural network is trained and used to predict the CFD result. Finally

the mechanism for the optimal propulsion performance at the optimized parameter combinations is analyzed. The results show that the aspect ratio

heaving amplitude

pitching amplitude

and flapping frequency can significantly affect the propulsion performance of the hydrofoil

among which

the flapping frequency (aspect ratio) has the greatest (least) influence on the propulsion performance. The maximum propulsion efficiency of the hydrofoil can reach 55.43% after the optimization of the neural network. Further analysis of the flow field structure around the hydrofoil with different parameters reveals that

under the optimal parameters there forms a stable vortex on the hydrofoil surface

which can stay on the hydrofoil surface for a long time during the flapping process. This is the intrinsic reason for the better propulsion performance at the optimal parameters.

关键词

Keywords

references

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