Data-driven surrogates of rotating detonation engine physics with neural ordinary differential equations and high-speed camera footage

Abstract

Interacting multi-scale physics in the Rotating Detonation Engine (RDE) lead to diverse nonlinear dynamical behavior, including combustion wave mode-locking, modulation, and bifurcations. Here, surrogate models of the RDE physics, including combustion, injection, and mixing, are sought that can reproduce mode-locked combustion waves through their interactions. These surrogate models are constructed and trained within the context of neural ordinary differential equations evolving through the latent representation of the waves: the traveling wave coordinate ξ=x−ct+a. It is shown 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.