一种基于MGACO-RFR发射率模型在高温背景下识别的多光谱辐射温度计方法
Shan Gao1,2, Qing Yang1, Hailong Liu1
1College of Information and Communication Engineering, Harbin Engineering University, Harbin, China.
The Review of scientific instruments
|July 21, 2025
概括
本研究介绍了一种使用混合遗传算法和殖民地优化随机森林 (MGACO-RFR) 的新方法,以准确识别高温环境中的目标发射率模型. 这可以提高工业应用 (如轮机叶片) 的温度测量精度.
科学领域:
- 热物理学的热物理.
- 计量学 计量学 计量学
- 材料科学 材料科学 材料科学
背景情况:
- 多光谱辐射温度计对于高温应用至关重要.
- 由于背景反射,现有的方法难以识别发射率模型,导致温度错误.
研究的目的:
- 为高温场开发一个准确的多光谱辐射温度计方法.
- 为了应对排放率模型识别和温度测量错误的挑战.
- 为了提高工业高温合金和轮机叶片的精度.
主要方法:
- 提出了一种混合基因算法和殖民地优化随机森林 (MGACO-RFR) 用于排放性模型的识别.
- 利用高温背景辐射与排放率模型相结合用于分类器培训.
- 采用黑翼风优化算法来解决辐射温度计方程.
主要成果:
- 在没有噪声的情况下实现了97.1%的排放性模型识别准确度,在10%的噪声下达到94.9%.
- 证明了低平均温度测量误差:GH3044的3.0K,GH3128的3.5K,热屏障涂层的3.1K.
- 验证了高温合金和热屏障涂层样本在高达1223K的温度下使用的方法.
结论:
- 在高温环境中,MGACO-RFR方法显著提高了排放性模型识别准确度.
- 综合方法在具有挑战性的热背景下为工业部件提供高精度的温度反转.
- 这一进步对于高温工业过程的质量控制和运营效率至关重要.
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