A data-driven approach to classify combustion regimes in detonation waves is implemented, and a procedure for domain-localized source term modeling based on these classifications is demonstrated. The models were generated from numerical datasets of canonical detonation simulations. In the first phase, delineations of combustion regimes within the detonation wave structure were analyzed through a clustering procedure. The clustering output usefully illuminated distinctions between detonation, deflagration, and intermediary regimes within the wave structure. In the second phase, the resulting delineated fields from the clustering step were used to guide localized source term modeling via artificial neural networks (ANNs), enabling a type of classification-based regression approach for source term estimation. A comparison of the estimations obtained from the local ANNs (trained for a subset of the domain given by a particular cluster) with the global ANN counterparts (trained agnostic to the clustering) showed general improvement of estimations provided by the domain-localized modeling in most cases. Ultimately, this work illuminates the useful role of data-driven classification and regression techniques for both physical analysis of the complex wave structure and for the development of new models which may serve as suitable pathways for long-time simulations of complex combustion systems (such as rotating detonation combustors).

中文翻译：

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爆轰波燃烧过程的数据驱动分类和建模

实施了一种对爆震波中的燃烧状态进行分类的数据驱动方法，并展示了基于这些分类的域局部源项建模程序。这些模型是从典型爆炸模拟的数值数据集生成的。在第一阶段，通过聚类程序分析了爆震波结构内燃烧状态的描述。聚类输出有用地阐明了波结构内爆轰、爆燃和中间状态之间的区别。在第二阶段，从聚类步骤得到的描绘场被用来指导通过人工神经网络 (ANN) 进行局部源术语建模，从而启用一种基于分类的回归方法来估计源术语。从局部人工神经网络（针对特定集群给出的域的子集进行训练）与全局人工神经网络对应物（对集群进行了不可知的训练）获得的估计值的比较显示，在大多数情况下，域本地化建模提供的估计值得到了普遍改进。案件。最终，这项工作阐明了数据驱动的分类和回归技术在复杂波结构的物理分析和开发新模型方面的有用作用，这些模型可以作为复杂燃烧系统（例如旋转爆震燃烧器）。