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Aerospace
Special Issues
Understanding Combustion Instability: A Data-Driven Approach
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Understanding Combustion Instability: A Data-Driven Approach

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2150

Special Issue Editors

Dr. Yu Guan

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Interests: combustion dynamics; data-driven methods
Dr. Jingxuan Li

E-Mail Website
Guest Editor
School of Astronautics, Beihang University, Beijing 100191, China
Interests: combustion instability; feedback control

Special Issue Information

Dear Colleagues,

Combustion instability continues to be a persistent challenge in many power and propulsion systems even today despite numerous efforts made by researchers in the past decades. High-amplitude flow fluctuations generated because of combustion instability can cause significant damage to components, resulting in increased maintenance costs and shortened system lifecycles. In recent years, the application of dynamical system theory has provided new insights into this problem, helping to efficiently model complex system behavior, accurately predict system stability, and effectively design relevant control strategies to suppress combustion instability-induced oscillations. Furthermore, with the rapid development of computing power and the explosion of both experimental and numerical data, machine learning tools have become a hot topic in the combustion community. Machine learning tools have successfully been used in a variety of combustion systems to model complex physical behavior, classify stable/unstable operation conditions, and predict system future behavior. Recent work has demonstrated the effectiveness of a data-driven approach to unlock long-standing and difficult problems related to combustion instability. This Special Issue aims to explore the potential of data-driven approaches, including topics such as nonlinear dynamics, synchronization, complex systems, nonlinear time series analysis, modal analysis, supervised and unsupervised machine learning tools, and their applications in combustion instability.

Dr. Yu Guan
Dr. Jingxuan Li
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. Aerospace 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

  • combustion instability
  • data-driven methods
  • nonlinear dynamics
  • complex systems
  • machine learning

Published Papers (2 papers)

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Research

16 pages, 2213 KiB  
Article
A Discontinuous Galerkin–Finite Element Method for the Nonlinear Unsteady Burning Rate Responses of Solid Propellants
by Zhuopu Wang, Kairui Yu and Yuanzhe Liu
Aerospace 2024, 11(1), 97; https://doi.org/10.3390/aerospace11010097 - 20 Jan 2024
Viewed by 807
Abstract
The unsteady combustion of solid propellants under oscillating environments is the key to understanding the combustion instability inside solid rocket motors. The discontinuous Galerkin–finite element method (DG-FEM) is introduced to provide an efficient yet flexible numerical platform to investigate the combustion dynamics of [...] Read more.
The unsteady combustion of solid propellants under oscillating environments is the key to understanding the combustion instability inside solid rocket motors. The discontinuous Galerkin–finite element method (DG-FEM) is introduced to provide an efficient yet flexible numerical platform to investigate the combustion dynamics of solid propellants. The algorithm is developed for the classical unsteady model, the Zel’dovich–Novozhilov model. It is then validated based on a special analytical solution. The DG-FEM algorithm is then compared with the classical spectral method based on Laguerre polynomials. It is shown that the DG-FEM works more efficiently than the traditional spectral method, providing a more accurate solution with a lower computational cost. Full article
(This article belongs to the Special Issue Understanding Combustion Instability: A Data-Driven Approach)
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14 pages, 5073 KiB  
Article
A Neural Network-Based Flame Structure Feature Extraction Method for the Lean Blowout Recognition
by Puti Yan, Zhen Cao, Jiangbo Peng, Chaobo Yang, Xin Yu, Penghua Qiu, Shanchun Zhang, Minghong Han, Wenbei Liu and Zuo Jiang
Aerospace 2024, 11(1), 57; https://doi.org/10.3390/aerospace11010057 - 07 Jan 2024
Viewed by 895
Abstract
A flame’s structural feature is a crucial parameter required to comprehensively understand the interaction between turbulence and flames. The generation and evolution processes of the structure feature have rarely been investigated in lean blowout (LBO) flame instability states. Hence, to understand the precursor [...] Read more.
A flame’s structural feature is a crucial parameter required to comprehensively understand the interaction between turbulence and flames. The generation and evolution processes of the structure feature have rarely been investigated in lean blowout (LBO) flame instability states. Hence, to understand the precursor features of the LBO flame, this work employed high-speed OH-PLIF measurements to acquire time-series LBO flame images and developed a novel feature extraction method based on a deep neural network to quantify the LBO features in real time. Meanwhile, we proposed a deep neural network segmentation method based on a tri-map called the Fire-MatteFormer, and conducted a statistical analysis on flame surface features, primarily holes. The statistical analysis results determined the relationship between the life cycle of holes (from generation to disappearance) and their area, perimeter, and total number. The trained Fire-MatteFormer model was found to represent a viable method for determining flame features in the detection of incipient LBO instability conditions. Overall, the model shows significant promise in ascertaining local flame structure features. Full article
(This article belongs to the Special Issue Understanding Combustion Instability: A Data-Driven Approach)
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