通过热辐射光谱学准确的深度分辨率温度分析:数值方法与机器学习
Optics express
|September 23, 2025
概括
我们开发了三种方法来使用热辐射测量样品内部的材料温度. 一个深度神经网络 (DNN) 模型被证明是最有效的,用于准确的深度解析温度概况,优于传统的解决方案.
科学领域:
- 材料科学 材料科学 材料科学
- 频谱学是一种光谱学.
- 计算物理 计算物理
背景情况:
- 准确的温度测量对于理解材料的特性和行为至关重要.
- 深度解析的温度分析提供了对材料内的热传输和相变的关键见解.
- 传统的热分析方法往往缺乏复杂材料结构所需的精度.
研究的目的:
- 介绍和比较三种不同的计算方法,用于从热辐射光谱中检索深度解析温度分布.
- 通过合成和实验热辐射数据验证这些方法.
- 在各种材料系统中确定最有效的技术,以准确地定制各种材料系统的温度.
主要方法:
- 非线性方程解法器 (商业软件)
- 定制的非线性方程解法器
- 深度神经网络 (DNN) 模型
- 使用具有不同温度配置和噪音水平的合成数据集进行验证.
- 使用加热的化窗的实验光谱进行评估.
主要成果:
- 深度神经网络 (DNN) 模型与传统的数值技术相比,表现优越.
- 在合成和实验数据集中,DNN方法始终提供了准确的深度解析温度配置文件.
- 定制和商业非线性解决器都在准确性和稳定性方面表现出局限性.
结论:
- 深度神经网络 (DNN) 模型为热辐射光谱的深度分辨率温度概况提供了强大的和高度准确的解决方案.
- 基于DNN的方法在材料分析的性能上超过了传统的数值解法器.
- 这项工作提升了材料科学和工程中精确热特性的能力.
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