Feature extraction and artificial neural networks for the on-the-fly classification of high-dimensional thermochemical spaces in adaptive-chemistry simulations

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
G. D’Alessio, A. Cuoci, A. Parente
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引用次数: 9

Abstract

Abstract The integration of Artificial Neural Networks (ANNs) and Feature Extraction (FE) in the context of the Sample- Partitioning Adaptive Reduced Chemistry approach was investigated in this work, to increase the on-the-fly classification accuracy for very large thermochemical states. The proposed methodology was firstly compared with an on-the-fly classifier based on the Principal Component Analysis reconstruction error, as well as with a standard ANN (s-ANN) classifier, operating on the full thermochemical space, for the adaptive simulation of a steady laminar flame fed with a nitrogen-diluted stream of n-heptane in air. The numerical simulations were carried out with a kinetic mechanism accounting for 172 species and 6,067 reactions, which includes the chemistry of Polycyclic Aromatic Hydrocarbons (PAHs) up to C$ {}_{20} $. Among all the aforementioned classifiers, the one exploiting the combination of an FE step with ANN proved to be more efficient for the classification of high-dimensional spaces, leading to a higher speed-up factor and a higher accuracy of the adaptive simulation in the description of the PAH and soot-precursor chemistry. Finally, the investigation of the classifier’s performances was also extended to flames with different boundary conditions with respect to the training one, obtained imposing a higher Reynolds number or time-dependent sinusoidal perturbations. Satisfying results were observed on all the test flames. Impact Statement The existing methodologies for the simulation of multidimensional flames with detailed kinetic mechanisms are time-consuming because of the large number of involved chemical species and reactions. This aspect has prompted the development of approaches to reduce the computational requirements of computational fluid dynamics simulations of reacting flows. Among them, adaptive chemistry is worth mentioning, as it allows to use complex kinetic mechanisms only where needed. In this work, an artificial neural network architecture with a prior encoding step via Principal Component Analysis was integrated in the Sample-Partitioning Adaptive Reduced Chemistry approach, to increase the on-the-fly classification accuracy when high-dimensional spaces are considered. Its performances were compared with others supervised classifiers, operating on the full thermochemical space, in terms of speed-up with respect to the detailed simulation and accuracy in the description of Polycyclic Aromatic Hydrocarbon species.
自适应化学模拟中高维热化学空间动态分类的特征提取和人工神经网络
摘要:本文研究了在样本划分自适应还原化学方法背景下,人工神经网络(ann)和特征提取(FE)的集成,以提高对非常大的热化学状态的实时分类精度。首先,将所提出的方法与基于主成分分析重构误差的实时分类器以及在全热化学空间运行的标准ANN (s-ANN)分类器进行了比较,用于自适应模拟空气中氮稀释的正庚烷流的稳定层流火焰。数值模拟了172种6067种反应的动力学机制,其中包括C${}_{20} $的多环芳烃(PAHs)的化学反应。在上述分类器中,利用有限元步骤与人工神经网络相结合的分类器对高维空间的分类效率更高,在描述多环芳烃和煤烟前体化学时具有更高的加速因子和更高的自适应模拟精度。最后,对分类器性能的研究还扩展到具有不同边界条件的火焰,这些边界条件相对于训练火焰施加了更高的雷诺数或时变正弦扰动。所有试验火焰均取得了满意的结果。由于涉及的化学物质和反应数量众多,现有的模拟多维火焰的详细动力学机制的方法非常耗时。这方面促使了各种方法的发展,以减少反应流动的计算流体动力学模拟的计算需求。其中,适应性化学值得一提,因为它只允许在需要时使用复杂的动力学机制。在这项工作中,通过主成分分析将具有先验编码步骤的人工神经网络架构集成到样本划分自适应还原化学方法中,以提高在考虑高维空间时的实时分类精度。在多环芳烃物种描述的详细模拟和准确性方面,将其性能与其他在全热化学空间运行的监督分类器进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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