Analysis of Reliability of Spectral Water Vapor Line Parameters at High Temperatures in Spectroscopic Databases View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-05-31

AUTHORS

O. K. Voitsekhovskaya, O. V. Egorov, D. E. Kashirskii

ABSTRACT

Parameters of high-temperature water vapor spectral lines presented in the well-known spectroscopic databases are discussed. As a reliability criterion for parameter values, recent experimental data on the transmittance measured with spectral resolution of 1 cm–1 at Т ~ 1000 K are employed. As a result of our analysis, 156 spectral lines were revealed (12 of them were found previously) in the HITEMP2010 database the use of which in calculations of the transmittance leads to considerable deviations (≥20%) from the available experimental data. The line intensities determined by this method exceed by 3 times and more the data presented in other spectroscopic bases. Additionally, the line intensities from the HITEMP2010 database in the spectral range 10–6000 cm–1 are completely compared with the result of semiempirical calculation at the high temperature available from the literature. The hot bands for which the relative deviation of the integral intensities exceeds 10% at T = 1302 K are identified. It is established that as a whole, the most reliable integral hot band intensities are contained in the HITEMP2010 database. This is confirmed by their least deviations from the measured transmittance at high temperatures. As a result, it is demonstrated that to obtain the best coincidence (Δav = 2.64%) with the measured data in the range 1000–2000 cm–1 in calculation based on the HITEMP2010 database, the parameter values recommended in the present work for lines with overestimated intensities and different distribution of the temperature coefficients must be used. More... »

PAGES

261-272

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11182-017-1070-2

DOI

http://dx.doi.org/10.1007/s11182-017-1070-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085731481


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