Inverse radiation analysis for simultaneous reconstruction of temperature and volume fraction fields of soot and metal-oxide nanoparticles in a nanofluid fuel sooting flame

Liu, Guannan; Liu, Dong

doi:10.1016/j.ijheatmasstransfer.2017.11.084

Cited by 36 publications

(12 citation statements)

References 78 publications

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“…Possibilities include methods based on spectral emissions [868,869], but it should be considered that these methods need high spatial resolution for applications in counterflow flames.…”

Section: Summary Challenges and Prospectsmentioning

confidence: 99%

Soot formation in laminar counterflow flames

Wang

Chung

2019

Progress in Energy and Combustion Science

360

116

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Many practical soot-emitting combustion systems such as diesel and jet engines rely on diffusion flames for efficient and reliable operation. Efforts to mitigate soot emissions from these systems are dependent on fundamental understanding of the physicochemical pathways leading from fuel to soot in laminar diffusion flames. Existing diffusion flame−based soot studies focused primarily on overventilated coflow flame where the fuel gas (or vapor) issues from a cylindrical tube into a co-flowing oxidizer, and counterflow flame, where a reacting zone is established between two opposing streams of fuel and oxidizer. As a canonical diffusion flame configuration, laminar counterflow diffusion flames have been widely used as a highly controllable environment for soot research, enabling significant progress in the understanding of soot formation for several decades. In view of the possibility of fuel/oxidizer premixing in practical systems, counterflow partially premixed flames have also been studied. In the present work we intend to provide a comprehensive review of the researches on various aspects of soot formation utilizing counterflow flames. Major processes of soot formation (formation of gas phase soot precursors, soot inception and surface reactions, as well as particle-particle interactions) are examined first, with focus on the most recent (post-2010) research progress. Experimental techniques and associated challenges for the measurement of soot-related properties (some knowledge of which is helpful for full appreciation of the experimental data to be reviewed) are then introduced. This is followed by a detailed description of soot evolution in counterflow flames, which is complemented by a discussion on the similarity and differences of the sooting structures between counterflow and coflow diffusion flames. Parametric studies of the effects of fuel molecular structure, fuel additive, partial-premixing, pressure, temperature, stoichiometric mixture fraction, and residence time on soot formation in counterflow flames will then be addressed in detail. This review concludes with a summary of the knowledge and challenges gathered and demonstrated through decades of research, and an outlook on opportunities for future counterflow flame−based soot research towards a more complete understanding of soot formation and the development of novel techniques for soot mitigation in practical combustion devices.

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“…Possibilities include methods based on spectral emissions [868,869], but it should be considered that these methods need high spatial resolution for applications in counterflow flames.…”

Section: Summary Challenges and Prospectsmentioning

confidence: 99%

Soot formation in laminar counterflow flames

Wang

Chung

2019

Progress in Energy and Combustion Science

360

116

View full text Add to dashboard Cite

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“…The attenuation coefficient 𝛽 𝜆 is given by 𝛽 𝜆 = 𝜅 𝑎,𝜆 + 𝜎 𝑠,𝜆 , where 𝜅 𝑎,𝜆 and 𝜎 𝑠,𝜆 are the absorption coefficient and the scattering coefficient, respectively. In this study, the absorption coefficient 𝜅 𝑎,𝜆 = 8 𝑚 −1 is considered according to the property of ethylene diffusion flame [3,7]. The effect of scattering is ignored because it is much smaller than the effect of absorption for a flame [33].…”

Section: Numerical Analysis Of the Spatial Resolution 41 Simulation S...mentioning

confidence: 99%

“…The in-situ measurement of the three-dimension (3-D) flame temperature with a high spatial resolution is crucial for a better understanding of the combustion processes and design of combustion devices [4][5][6]. To achieve nonintrusive measurement of the flame, various techniques have been developed based on either the active interaction between the flame and the emitted signal, such as laser-based or acoustic-based techniques [4,6], or the passive flame radiation such as imaging-based techniques [2,3,[7][8][9]. Although the laser-based or acoustic-based techniques usually have higher measurement accuracy, these techniques have a disadvantage of bulky signal emitting and receiving devices.…”

Section: Introductionmentioning

confidence: 99%

“…The multi-camera scheme can be used for non-symmetric/turbulent flames, which requires multiple cameras to locate around the flame. However, it is difficult to mount the multi-cameras system if the optical access is limited in combustion facilities [4,7,9], such as the gas-turbine combustors and other high-temperature or high-pressure environments [5].…”

Section: Introductionmentioning

confidence: 99%

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Spatial resolution of light field sectioning pyrometry for flame temperature measurement

Liu

Zhu

Wang³

et al. 2021

Optics and Lasers in Engineering

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“…However, the accuracy of retrieved temperatures are highly dependent on initial guesses and "measurement" noise level.The optical diagnostics described so far all deal with a single line-of-sight measurement. Researchers attempted to relax this restriction and focused on axisymmetric flames [24,25], in which optical data were collected at uniformly-spaced, parallel lines-of-sight. These data are related to an unknown radial distribution.…”

mentioning

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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Inverse radiation analysis for simultaneous reconstruction of temperature and volume fraction fields of soot and metal-oxide nanoparticles in a nanofluid fuel sooting flame

Cited by 36 publications

References 78 publications

Soot formation in laminar counterflow flames

Soot formation in laminar counterflow flames

Spatial resolution of light field sectioning pyrometry for flame temperature measurement

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

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