A self-organizing-map approach to chemistry representation in combustion applications
A self-organizing-map approach to chemistry representation in combustion applications
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DOI：
10.1088/1364-7830/4/1/304
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发表时间：
2000-03
影响因子：
1.3
通讯作者：
J. Blasco;N. Fueyo;C. Dopazo;Jing Chen
中科院分区：
文献类型：
--
作者：
J. Blasco;N. Fueyo;C. Dopazo;Jing Chen
文献中提出了几种替代技术，以避免在燃烧过程模拟中对热化学方程进行cpu密集的数值积分。本文介绍了一种基于自组织映射（SOM）和多层感知器（MLP）两种人工神经网络范式的新方法。SOM首先用于热化学空间的子域自动划分。然后，训练一个专门的MLP来拟合属于给定子域的热化学点。该策略在部分搅拌反应器（PaSR）上进行了测试，并取得了令人鼓舞的结果。该方法相对适度的cpu时间和内存要求使SOM-MLP方法成为在复杂应用环境中包含大型化学机制的有前途的技术，例如燃烧的多维模拟。
Several alternative techniques have been proposed in the literature in order to avoid the CPU-intensive numerical integration of the thermochemical equations in the simulation of combustion processes. The present paper introduces a new approach, which is based on two artificial neural-network (ANN) paradigms, namely the self-organizing map (SOM) and the multilayer perceptron (MLP). The SOM is first employed for the automatic partitioning of the thermochemical space into subdomains. Then, a specialized MLP is trained in order to fit the thermochemical points belonging to a given subdomain. The presented strategy is tested on a partially stirred reactor (PaSR) with a reduced methane-air mechanism, and encouraging results are reported. The relatively modest CPU-time and memory requirements of the method make the SOM-MLP approach a promising technique for the inclusion of large chemical mechanisms in the context of complex applications, such as the multidimensional simulation of combustion.