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Uncertainty encountered when modelling self-excited thermoacoustic oscillations with artificial neural networks
International Journal of Spray and Combustion Dynamics ( IF 1.6 ) Pub Date : 2017-05-05 , DOI: 10.1177/1756827716687583
Stefan Jaensch 1 , Wolfgang Polifke 1
Affiliation  

Artificial neural networks are a popular nonlinear model structure and are known to be able to describe complex nonlinear phenomena. This article investigates the capability of artificial neural networks to serve as a basis for deducing nonlinear low-order models of the dynamics of a laminar flame from a Computational Fluid Dynamics (CFD) simulation. The methodology can be interpreted as an extension of the CFD/system identification approach: a CFD simulation of the flame is perturbed with a broadband, high-amplitude signal and the resulting fluctuations of the global heat release rate and of the reference velocity are recorded. Thereafter, an artificial neural network is identified based on the time series collected. Five data sets that differ in amplitude distribution and length were generated for the present study. Based on each of these data sets, a parameter study was conducted by varying the structure of the artificial neural network. A general fit-value criterion is applied and the 10 artificial neural networks with the highest fit values are selected. Comparing of these 10 artificial neural networks allows to obtain information on the uncertainty encountered. It is found that the methodology allows to capture the forced response of the flame reasonably well. The validation against the forced response, however, depends strongly on the forcing signal used. Therefore, an additional validation criterion is investigated. The artificial neural networks are coupled with a thermoacoustic network model. This allows to model self-excited thermoacoustic oscillations. If the training time series are sufficiently long, this coupled model allows to predict the trend of the root mean square values of fluctuations of the global heat release rate. However, the prediction of the maximal value of the fluctuation amplitude is poor. Another drawback found is that even if very long-time series are available, the behaviour of artificial neural networks cannot be guaranteed. It is concluded that more sophisticated nonlinear low-order models are necessary.

中文翻译:

使用人工神经网络对自激热声振荡进行建模时遇到的不确定性

人工神经网络是一种流行的非线性模型结构,已知能够描述复杂的非线性现象。本文研究了人工神经网络的功能,该能力可作为根据计算流体动力学(CFD)模拟推导层流火焰动力学的非线性低阶模型的基础。该方法可以解释为CFD /系统识别方法的扩展:火焰的CFD模拟被宽带高振幅信号干扰,并记录了整体放热率和参考速度的波动。此后,基于收集的时间序列来识别人工神经网络。本研究生成了五个振幅分布和长度不同的数据集。基于这些数据集,通过改变人工神经网络的结构进行了参数研究。应用通用拟合值准则,并选择具有最高拟合值的10个人工神经网络。比较这10个人工神经网络可以获取有关遇到的不确定性的信息。发现该方法允许合理地捕获火焰的强制响应。但是,针对强制响应的验证很大程度上取决于所使用的强制信号。因此,研究了一个附加的验证标准。人工神经网络与热声网络模型耦合。这允许对自激热声振荡进行建模。如果训练时间序列足够长,这种耦合模型可以预测总体放热率波动的均方根趋势。但是,对波动幅度的最大值的预测很差。发现的另一个缺点是,即使可以使用很长时间的序列,也无法保证人工神经网络的行为。结论是,需要更复杂的非线性低阶模型。
更新日期:2017-05-05
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