多光谱测温法基于黑体辐射定律,在当今的工程实践中得到了广泛的应用。可以从辐射强度和多组波长推断温度值。多光谱测温法消除了对单光谱和光谱相似性的要求,与双色测温相关。在多光谱温度反演过程中,光谱发射率和多光谱数据处理的解决方案可以看作是精确测温的关键。目前,光谱发射率通常使用假设模型来估计。当假设模型与实际情况非常匹配时,温度的反演和光谱发射率的精度都很高;然而,当两者不紧密匹配时,反演结果与实际情况有很大不同。光谱发射率的假设模型在用于复杂材料的测温时会出现缺点,或在燃烧过程中特性动态变化的任何材料。为解决上述问题,在本研究中,我们开发了一种基于优化思想的多光谱测温方法。该方法涉及在多光谱温度反演过程中分析每个通道的测量温度之间的连接;它还利用不同温度下的多光谱信号之间的相关性。总之,我们基于多光谱辐射测温原理建立了多变量温差相关函数,在温度反演过程中使用每个通道的数据之间的信息相关性。然后,我们通过优化相关函数并纠正任何测量误差来建立高精度测温模型。该方法简化了建模过程,从而成为温度差函数的优化问题。这也消除了假设光谱发射率和其他物理量之间的关系的需要,简化多光谱测温过程。最后,这涉及到光谱数据的校正,以减少测量误差对测温的任何影响。为了验证该方法的可行性和可靠性,一个简单的八通道多光谱测温装置用于实验验证,其中从黑体炉发出的温度被确定为标准值。此外,在1923.15-2273.15K的温度范围内校准468-603nm波段的光谱数据,导致基于优化原理的多光谱测温,误差率约为0.3%,温度计算时间小于3s。实现的反演精度水平优于使用二次测量方法(SMM)或神经网络方法获得的反演精度水平。并且实现的计算速度比使用SMM方法获得的计算速度快得多。
Multispectral
thermometry is based on the law of blackbody radiation and is widely used in engineering practice today. Temperature values can be inferred from radiation intensity and multiple sets of wavelengths. Multispectral
thermometry eliminates the requirements for single-spectral and spectral similarity, which are associated with two-colour
thermometry. In the process of multispectral temperature inversion, the solution of spectral emissivity and multispectral data processing can be seen as the keys to accurate thermometry. At present, spectral emissivity is most commonly estimated using assumption models. When an assumption model closely matches an actual situation, the inversion of the temperature and the accuracy of spectral emissivity are both very high; however, when the two are not closely matched, the inversion result is very different from the actual situation. Assumption models of spectral emissivity exhibit drawbacks when used for
thermometry of a complex material, or any material whose properties dynamically change during a combustion process. To address the above problems, in the present study, we developed a multispectral
thermometry method based on optimisation ideas. This method involves analysing connections between measured temperatures of each channel in a multispectral temperature inversion process; it also makes use of correlations between multispectral signals at different temperatures. In short, we established a multivariate temperature difference correlation function based on the principles of multispectral radiometric thermometry, using information correlations between data for each channel in a temperature inversion process. We then established a high-precision thermometry model by optimising the correlation function and correcting any measurement errors. This method simplifies the modelling process so that it becomes an optimisation problem of the temperature difference function. This also removes the need to assume the relationships between spectral emissivity and other physical quantities, simplifying the process of multispectral thermometry. Finally, this involves correction of the spectral data so that any impact of measurement error on the thermometry is reduced. In order to verify the feasibility and reliability of the method, a simple eight-channel multispectral thermometry device was used for experimental validation, in which the temperature emitted from a blackbody furnace was identified as the standard value. In addition, spectral data from the 468-603 nm band were calibrated within a temperature range of 1923.15-2273.15 K, resulting in multispectral thermometry based on optimisation principles with an error rate of around 0.3% and a temperature calculation time of less than 3 s. The achieved level of inversion accuracy was better than that obtained using either a secondary measurement method (SMM) or a neural network method, and the calculation speed achieved was considerably faster than that obtained using the SMM method.