Sooting Flame Diagnostics and Modeling

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Mathematical Modelling and Numerical Simulation of Combustion and Fire".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 4444

Special Issue Editors


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Guest Editor
School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing, China
Interests: flame diagnostics; soot formation and control

E-Mail Website
Guest Editor
School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: flame spectral diagnostics; 3D computation imaging; soot formation modeling

Special Issue Information

Dear Colleagues,

Extremely detrimental effects on human health and climate change have been linked to emissions of soot particles from combustion systems. Therefore, it is important to fully understand the soot formation and consumption process. However, the study of soot production has still been recognized as a challenge, subject to the advanced diagnostic techniques and also highly affected by several factors such as pressure, additives, fuel chemistry, and flame configuration.

Thus, we are pleased to invite you to contribute to this Special Issue of Fire, entitled ‘Sooting Flame Diagnostics and Modeling’. Studies about the soot production process in the sooting flame using effective diagnostic techniques and numerical modeling approaches are highly welcomed.

This Special Issue aims to provide insights into the latest progress in flame diagnostics and modeling approaches relevant to sooting flame combustion. We are interested in advanced measurements techniques, such as laser-based, radiation-based, etc. The measured properties include the temperature field, soot particles and key species distributions, etc. We also welcome studies that establish or modify prediction models of soot formation processes based on experiments, simulations, and theoretical analyses.

For this Special Issue, original research articles and reviews are welcomed. Research areas may include (but are not limited to) the following:

  • Sooting flame diagnostic techniques (laser-based, radiation-based, etc.);
  • Characterization of sooting flame (intensity, temperature, soot particles, species concentrations, etc.);
  • Reconstruction of measured sooting flame characteristics;
  • Sooting flame modeling and simulation.

We look forward to receiving your contributions.

Prof. Dr. Dong Liu
Dr. Tianjiao Li
Guest Editors

Manuscript Submission Information

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Keywords

  • flame diagnostics
  • flame modeling
  • flame simulation
  • reconstruction
  • soot formation mechanism
  • temperature field
  • soot particles
  • species concentration

Published Papers (3 papers)

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Research

18 pages, 4870 KiB  
Article
Reconstruction Method of 3D Turbulent Flames by Background-Oriented Schlieren Tomography and Analysis of Time Asynchrony
by Peng Gao, Yue Zhang, Xiaoxiao Yu, Shikui Dong, Qixiang Chen and Yuan Yuan
Fire 2023, 6(11), 417; https://doi.org/10.3390/fire6110417 - 27 Oct 2023
Viewed by 1402
Abstract
Background-oriented Schlieren tomography (BOST) is widely used for 3D reconstruction of turbulent flames. Two major concerns are associated with 3D reconstruction. One is the time asynchrony within the data acquisition of the high-speed camera. The other is that the ray tracing process requires [...] Read more.
Background-oriented Schlieren tomography (BOST) is widely used for 3D reconstruction of turbulent flames. Two major concerns are associated with 3D reconstruction. One is the time asynchrony within the data acquisition of the high-speed camera. The other is that the ray tracing process requires significant computational consumption. This study proposes a ray tracing optimization method based on the k-d tree. The study results show that the average search nodes for each ray are only 0.018% of 3D flame with 3.07 million grid nodes. In addition, a parameter estimation method of the unknown azimuth power spectrum function is proposed. First, a typical Sandia turbulent jet diffusion flame dataset was built and validated accordingly, with experiments. The algorithm’s applicability to the 3D reconstruction of temperature and density fields is discussed on this basis. The root-mean-square error (RMSE) of the cross-section density for 3D reconstruction is below 0.1 kg/m3. In addition, the RMSE of the cross-section temperature is below 270 K. Finally, an uncertainty analysis of the flame reconstruction based on a physical model is performed by optimizing the ray tracing method. For the time asynchronous variance of 1 ms, the density uncertainty of the 3D reconstruction is below 1.6 × 10−2 kg/m3, and the temperature uncertainty is below 70 K. The method can provide an essential basis for the design of BOST systems and the 3D reconstruction of turbulent flames. Full article
(This article belongs to the Special Issue Sooting Flame Diagnostics and Modeling)
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14 pages, 5713 KiB  
Article
Numerical and Experimental Reconstruction of Temperature Distribution and Soot Concentration Field for Thin-Slice Flames Using a Charge Coupled Device Camera
by Mingfei Chen, Yun Chen, Tianjiao Li and Dong Liu
Fire 2023, 6(10), 392; https://doi.org/10.3390/fire6100392 - 13 Oct 2023
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Abstract
This work proposed a novel model to simultaneously reconstruct temperature distribution and soot volume fraction field for two-dimensional thin-slice flames using the knowledge of monochromatic radiation intensities at two wavelengths using a CCD camera. The deduction process and numerical analysis of the model [...] Read more.
This work proposed a novel model to simultaneously reconstruct temperature distribution and soot volume fraction field for two-dimensional thin-slice flames using the knowledge of monochromatic radiation intensities at two wavelengths using a CCD camera. The deduction process and numerical analysis of the model were described. Effects of wavelength combinations and measurement errors on reconstruction accuracy were considered in detail. Numerical results have proven the model’s accuracy and showed that the temperature and soot volume fraction fields can be reconstructed well even with noisy input data from flame radiation. In addition, a series of experiments were conducted on a mesoscale combustor to obtain the real thin-slice flames for further experimental reconstruction via the model. The experimental results indicated that the proposed model can successfully reconstruct the flame temperature distribution and soot volume fraction field and the main features of thin-slice flames also can be reasonably reproduced. Full article
(This article belongs to the Special Issue Sooting Flame Diagnostics and Modeling)
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20 pages, 7762 KiB  
Article
Numerical Simulation of Soot Formation in Ethylene Laminar Diffusion Flame
by Xiu-Yan Gao, Fan Yang, Chuan-Xin Zhang, Qi-Xiang Chen and Yuan Yuan
Fire 2023, 6(8), 316; https://doi.org/10.3390/fire6080316 - 14 Aug 2023
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Abstract
The soot produced by fossil fuel combustion affects climate and human health, and the ethylene laminar flame is a crucial research object of soot generation. After verifying the accuracy of the numerical calculation model by comparing experimental data, the impact of changes in [...] Read more.
The soot produced by fossil fuel combustion affects climate and human health, and the ethylene laminar flame is a crucial research object of soot generation. After verifying the accuracy of the numerical calculation model by comparing experimental data, the impact of changes in inlet flow rate and fuel flow composition operating conditions on the generation of soot were compared and analyzed. The calculated results obtained are consistent with the experimental data in terms of distribution trend. The deviation of the calculated peak integral smoke volume fraction is only 5%. Under the operating conditions set in this study, increasing the volume flow rate of the accompanying air will increase the volume fraction of soot generated by the ethylene laminar diffusion flame. Increasing the fuel volume flow rate will first increase and then decrease the volume fraction of soot. Full article
(This article belongs to the Special Issue Sooting Flame Diagnostics and Modeling)
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