Data-Driven Surrogates of Rotating Detonation Engine Physics with Neural ODEs and High-Speed Camera Footage

Abstract

Interacting multi-scale physics present in the Rotating Detonation Engine lead to diverse nonlinear dynamical behavior, including combustion wave mode-locking, modulation, and bifurcations. In this work, surrogate models of the RDE physics, including combustion, injection, and mixing, are sought that can reproduce the observed behavior through their interactions. These surrogate models are constructed and trained within the context of Neural ODEs evolving through the latent representation of the waves: the traveling wave coordinate $\xi =x-ct+a$. Shown is that the multi-scale nature of the physics can be successfully separated and analyzed separately, providing valuable insight into the fundamental physical processes of the RDE.

References in corpus (7)

• Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders
• DiffEqFlux.jl - A Julia Library for Neural Differential Equations
• Modeling Thermodynamic Trends of Rotating Detonation Engines
• Mode-Locked Rotating Detonation Waves: Experiments and a Model Equation
• Linear stability analysis of detonations via numerical computation and dynamic mode decomposition
• Multi-scale Physics of Rotating Detonation Engines: Autosolitons and Modulational Instabilities
• Data-Driven Modeling of Nonlinear Traveling Waves