基于计算机视觉和深度学习的流动模式,空隙分数和阻力传感器数据在微通道流水沸中的确定
Mark Schepperle1, Shayan Junaid1, Peter Woias1
1Laboratory for the Design of Microsystems, Department of Microsystems Engineering-IMTEK, University of Freiburg, 79110 Freiburg, Germany.
Sensors (Basel, Switzerland)
|June 19, 2024
概括
这项研究引入了一种新的深度学习方法,用于微通道流程沸分析. 它使用图像细分精确识别流动模式和空隙分数,为手工方法提供更快的替代方案.
科学领域:
- 多相流动动力学 多相流动力学
- 微流体学 微流体学
- 热传递是一种热传递.
背景情况:
- 微通道流水沸对于紧型换热器至关重要.
- 精确识别流量模式和空隙分数对于性能优化至关重要.
- 目前的分析方法可能是耗时和劳动密集的.
研究的目的:
- 开发和验证一种新的自动化方法,用于识别微通道流水沸中的流动模式和空隙分数.
- 为了比较数字图像处理和深度学习技术对此任务的有效性.
- 为实时流量分析建立一个强大而高效的系统.
主要方法:
- 一个数字图像处理管道,涉及自适应值,模糊,马校正,轮检测和直方图比较.
- 一种使用定制卷积神经网络 (CNN) U-net 架构进行特征提取和图像细分的深度学习方法.
- 流水沸条件的自动分类 (泡,,环形流) 和空隙分数的计算.
主要成果:
- 基于U-net的CNN在图像细分方面获得了99.1%的子分数,在流动模式分类方面获得了91%的准确性.
- 图像处理和深度学习方法都能够自动检测流量模式和空分数计算.
- 该CNN模型准确地预测了来自图像数据的电阻传感器读数,平均平方误差 (MSE) <10-6 .
结论:
- 开发的CNN方法为微通道流沸点分析提供了快速,可靠和自动化的解决方案.
- 这种方法比手工特征提取和传统分析技术有显著的改进.
- 这些发现表明深度学习在微观传热应用中的实时监控和控制方面的潜力.
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