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基于神经网络的非牛顿流体流动全电子控制。

Neural network-enabled, all-electronic control of non-Newtonian fluid flow.

作者信息

Bao Huilu, Zhang Xin, Zhang Xiaoyu, Fan Xiao, Boley J William, Ping Jinglei

机构信息

Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, USA.

出版信息

Appl Phys Lett. 2024 Oct 14;125(16):164105. doi: 10.1063/5.0226525. Epub 2024 Oct 18.

Abstract

Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 l min) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups.

摘要

对微尺度通道中通过的非牛顿流体进行实时全电子控制,对于制造和医疗保健中的各种应用至关重要。然而,现有方法缺乏精确测量所需的灵敏度以及有效调节所需的实时响应能力。在此,我们展示了一种全电子系统,该系统能够对通过微型出口的非牛顿流体流(0.76升/分钟)的各种波形进行闭环、实时、高灵敏度控制。我们的方法将非接触式袖带状流量传感器与神经网络控制程序相结合。该系统为非牛顿流体流量控制提供了一种简单、小型化、多功能且高性能的解决方案,可轻松集成到现有装置中。

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