Nonlinear reduction of combustion composition space with kernel principal component analysis

被引：49

作者：

Mirgolbabaei, Hessam ^{[1
]}

Echekki, Tarek ^{[1
]}

机构：

[1] N Carolina State Univ, Dept Mech & Aerosp Engn, Raleigh, NC 27695 USA

来源：

COMBUSTION AND FLAME | 2014年 / 161卷 / 01期

基金：

美国国家科学基金会;

关键词：

Kernel principal component analysis; Principal component analysis; Turbulent nonpremixed flames; ODT;

D O I：

10.1016/j.combustflame.2013.08.016

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

Kernel principal component analysis (KPCA) as a nonlinear alternative to classical principal component analysis (PCA) of combustion composition space is investigated. With the proposed approach, thermochemical scalar's statistics are reconstructed from the KPCA derived moments. The tabulation of the scalars is then implemented using artificial neural networks (ANN). Excellent agreement with the original data is obtained with only 2 principal components (PCs) from numerical simulations of the Sandia Flame F flame for major species and temperature. A formulation for the source and diffusion coefficient matrix for the PCs is proposed. This formulation enables the tabulation of these key transport terms in terms of the PCs and their potential implementation for the numerical solution of the PCs' transport equations. (C) 2013 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

引用

页码：118 / 126

页数：9

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