Principal component analysis coupled with nonlinear regression for chemistry reduction
Principal component analysis coupled with nonlinear regression for chemistry reduction
复制标题
DOI：
10.1016/j.combustflame.2017.08.012
复制
发表时间：
2018-01-01
影响因子：
4.4
通讯作者：
Parente, Alessandro
中科院分区：
文献类型：
--
作者：
Malik, Mohammad Rafi;Isaac, Benjamin J.;Parente, Alessandro
为了准确地模拟燃烧系统，需要较大的动力学机构。如果不使用热化学状态空间的参数化，则由于所产生的系统涉及广泛的时间和长度尺度，因此物质传输方程的求解在计算上可能变得困难。将热化学状态空间的参数化为基础流形尺寸的先验规定将导致简化但准确的描述。为此，主成分分析(PCA)在识别低维流形方面提供的潜力是非常有吸引力的。本工作旨在通过分析使用非线性回归的主成分分析方法对热化学态进行参数化的能力来促进对PC-TRANSPORT方法的理解和应用。为了在数值求解器中验证该方法的准确性，用PC输运方法对非定常完全搅拌反应器(PSR)进行了计算。PSR分析将之前的研究扩展到更复杂的燃料(甲烷和丙烷)，显示了该方法处理相对较大的动力学机制的能力。还展示了通过基于高斯过程的非线性回归来实现高精度映射的能力。此外，还研究了一种基于PC源项局部回归的新方法，改进了结果。(C)2017年，燃烧研究所。爱思唯尔公司出版，版权所有。
Large kinetic mechanisms are required in order to accurately model combustion systems. If no parameterization of the thermo-chemical state-space is used, solution of the species transport equations can become computationally prohibitive as the resulting system involves a wide range of time and length scales. Parameterization of the thermo-chemical state-space with an a priori prescription of the dimension of the underlying manifold would lead to a reduced yet accurate description. To this end, the potential offered by Principal Component Analysis (PCA) in identifying low-dimensional manifolds is very appealing. The present work seeks to advance the understanding and application of the PC-transport approach by analyzing the ability to parameterize the thermo-chemical state with the PCA basis using nonlinear regression. In order to demonstrate the accuracy of the method within a numerical solver, unsteady perfectly stirred reactor (PSR) calculations are shown using the PC-transport approach. The PSR analysis extends previous investigations to more complex fuels (methane and propane), showing the ability of the approach to deal with relatively large kinetic mechanisms. The ability to achieve highly accurate mapping through Gaussian Process based nonlinear regression is also shown. In addition, a novel method based on local regression of the PC source terms is also investigated which leads to improved results. (C) 2017 The Combustion Institute. Published by Elsevier Inc. All rights reserved.