Perception-Augmented Hierarchical Learning Framework for Modeling Unsteady Combustion Processes in Supersonic Flow

This study proposes a perception-augmented hierarchical learning framework (PAHLF) for reconstructing unsteady combustion processes in scramjets from sparse pressure signals, addressing limitations in capturing transient dynamics under extreme flight conditions. Six Mach 7.0 wind-tunnel tests with kerosene combustion generated synchronized pressure–schlieren datasets across varied equivalence ratios and multiport injections. PAHLF integrates spectral–pixel domain co-augmentation for feature enhancement, a multiscale feature-mapping network for combustion dynamics extraction, and a focused training strategy to resolve shock–flame interactions. Validated against the architectures of Chen et al. (“Intelligent Reconstruction of the Flow Field in a Supersonic Combustor Based on Deep Learning,” Physics of Fluids, Vol. 34, No. 3, 2022, Paper 035128) and Kong et al. (“Data-Driven Super-Resolution Reconstruction of Supersonic Flow Field by Convolutional Neural Networks,” AIP Advances, Vol. 11, No. 6, 2021, Paper 065021) via dual-axis evaluation metrics, PAHLF achieves absolute gains of [Formula: see text] in peak signal-to-noise ratio and [Formula: see text] in structural similarity while reducing parameters by [Formula: see text] (80.9 MB vs [Formula: see text]). The framework demonstrates superior capability in maintaining flowfield structural integrity, precisely mapping combustion intensity, and stabilizing predictions during transient evolution with intense shock oscillations. By enabling high-fidelity visualization of unsteady combustion from minimal sensors, PAHLF establishes new potential for real-time monitoring of hypersonic combustors. This approach advances intelligent modeling of combustion characteristics and provides foundational support for active combustion control through early flame-stability prediction in scramjet propulsion systems.