Flame Reconstruction

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 (31 January 2024) | Viewed by 2000

Special Issue Editors

China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai, China
Interests: laser diagnostics of flow and combustion; computational imaging
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia
Interests: computational fluid dynamics; combustion; pyrolysis; flame retardant material; fire safety science; molecular dynamics
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia
Interests: computational fluid dynamics; soot modelling; fire modelling; population balance model; combustion

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute papers on measurement, numerical simulation and/or computational imaging of 2D and 3D structures of flames, the structure can be characterized by the distribution of species concentration, distribution of temperature, velocity, luminosity and other spectroscopic characters, etc. A detailed reconstruction of the 2D and 3D structure of flames is fundamental for validating numerical models and understanding the complex combustion-flow interactions. While visualization and reconstruction of flames have been an area of interest for a few decades, recent advances in optical diagnostics and computational imaging have both promoted the development of this area. Therefore, this Special Issue aims at publishing a collection of recent work on new experimental, numerical, theoretical or computational techniques for flame reconstruction.

This Special Issue aims to gather recent advances and novel contributions from academic researchers and industry practitioners on the vibrant topic of flame reconstruction. In addition, this Special Issue welcomes relevant researchers to discuss the latest developments in the feasibility of new techniques for experimental and computational reconstruction of the flame structure. We welcome original research and review articles.

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

  • 2D/3D or line of sight measurement or simulation of flame temperature, velocity, the concentration of key species such as OH, CH2O, soot, flame luminosity and spectroscopic characters, etc.
  • Tomographic reconstruction technique for laminar and turbulent flames
  • Measurement combined with computational imaging techniques
  • Deep learning-based flame imaging and reconstruction technique
  • Structure of hydrocarbon or non-hydrocarbon flames

We look forward to receiving your contributions.

Dr. Dong Xue
Dr. Timothy Bo Yuan Chen
Dr. Cheng Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fire is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • 2D/3D reconstruction
  • species concentration
  • velocity field
  • temperature field
  • spectroscopic characters
  • computational imaging
  • turbulent flames

Published Papers (1 paper)

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Research

14 pages, 11187 KiB  
Article
Object Detection through Fires Using Violet Illumination Coupled with Deep Learning
by Haojun Zhang, Xue Dong and Zhiwei Sun
Fire 2023, 6(6), 222; https://doi.org/10.3390/fire6060222 - 31 May 2023
Viewed by 1376
Abstract
Fire accidents threaten public safety. One of the greatest challenges during fire rescue is that firefighters need to find objects as quickly as possible in an environment with strong flame luminosity and dense smoke. This paper reports an optical method, called violet illumination, [...] Read more.
Fire accidents threaten public safety. One of the greatest challenges during fire rescue is that firefighters need to find objects as quickly as possible in an environment with strong flame luminosity and dense smoke. This paper reports an optical method, called violet illumination, coupled with deep learning, to significantly increase the effectiveness in searching for and identifying rescue targets during a fire. With a relatively simple optical system, broadband flame luminosity can be spectrally filtered out from the scattering signal of the object. The application of deep learning algorithms can further and significantly enhance the effectiveness of object search and identification. The work shows that this novel optics–deep learning combined method can improve the object identification accuracy from 7.0% with the naked eye to 83.1%. A processing speed of 10 frames per second can also be achieved on a single CPU. These results indicate that the optical method coupled with machine learning algorithms can potentially be a very useful technique for object searching in fire rescue, especially considering the emergence of low-cost, powerful, compact violet light sources and the rapid development of machine learning methods. Potential designs for practical systems are also discussed. Full article
(This article belongs to the Special Issue Flame Reconstruction)
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