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arXiv 2108.05415physics.flu-dyn
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基于物理感知神经网络的单喷嘴发动机燃烧不稳定性建模

Physics-Aware Neural Network Flame Closure for Combustion Instability Modeling in a Single-Injector Engine

Zeinab Shadram, Tuan M. Nguyen, Athanasios Sideris, William A. Sirignano

摘要:

在单喷嘴液体火箭发动机大涡模拟中,采用神经网络作为亚网格火焰模型,以取代传统的查表法。NN训练过程提出了一个非凡的挑战。多维燃烧不稳定性问题涉及多尺度长度和非线性声学的非定常流问题中的特征时间,解决瞬态和动态平衡行为,叠加在具有非常窄的移动火焰区域的湍流反应流上。训练数据点之间的精确插值变得至关重要。建议的神经网络的一个主要的新颖的方面是,他们被训练来再现存储在一个小火焰表中的信息的相关部分,仅使用有限的数据从几个CFD模拟的单喷射器液体推进剂火箭发动机在不同的动态配置。这是通过用从燃烧模型的物理特性产生的人为数据丰富训练集,并且还通过包括火焰温度作为被训练以对其他感兴趣的火焰变量进行建模的NN的额外输入来实现的。这些物理感知的NN为基础的封闭模型首先进行离线测试,将它们直接与小火焰表进行比较,然后成功地实施到CFD模拟的小火焰表的地方,并验证各种动态配置。这些测试的结果与基于表格的CFD模拟结果相比,毫不逊色。该方法的计算优势进行了讨论。

英文摘要:

Neural networks (NN) are implemented as sub-grid flame models in a large-eddy simulation of a single-injector liquid-propellant rocket engine with the aim to replace a look-up table approach. The NN training process presents an extraordinary challenge. The multi-dimensional combustion instability problem involves multi-scale lengths and characteristic times in an unsteady flow problem with nonlinear acoustics, addressing both transient and dynamic-equilibrium behaviors, superimposed on a turbulent reacting flow with very narrow, moving flame regions. Accurate interpolation between the points of the training data becomes vital. A major novel aspect of the proposed NNs is that they are trained to reproduce relevant portions of the information stored in a flamelet table by using only limited data from a few CFD simulations of a single-injector liquid-propellant rocket engine under different dynamical configurations. This is made possible by enriching the training set with contrived data resulting from the physical characteristics of the combustion model and also by including the flame temperature as an extra input to the NNs that are trained to model other flame variables of interest. These physics-aware NN-based closure models are first tested offline by comparing them directly with the flamelet table and then are successfully implemented into CFD simulations in place of the flamelet table and verified on various dynamical configurations. The results from those tests compare favorably with counterpart table-based CFD simulations. Computational advantages of the approach are discussed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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