研究基于TDLAS阵列传感器和SwinLSTM模型的二维温度场的时空预测方法
Yongxin Hou1,2, Rui Jia1,2, Shenxiang Feng1,2
1State key Laboratory of Extreme Environment Optoelectronic Dynamic Measurement Technology and Instrument, Taiyuan, Shanxi, China. haoxiaojian@nuc.edu.cn.
The Analyst
|January 8, 2026
概括
这项研究引入了一种使用二维阵列可调节二极管激光吸收光谱 (TDLAS) 和深度学习来测量火焰温度的新方法. 该方法准确地捕捉了火焰结构,并预测了未来的温度场.
科学领域:
- 燃烧诊断仪器的使用
- 光学光谱学是指光学光谱学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 传统的可调节二极管激光吸收光谱 (TDLAS) 为燃烧场提供有限的空间分辨率.
- 现有的燃烧诊断工具面临着动态响应延迟的挑战.
研究的目的:
- 为2D燃烧场开发一个时空预测诊断方法.
- 为了提高火焰温度测量的准确性和速度.
- 为了利用深度学习来增强燃烧诊断.
主要方法:
- 使用2D阵列TDLAS直接成像与64像素探测器进行并行O2吸收测量.
- 开发了一个SwinLSTM深度学习模型,以捕捉空间和时间温度场动态.
- 采用"点-表面集成"策略,使用热电偶和热成像仪进行多维验证.
主要成果:
- 在单点定量反转中达到3.75%的最大相对误差.
- 准确地重建了2D火焰温度场及其宏观结构.
- 在预测任务中,SwinLSTM-D模型实现了高性能 (SSIM:0.961,PSNR:38.625 dB),优于其他深度学习模型.
结论:
- 拟议的方法准确地重建了二维火焰温度场.
- 深度学习模型有效地预测了火焰温度的时空演变.
- 这项研究为燃烧测量和诊断技术提供了新的方法.
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