Data-driven Classification and Modeling of Combustion Regimes in Detonation Waves

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).

Automated summary

This research implemented a data-driven approach to classify combustion regimes in detonation waves and demonstrated a procedure for domain-localized source term modeling based on these classifications. Through clustering analysis, distinctions between different combustion regimes within the detonation wave structure were illuminated. By using artificial neural networks, localized source term modeling was guided, leading to improved estimations of source terms.