Analysis of Turbulent Reacting Jets via Principal Component Analysis
Analysis of Turbulent Reacting Jets via Principal Component Analysis
复制标题
通过主成分分析对湍流反应射流进行分析
DOI：
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发表时间：
2020
期刊：
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通讯作者：
A. Parente
中科院分区：
文献类型：
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作者：
G. D’Alessio;A. Attili;A. Cuoci;H. Pitsch;A. Parente
高维数据的解释，如从湍流反应流的直接数值模拟（DNS）获得的数据，构成了科学和工程中最大的挑战之一。尽管这些模拟是推进湍流燃烧知识以及开发和验证建模方法的关键信息来源，但数据的维度通常限制了充分利用数据集中存储的详细和全面信息的机会。主成分分析（PCA）及其局部形式（LPCA）被广泛应用于包括燃烧在内的许多领域。在过去的20年中，它们已被用于燃烧低维流形的识别，数据分析和降阶模型的开发。低维结构，无论是全局还是局部，都可以提供对潜在物理现象的更好见解，并导致高保真模型的制定。本章旨在向读者全面介绍PCA在数据分析中的潜力，首先介绍主要的理论概念，然后通过MATLAB®代码完成所有必要的计算步骤。最后，该方法适用于从DNS的湍流反应非预混正庚烷射流在空气中获得的数据。后者可以被视为一个最佳的情况下，数据分析，因为复杂的物理特性，其特点是顺磁性化学相互作用和烟尘的形成。
The interpretation of high-dimensional data, like those obtained from Direct Numerical Simulations (DNS) of turbulent reacting flows, constitutes one of the biggest challenges in science and engineering. Although these simulations are a source of key information to advance the knowledge of turbulent combustion, as well as to develop and validate modeling approaches, the dimensionality of the data often limits the full opportunity to leverage the detailed and comprehensive information stored in datasets. The Principal Component Analysis (PCA) and its local formulation (LPCA) are widely used in many fields, including combustion. During the last 20 years, they have been used in combustion for the identification of low-dimensional manifolds, data analysis, and development of reduced-order models. Lower dimensional structures, either global or local, can provide better insights on the underlying physical phenomena, and lead to the formulation of high-fidelity models. This chapter aims to offer to the reader a comprehensive introduction of the PCA potential for data analysis, firstly introducing the main theoretical concepts, and then going through all the required computational steps by means of a MATLAB® code. Finally, the methodology is applied to data obtained from a DNS of a turbulent reacting non-premixed n-heptane jet in air. The latter can be regarded as an optimal case for data analysis because of the complex physics characterized by turbulence–chemistry interaction and soot formation.