Department of Mechanical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
Japan Aerospace Exploration Agency, 7-44-1 Jindaiji-Higashimachi, Chofu-shi, Tokyo 182-8522, Japan.
Chaos. 2023 Jul 1;33(7). doi: 10.1063/5.0140854.
We experimentally conduct an early detection of thermoacoustic instability in a staged single-sector combustor using a novel methodology that combines symbolic dynamics and machine learning. We propose two invariants in this study: the determinisms of the joint symbolic recurrence plots DJ and the ordinal transition pattern-based recurrence plots DT. These invariants enable us to capture the phase synchronization between acoustic pressure and heat release rate fluctuations associated with a precursor of thermoacoustic instability. The latent space consisting of DJ and DT, which is obtained by a support vector machine in combination with the k-means clustering method, can appropriately determine a transitional regime between stable combustion and thermoacoustic instability.
我们使用一种结合符号动力学和机器学习的新方法，在分段单腔燃烧器中进行热声不稳定性的早期检测实验。我们在这项研究中提出了两个不变量：联合符号递归图 DJ 的确定性和基于顺序转换模式的递归图 DT 的确定性。这些不变量使我们能够捕捉与热声不稳定性前兆相关的声压和热释放率波动之间的相位同步。通过支持向量机与 k-均值聚类方法相结合得到的由 DJ 和 DT 组成的潜在空间，可以适当确定稳定燃烧与热声不稳定性之间的过渡状态。
