Model Reduction of Rich Premixed Hydrogen/air Oscillatory Flames by Global Quasi-Linearization (GQL)

Model Reduction of Rich Premixed Hydrogen/air Oscillatory Flames by Global Quasi-Linearization (GQL)
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DOI:
10.1080/00102202.2020.1869729
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发表时间:
2021-01
影响因子:
1.9
通讯作者:
V. Bykov;Sudhi Shashidharan;E. Berszány;V. Gubernov;U. Maas
V. Bykov;Sudhi Shashidharan;E. Berszány;V. Gubernov;U. Maas
中科院分区:
工程技术4区
文献类型:
--
作者:
V. Bykov;Sudhi Shashidharan;E. Berszány;V. Gubernov;U. Maas

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本文采用化学动力学模型降阶的全局拟线性化(GQL)方法,描述了富氢-空气火焰热扩散振荡开始的非常敏感的区域。这种类型的火焰传播具有化学反应、普通扩散和热扩散的复杂相互作用。对于建模和计算来说,这都是一个非常具有挑战性的现象。因此,选择该模型作为GQL模型降阶方法的测试用例。结果表明,所得到的自燃问题的GQL矩阵可用于研究预混燃烧系统的扩散热不稳定性。为了证明该方法是通用的,在一些众所周知和已建立的机制上测试了GQL基的不变性。结果表明,基于GQL基的低维(4D)慢运动流形能够成功地描述丰富的氢气-空气火焰,并再现了火焰脉动起始和火焰传播振荡区域特性的所有重要特征。此外,基于GQL简化模型对关键参数的估计,例如,脉动开始的临界压力,如果与详细的化学动力学机制之间的差异相比,表现出明显更好的性能。
ABSTRACT The Global Quasi-Linearization (GQL) method for model reduction of chemical kinetics is applied to describe the very sensitive regime of the onset of the thermal-diffusion oscillations of the rich hydrogen-air flames. This type of flame propagation is characterized by a complex interaction of chemical reaction, ordinary diffusion, as well as thermal diffusion. It represents a very challenging phenomenon with respect to both modeling and computation. Therefore, it is chosen as a test case for the GQL model reduction method. We show that the GQL matrix obtained for the auto-ignition problem can be applied to study the diffusive-thermal instabilities of premixed combustion systems. In order to demonstrate that the method is generic, the invariance of the GQL basis is tested on a number of well-known and established mechanisms. The results show that the GQL basis-based low-dimensional (4D) manifolds of slow motions can successfully describe rich hydrogen-air flames and reproduce all important characteristics for both the onset of pulsations and the properties of oscillatory regimes of the flame propagation. Moreover, estimations of critical parameters, e.g., critical pressure for the onset of pulsations, based on the GQL reduced models, show significantly better performance if compared to the differences between detailed mechanisms of chemical kinetics.