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 3220
Special Issue Editors
Interests: combustion dynamics; data-driven methods
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
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Keywords
- combustion instability
- data-driven methods
- nonlinear dynamics
- complex systems
- machine learning
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