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Early detection of thermoacoustic combustion oscillations using a methodology combining statistical complexity and
Takayoshi Hachijo1, Shinga Masuda1, Takuya Kurosaka1
1Department of Mechanical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
Chaos (Woodbury, N.Y.)
|November 3, 2019
Summary
Early detection of combustion oscillations is possible using statistical complexity and machine learning. This method effectively identifies precursors to thermoacoustic instability by analyzing pressure fluctuations.
Area of Science:
- Thermodynamics and Combustion Science
- Nonlinear Dynamics and Chaos Theory
- Machine Learning and Data Analysis
Background:
- Thermoacoustic combustion oscillations pose significant risks in various engineering applications.
- Early detection of these oscillations is crucial for preventing system failure and ensuring operational safety.
- Characterizing intermittent and chaotic combustion dynamics presents a significant challenge.
Purpose of the Study:
- To develop and validate a novel method for the early detection of thermoacoustic combustion oscillations.
- To characterize the intermittent nature of combustion oscillations, including transitions between aperiodic and periodic states.
- To leverage statistical complexity and machine learning for precursor identification.
Main Methods:
- Experimental investigation of combustion oscillations.
- Application of statistical complexity measures to analyze pressure fluctuations.
- Utilizing machine learning algorithms, specifically Support Vector Machines (SVM), for classification.
- Employing the complexity-entropy causality plane for dynamic state characterization.
Main Results:
- The complexity-entropy causality plane effectively captured subtle changes in combustion states during transitions.
- Aperiodic, small-amplitude pressure fluctuations were identified as chaotic.
- The feature space derived from the complexity-entropy causality plane demonstrated potential for precursor detection.
- The study successfully characterized intermittent combustion oscillations.
Conclusions:
- The combined approach of statistical complexity and machine learning offers a promising tool for early detection of combustion oscillations.
- The complexity-entropy causality plane is effective in identifying precursors to thermoacoustic instability.
- This method has the potential to enhance the safety and reliability of combustion systems.
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