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在飞机发动机的分阶段单部门燃烧器中早期检测热声学不稳定性,使用基于符号动态的方法
Kento Baba1, Sena Kishiya1, Hiroshi Gotoda1
1Department of Mechanical Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
在燃烧器中早期检测热声学不稳定性是通过符号动态和机器学习实现的. 新型不变量,联合象征性复发图 (DJ) 和基于顺序过渡模式的复发图 (DT) 的决定性,确定不稳定的前体.
科学领域:
- 燃烧动态和控制方式
- 非线性动力学和混沌理论
- 机器学习在工程中的应用.
背景情况:
- 热声学不稳定性对燃烧器的运行和安全构成重大风险.
- 早期发现不稳定性对于防止灾难性故障至关重要.
- 传统方法通常依赖于基于值的检测,缺乏预测能力.
研究的目的:
- 开发和验证一种用于早期检测热声学不稳定的新方法.
- 引入两个新的不变量,DJ和DT,用于描述燃烧动态.
- 建立一个机器学习框架,用于识别燃烧中的过渡模式.
主要方法:
- 在分阶段的单部门燃烧器中进行实验研究.
- 符号动态的应用,特别是联合符号复发图 (DJ) 和基于顺序过渡模式的复发图 (DT).
- 使用支向量机和k-means集群来分析DJ和DT的潜在空间.
主要成果:
- 提出的DJ和DT不变量有效地捕捉了声压和热释放率波动之间的相同步.
- 这些不变量是热声学不稳定的前体.
- 机器学习方法成功地区分了稳定燃烧和过渡状态.
结论:
- 符号动态和机器学习的结合方法为早期的热声学不稳定性检测提供了强大的方法.
- DJ和DT不变提供了对不稳定之前的非线性动态有价值的见解.
- 这种方法提高了燃烧器的安全性和运营效率.
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