Numerical assessment of uncertainty and dynamic range expansion of multispectral image-based pyrometry

Sauer, Vinicius M.; Schoegl, Ingmar

doi:10.1016/j.measurement.2019.04.089

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…This methodology overcomes the basic problem of thermography when applied to flames or, in general, gas plumes: the unknown emissivity of the target. This is also a problem in the thermography of solids, but it can be dealt with by assuming a smooth spectral dependence of emissivity, and using Bayesian or regularization methods to solve the coupling between temperature and emissivity effects in spectral radiance [8,9]. The spectral structure of gases, in contrast, is extremely complex, with thousands of discrete absorption-emission lines.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Mid-Infrared Camera System for Accurate Stand-Off Temperature and Column Density Measurements on Flames

Meléndez

Guarnizo

2021

Sensors

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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·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 QCO2 in flames are studied. Both the hyperspectral and multispectral measurement methods are described and applied to a standard flame, with known T and QCO2, and to an ordinary Bunsen flame. Hyperspectral results, based on emission spectra with 0.5 cm−1 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 μm) with a six interference filter wheel. Maps of T and Q obtained by both methods show that, for regions with T ≳1300 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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Section: Introductionmentioning

confidence: 99%