基于人工侧线传感器系统的流场感知研究

Research on Flow Field Perception Based on Artificial Lateral Line Sensor System.

作者信息

Liu Guijie, Wang Mengmeng, Wang Anyi, Wang Shirui, Yang Tingting, Malekian Reza, Li Zhixiong

机构信息

Department of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.

Department of Electrical, Electronic & Computer Engineering, University of Pretoria, Pretoria 0002, South Africa.

出版信息

Sensors (Basel). 2018 Mar 11;18(3):838. doi: 10.3390/s18030838.

DOI:10.3390/s18030838

PMID:29534499

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5877381/

Abstract

In nature, the lateral line of fish is a peculiar and important organ for sensing the surrounding hydrodynamic environment, preying, escaping from predators and schooling. In this paper, by imitating the mechanism of fish lateral canal neuromasts, we developed an artificial lateral line system composed of micro-pressure sensors. Through hydrodynamic simulations, an optimized sensor structure was obtained and the pressure distribution models of the lateral surface were established in uniform flow and turbulent flow. Carrying out the corresponding underwater experiment, the validity of the numerical simulation method is verified by the comparison between the experimental data and the simulation results. In addition, a variety of effective research methods are proposed and validated for the flow velocity estimation and attitude perception in turbulent flow, respectively and the shape recognition of obstacles is realized by the neural network algorithm.

摘要

在自然界中，鱼类的侧线是一种独特且重要的器官，用于感知周围的流体动力环境、捕食、逃避捕食者以及集群。本文通过模仿鱼类侧线管神经丘的机制，开发了一种由微压力传感器组成的人工侧线系统。通过流体动力学模拟，获得了优化的传感器结构，并建立了均匀流和湍流中侧面的压力分布模型。进行相应的水下实验，通过实验数据与模拟结果的比较验证了数值模拟方法的有效性。此外，分别针对湍流中的流速估计和姿态感知提出并验证了多种有效研究方法，并通过神经网络算法实现了障碍物的形状识别。