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High-speed Particle Image Velocimetry Near Surfaces
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基于流边界层中壁壁热流的速度场预测的完全卷积网络
Luca Guastoni1,2, Arivazhagan G Balasubramanian1,2, Firoozeh Foroozan3
1FLOW, Engineering Mechanics, KTH Royal Institute of Technology, Osquars Backe 18, 114 28 Stockholm, Sweden.
概括
这项研究引入了一种使用墙壁热流来预测流状态的新模型,克服了以往依赖于难以测量的墙壁剪压力的方法的局限性. 这种方法可以在实际应用中实现非侵入性的流量传感.
科学领域:
- 流体动力学 流体动力学
- 流建模 流建模
- 机器学习应用 机器学习应用
背景情况:
- 完全卷积神经网络 (FCN) 从墙壁测量中有效预测流状态.
- 之前的FCN模型使用了墙壁剪切应力,这在实验上很难获得.
- 准确的热流量测量在实验环境中是可行的.
研究的目的:
- 开发和验证一个FCN模型来预测使用墙壁热流作为输入的流状态.
- 克服与墙壁剪切应力测量相关的实验限制.
- 展示从模拟到实验数据的转移学习的首次应用,用于基于神经网络的流量传感.
主要方法:
- 开发了一种新的FCN模型,以接受来自被动标量器的墙壁热流数据作为输入.
- 该模型以直接数值模拟 (DNS) 数据进行训练,并根据实验数据进行微调.
- 研究了四个不同的普兰特尔数,以确定它们对模型性能的影响.
- 该模型在从水道流量中获得的实验数据上进行了测试.
主要成果:
- 开发的FCN模型使用墙壁热量流量测量成功预测了流状态.
- 从DNS转移到实验数据的学习成功实现,证明了模型的适应性.
- 该模型的性能在真实世界的实验数据上进行了评估,证实了其实际适用性.
结论:
- 墙壁热流是FCN在流预测中的可行替代输入.
- 转移学习提高了模拟训练的神经网络对实验数据的适用性.
- 这项工作为实际工程应用中的非侵入性流量传感奠定了基础.
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