Detection of Intrinsic Instabilities From Pressure Traces of Spherically Expanding Flames Using Time Series Classification

Abstract

Intrinsic combustion instabilities manifest as wrinkles or cellular structures on spherically expanding flame fronts. They are a fundamental aspect of the physics of flames and can be practically significant for some mixtures, such as those containing hydrogen (H2). In the case of spherical flames, they are studied visually using high-speed cameras, while the use of pressure traces is significantly less explored. This work investigates the use of time series classification (TSC) algorithms for detecting flame wrinkling in spherical flames using pressure traces recorded in a constant volume combustion chamber (CVCC). Some algorithms are observed to have very high classification accuracies (>90%), with 94.5% for the ROCKET (Random Convolutional Kernel Transform) classifier, 92.5% for HIVE-COTE (Hierarchical Vote Collective of Transformation-based Ensembles), and 96.1% for 1-nearest neighbor (1-NN) with DTW (dynamic time warping). The approach potentially provides an alternative methodology for instability detection, particularly where optical access is unavailable.