Kato Kenta, Hashiba Hiroyuki, Nagao Jun, Gotoda Hiroshi, Nabae Yusuke, Kurose Ryoichi
Department of Mechanical Engineering, <a href="https://ror.org/05sj3n476">Tokyo University of Science</a>, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan.
Department of Mechanical Engineering and Science, <a href="https://ror.org/02kpeqv85">Kyoto University</a>, Kyoto daigaku-Katsura, Nishikyo-ku, Kyoto 615-8540, Japan.
Phys Rev E. 2024 Aug;110(2-1):024204. doi: 10.1103/PhysRevE.110.024204.
We numerically study the dynamic behavior and driving region of spray combustion instability in a backward-facing step combustor using analytical methodologies based on dynamical systems theory, symbolic dynamics, complex networks, and machine learning. The global dynamic behavior of a heat release rate field represents low-dimensional chaotic oscillations with deterministically aperiodic intercycle dynamics. Spray combustion instability is driven in the formation and separation region of a large-scale organized vortex induced by the hydrodynamic shear layer instability at the edge of the backstep. This region corresponds fairly to that of the hub in an acoustic-energy-flux-based spatial network. The feature importance in a random forest is valid for clarifying the feedback coupling of spray combustion instability.
我们使用基于动力系统理论、符号动力学、复杂网络和机器学习的分析方法，对后向台阶燃烧器中喷雾燃烧不稳定性的动态行为和驱动区域进行了数值研究。热释放率场的全局动态行为表现为具有确定性非周期循环间动态的低维混沌振荡。喷雾燃烧不稳定性是由后台阶边缘的流体动力剪切层不稳定性诱导的大规模有组织涡旋的形成和分离区域驱动的。该区域与基于声能通量的空间网络中的枢纽区域相当吻合。随机森林中的特征重要性对于阐明喷雾燃烧不稳定性的反馈耦合是有效的。
