Neural prediction of combustion instability
Neural prediction of combustion instability
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DOI：
10.1016/s0306-2619(02)00024-7
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发表时间：
2002-06-01
期刊：
影响因子：
11.2
通讯作者：
Pagano, A
中科院分区：
文献类型：
--
作者：
Cammarata, L;Fichera, A;Pagano, A
燃烧不稳定性存在于燃烧室中的自激振荡中，并可引起结构退化。当燃烧不稳定发生时，燃烧过程的特征在于各种非线性现象的耦合，这可能导致极限环或混沌的形成。固有的高度非线性行为，特别是当混沌出现时，对制定良好的预测和设计可靠的控制系统提出了一个主要问题。由于自由度的相关数量和不同现象的非线性耦合，燃烧不稳定性的数学建模在计算上是繁重的，并且可能在模拟数据和实验数据之间产生不令人满意的对应关系。类似的问题也来自于过程参数的不确定性，例如火焰前缘的局部速度（以及因此的反应速率和排气温度）及其不可预测的变化。事实上，请注意，大多数的化学和热物理变量都强烈依赖和影响的瞬时位移的火焰前锋，这是定位在不稳定的涡流外表面。为了获得一个可靠的模型，热声燃烧不稳定性，本文选择了不同的方法。采用广义NARMAX模型对实验燃烧室进行了黑箱辨识。该模型是通过训练一个多层感知器人工神经网络的输入输出实验数据。所提出的方法的主要优点在于神经网络在以快速和简单的方式建模非线性动力学方面的自然能力，以及在处理被建模为输入-输出黑盒的过程的可能性中，对系统具有很少或没有数学信息。结果表明，该模型能较好地预测热声燃烧不稳定性随时间的演化。(C)2002爱思唯尔科技有限公司版权所有。
Combustion instabilities consist in self-exited oscillations in combustion chambers and can cause structure degradations. When combustion instabilities occur, the combustion process is characterised by the coupling of various non-linear phenomena, which can lead to the formation of either a limit cycle or chaos. The intrinsic highly non-linear behaviour, especially when chaos arises, poses a major problem for the formulation of good predictions and the design of reliable control systems. Due to the relevant number of degree of freedom and to the non-linear coupling of different phenomena, the mathematical modeling of combustion instabilities is computationally heavy and may produce an unsatisfactory correspondence between simulated and experimental data. Analogous problems arise also from the uncertainty for the parameters of the process, such as the local velocity of the flame front (and, hence, the reaction rate and the exhausted temperatures), and their unpredictable variations. In fact, note that most of the chemical and thermo-physical variables both strongly depend and influence the instantaneous displacement of the flame front, which is positioned on the unstationary eddies external surface. In order to obtain a reliable model for thermo-acoustic combustion instabilities, a different approach was chosen in this paper. The black-box identification of an experimental combustion chamber was obtained by means of a generalized NARMAX model. The model was implemented by training a Multilayer Perceptron artificial neural network with input-output experimental data. The main advantages of the proposed approach consisted in the natural ability of neural networks in modeling nonlinear dynamics in a fast and simple way and in the possibility to address the process to be modeled as an input-output black box, with little or no mathematical information on the system. Satisfactory agreement between simulated and experimental data was found and results show that the model successfully predicted the temporal evolution of thermo-acoustic combustion instabilities. (C) 2002 Elsevier Science Ltd. All rights reserved.