Medium Resolution Transmission Measurements of Water Vapor at High Temperature

Bharadwaj, Sudarshan P.; Modest, Michael F.; Riazzi, Robert J.

doi:10.1115/1.2165209

Cited by 27 publications

(10 citation statements)

References 17 publications

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“…The experimental errors on the measured transmissivities are analyzed following the procedure described in Refs. [14][15][16]. The measured NB absorption coefficient is defined as:…”

Section: Experimental Uncertaintiesmentioning

confidence: 99%

“…A NB database of k-distributions for carbon monoxide, carbon dioxide and water vapour can be generated [5-7, 9, 10] from HITEMP [12] and CDSD-1000 [13] spectroscopic databases, which were found to provide a good description of the line data over the range of temperatures encountered in combustion applications [14][15][16]. In order to apply this database to pool fire simulations it should be extended by adding k-distributions of fuels.…”

Section: Introductionmentioning

confidence: 99%

“…HITRAN 2004 [17] was used to generate k-distributions for methane and ethylene [18,19]. However, HITRAN was mainly developed for atmospheric applications and is expected to become inaccurate at temperatures higher than 600 K [10][11][12][13][14][15][16][17][18][19][20][21][22]. In addition, only a limited number of hydrocarbon fuels, such as methane, methanol, ethane, ethylene, and acetylene, is available in HITRAN.…”

Section: Introductionmentioning

confidence: 99%

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A database of narrow-band parameters for fuels commonly encountered in fire applications

Consalvi

Liu

2015

Fire Safety Journal

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International audienceThe objective of this paper is to derive narrow band (NB) parameters for the Malkmus statistical narrow band model from the medium resolution transmission measurements carried out at NIST (Wakatsuki et al., Proc. Combust. Inst., vol. 31, 2007, pp. 2573-2580) up to about 1000 K for nine fuels commonly encountered in fire applications, namely methane, methanol,,ethane, ethylene, propane, propylene, heptane, methyl methacrylate and toluene. These NB parameters can be used to generate a database of NB k-distributions of these fuels in order to apply the k-distribution methods to model their contributions to radiative heat transfer. The accuracy of this NB database, the EM2C NB database for methane (Perrin and Soufiani, JQSRT, vol. 103, 2007, pp. 3-13), and HITRAN 2012 for methane, methanol, ethane, and ethylene is assessed by comparison with the measured medium resolution transmissivities and spectrally-integrated radiative properties. Results show that the EM2C database is the most accurate for methane over the entire range of the temperature and the fuel partial pressure path length considered. The line parameters in HITRAN 2012 are not available for some important bands of methanol, ethane and ethylene and this spectroscopic database cannot be used with confidence above 600 K. The Malkmus narrow band database derived in the present study is found to reproduce spectrally-integrated radiative properties with error less than 20%, which suggests that it can be used for engineering applications. Finally, applications to radiation calculations along lines of sight with methane concentrations and temperatures representative of pool fires show that both the present Malkmus narrow band and the NIST databases can predict accurately the radiative intensity for small scale pool fires where as discrepancies of the order of 30% are expected in the case of methane pool fires with a heat release rate of 176 kW. (C) 2015 Elsevier Ltd. All rights reserved

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“…The experimental errors on the measured transmissivities are analyzed following the procedure described in Refs. [14][15][16]. The measured NB absorption coefficient is defined as:…”

Section: Experimental Uncertaintiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A database of narrow-band parameters for fuels commonly encountered in fire applications

Consalvi

Liu

2015

Fire Safety Journal

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“…HITEMP 2010 [53] is limited to only 5 species (CO 2 , H 2 O, CO, NO and OH), but contains data for "hot lines," which become active at high temperature. The database was extensively tested against measured medium-to high-resolution FTIR spectra of CO 2 [56,57,58], H 2 O [59,60], and CO [61] at relevant combustion conditions, and can be used to fulfill this task.…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

et al. 2019

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Inversion of temperature and species concentration distributions from radiometric measurements involves solving nonlinear, ill-posed and high-dimensional problems. Machine Learning approaches allow solving such highly nonlinear problems, offering an alternative way to deal with complex and dynamic systems with good flexibility. In this study, we present a machine learning approach for retrieving temperatures and species concentrations from spectral infrared emission measurements in combustion systems. The training spectra for the machine learning model were synthesized through calculations from HITEMP 2010 for gas mixtures of CO 2 , H 2 O, and CO. The method was tested for different line-of-sight temperature and concentration distributions, different gas path lengths and different spectral intervals. Experimental validation was carried out by measuring spectral emission from a Hencken flat flame burner with a Fourier-transform infrared spectrometer with different spectral resolutions. The temperature fields above the burner for combustion with equivalence ratios of φ =1, φ = 0.8, and φ = 1.4 were retrieved and were in excellent agreement with temperatures deduced from Rayleigh scattering thermometry. performed to address the inverse radiation problems using gradient-based [14] optimization methods. Griffith et al. [15,16] were the first to recognize that measurements of the transmissivity or emissivity of rotational spectral lines of a gas can reveal its temperature. In order to extract temperature, a nonlinear least-square method was used to fit the integrated transmissivity minima. Best et al. [17,18] combined tomography and Fourier transform infrared (FTIR) spectrometer transmission and emission spectra to extract temperature, concentration and soot volume fraction fields. By measuring spectral intensity of the CO 2 4.3 µm band, temperature profiles were retrieved in a number of ways [19,20]. At their time these results were not accurate enough due to lack of an accurate radiation prediction model and robust inverse algorithms. Song et al. [21,22] developed a spectral remote sensing technique to reconstruct CO 2 temperature profiles based on radiative intensity measurements. An accurate narrow band radiation model was employed and several Newton-type regression methods were tested. Due to the nonlinearity and ill-posedness of the problem, regularization of the inverse problems was applied to enforce some degree of smoothness to the solution. It is always difficult to select an appropriate regularization parameter and empirical values for the regularization parameter were employed. Ren and Modest [23] applied the Levenberg-Marquardt optimization method with Tikhonov regularization to reconstruct CO 2 temperature profiles and average concentrations from synthetic line-of-sight spectral intensity data. Two types of temperature profiles were tested for different gas path lengths and different CO 2 spectral bands. A new regularization selection method based on the combination of the L-curve criterion and the discre...

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“…The absorption coefficients, which are required to calculate transmissivity, were determined from HITEMP 2010, the high-temperature molecular spectroscopic database [52]. The database has been extensively tested against measured FTIR spectra of CO 2 [53][54][55] and H 2 O [56,57]. Good agreement between measured and calculated spectra was found.…”

Section: Introductionmentioning

confidence: 99%

Optical determination of temperature and concentrations of homogeneous turbulent gas mixtures

Ren

Modest

2017

International Journal of Heat and Mass Transfer

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In this study, we present an inverse radiation model to reconstruct time-averaged temperature, species concentrations and their root mean square (rms) values for turbulent gas mixtures, based on line-of-sight spectral transmissivity measurements. Time-averaged transmissivities and their rms values are successfully related to timeaveraged temperatures, species concentrations and their rms values by considering interactions between turbulence and radiation (TRI). The turbulence length scale is retrieved simultaneously with the turbulent scalars. Optimal wavenumber ranges for retrieving temperature and species concentrations of the gas mixtures are discussed. To validate the models, measured spectra are synthesized by calculations from HITEMP 2010 for turbulent gas mixtures of CO 2 , H 2 O and CO.

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Medium Resolution Transmission Measurements of Water Vapor at High Temperature

Cited by 27 publications

References 17 publications

A database of narrow-band parameters for fuels commonly encountered in fire applications

A database of narrow-band parameters for fuels commonly encountered in fire applications

Machine learning applied to retrieval of temperature and concentration distributions from infrared emission measurements

Optical determination of temperature and concentrations of homogeneous turbulent gas mixtures

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