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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 Cao1,2, 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.
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