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基于散射图像和深度学习的流场重建和粉末燃料运输预测

Hongyuan Du¹, Zhen Cao^1,2, Yingjie Song¹

¹National Key Laboratory of Laser Spatial Information, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|August 14, 2025

概括

深度学习使用散射图像准确预测粉末燃料流量. 该方法重建流域并对动态结构进行分类,从而实现实时质量流速估计.

科学领域:

粉末技术技术粉末技术
流体动力学流体动力学
机器学习机器学习

背景情况:

粉末燃料运输系统需要精确的流量监控.
光学传感提供了一种非侵入性的流量表征方法.
深度学习可以从图像数据中提取复杂的模式.

研究的目的:

开发一个深度学习框架,用于粉末燃料流域的重建和预测.
使用散射图像进行特征提取和流动动力学分析.
使用光学技术实现实时质量流速估计.

主要方法:

在不同流速下对基燃料进行散射光谱实验.
一个深度网络框架,结合了堆叠自动编码器 (SAE),逆向传播神经网络 (BP) 和长短期记忆 (LSTM).
从散射图像中提取特征,用于分类和预测.

主要成果:

SAE有效地提取特征向量,形成可分离的集群,以获得高分类准确度.
LSTM预测显示与基本真相有很强的一致性 (MSE:0.0027,MAE:0.0398,R平方:0.9897).
重建的图像在视觉上代表了流场变化,验证了结构层次的恢复.

结论:

拟议的深度学习方法可靠地重建和预测粉末燃料流量场.
从散射图像中提取特征对于动态流结构分析是有效的.
这种方法支持粉末燃料运输系统的实时质量流速预测.

关键词:

在 LSTM 网络中,功能提取特性提取分散图像的分散图像.堆叠的自动编码器时间预测时间预测.

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