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Multispectral Mid-Infrared Camera System for Accurate Stand-Off Temperature and Column Density Measurements on Flames
Accurate measurement of temperature in flames is a challenging problem that has been successfully addressed by hyperspectral imaging. This technique is able to provide maps of not only temperature T (K) but also of column density Q (ppm [Formula: see text] m) of the main chemical species. Industrial...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8703999/ https://www.ncbi.nlm.nih.gov/pubmed/34960488 http://dx.doi.org/10.3390/s21248395 |
Sumario: | Accurate measurement of temperature in flames is a challenging problem that has been successfully addressed by hyperspectral imaging. This technique is able to provide maps of not only temperature T (K) but also of column density Q (ppm [Formula: see text] m) of the main chemical species. Industrial applications, however, require cheaper instrumentation and faster and simpler data analysis. In this work, the feasibility and performance of multispectral imaging for the retrieval of T and Q [Formula: see text] in flames are studied. Both the hyperspectral and multispectral measurement methods are described and applied to a standard flame, with known T and Q [Formula: see text] , and to an ordinary Bunsen flame. Hyperspectral results, based on emission spectra with [Formula: see text] cm [Formula: see text] resolution, were found in previous works to be highly accurate, and are thus considered as the ground truth to compare with multispectral measurements of a mid-IR camera (3 to 5 [Formula: see text] m) with a six interference filter wheel. Maps of T and Q obtained by both methods show that, for regions with T [Formula: see text] K, the average of relative errors in multispectral measurements is ∼5% for T (and can be reduced to ∼2.5% with a correction based on a linear regression) and ∼20% for Q. Results obtained with four filters are very similar; results with two filters are also similar for T but worse for Q. |
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