Determination of the dominant physical processes in downward-propagating flame spread over a solid fuel using machine learning

Abstract

Flame spread over solid fuel (FSS) plays a key role in solid-fuel combustion and fire-related phenomenon. The mechanisms of flame spread over solid fuel are commonly described by means of dimensionless numbers or scaling analysis that describes the balanced relationship of several processes. However, these approaches rely on prior knowledge or explicit assumption of the relevant physical processes, and it is difficult to spatially distinguish among multiple physical processes. This work demonstrated a generalized way using an unsupervised machine learning method based on the Gaussian mixture models and sparse principal component analysis (GMM-SPCA) to automatically delineates the spatial domain of the FSS from a numerical simulation into several regions that are dominated by the balance between different physical processes. The idea of equation space is employed such that each coordinate in the equation space corresponds to a specific physical process as represented by the individual term in the corresponding governing equation. The dominant heat/mass transport processes for both gas and solid phases have been analyzed, and their spatial correspondence for the fields of temperature, flow, and species has been discussed. Some critical characteristics, such as the flame stand-off distance profile, the triple flame structure, and the pyrolysis zone of the solid fuel have been properly identified and quantified. It is demonstrated that the generalized GMM-SPCA method provides an intuitive insight into the heat and mass transfer processes of the FSS for further development of the flame spread model.