Turbulent premixed flame modeling using artificial neural networks based chemical kinetics
Turbulent premixed flame modeling using artificial neural networks based chemical kinetics
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DOI：
10.1016/j.proci.2008.05.077
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
B. Sen;S. Menon
中科院分区：
文献类型：
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作者：
B. Sen;S. Menon
人工神经网络（ANN）方法作为化学积分器的反应流的大涡模拟（LES）的适用性进行评估，特别强调生成独立的计算感兴趣的训练表。基于反向传播算法的人工神经网络代码开发了一种新的方法，用于自适应地确定相对于误差表面拓扑结构的模型系数。训练表是由独立的火焰研究构成的，训练好的网络用于不同当量比和湍流水平下湍流火焰-涡干扰（FVI）的大涡模拟研究。结果表明，一旦人工神经网络得到良好的训练，它可以成功地预测反应速率的内存和时间效率的方式相比，传统的查找表的方法和僵硬的ODE求解器，分别。
The applicability of Artificial Neural Networks (ANN) approach as a chemistry integrator for Large Eddy Simulations (LES) of reactive flows is evaluated with special emphasis on generating training tables independent of the computation of interest. An ANN code based on back-propagation algorithm is developed with a new approach for self-determining the model coefficients adaptively with respect to the error surface topology. The training table is constructed with an independent flame study, and the trained networks are used in LES of turbulent Flame–Vortex Interaction (FVI) studies at different equivalence ratios and turbulence levels. It is shown that once the ANN is well trained, it can successfully predict the reaction rates in both memory and time efficient manner compared to traditional look-up table approach and stiff ODE solvers, respectively.