Application of data-driven method in flow field reconstruction of unmanned underwater vehicle pump-jet propulsor

数据驱动在水下无人航行器泵喷推进器流场重构中的应用

Department of Missile and Weapon, Naval Submarine Academy, Qingdao 266199, China

Received: 13 April 2024

Abstract

To address the difficulty of obtaining high fidelity flow field information for unmanned underwater vehicle pump-jet propulsor, the accuracy of super-resolution flow field reconstruction by using a hybrid down-sampling skip connection/multi-scale reconstruction model of data-driven methods is investigated. This model can use the nonlinear distribution of bias and weight to establish a complex relationship between the low resolution and the super-resolution flow fields. Comparing with the experimental and numerical simulation methods, which has the advantages of high efficiency, low cost, and high accuracy. Meanwhile, the uncertainty distribution of super-resolution reconstruction of pump-jet propulsor was analyzed by using the variational Bayesian theory. The results indicate that the hybrid data-driven model with variational Bayesian theory has higher accuracy in reconstructing the flow field of pump-jet propulsor, and high reconstruction errors are mainly distributed in the hub area and rotor blade rotation area. The present method can increase the low resolution flow field of the unmanned underwater vehicles pump-jet propulsor by 256 times, high uncertainty area is mainly distributed at the peak of the reconstructed curve.

摘要

针对水下无人航行器泵喷推进器难以获取高保真流场信息的缺陷, 利用数据驱动方法中的混合下采样跳跃连接/多尺度重构模型研究了后置定子泵喷推进器超分辨流场重构的准确性。该模型可利用偏置、权重的非线性分布建立低分辨率流场与超分辨率流场之间的复杂关系。相比实验方法, 所提方法存在成本低的优势, 而相比数值仿真方法, 存在效率高、精度高的优势。同时, 结合变分贝叶斯理论分析了泵喷推进器超分辨重构的不确定性分布。研究结果表明: 含有变分贝叶斯理论的混合数据驱动模型重构泵喷推进器准确性更高, 较高的重构误差主要分布在轮毂区域及叶片旋转区域。该方法可将水下无人航行器泵喷推进器的低分辨流场提高256倍, 较高不确定性区域主要分布在重构曲线的波峰处。