Intrusive and Non-intrusive Non-linear Reduced-order Models for Reacting Flows

Reduced-order models (ROMs) offer methods to learn low-dimensional, inexpensive representations of a system's dynamics from a small number of high-fidelity simulations. However, traditional projection-based ROMs, which project the governing equations onto a linear subspace, are typically inadequate to produce accurate models of combusting flows. Artificial neural networks show promise in generating more accurate ROMs for advection-dominated flows by learning a non-linear manifold on which the system dynamics can be described more compactly than on a linear subspace. In particular, autoencoder (AE) ROMs offer a means of learning such non-linear manifolds in an unsupervised fashion. In this work, we develop and apply AE ROMs for advection-dominated reacting flows to create low-cost approximations of reacting flow dynamics. We investigate whether intrusive AE ROMs, which require access and modifications to the governing equations via a numerical solver, provide superior performance over non-intrusive AE ROMs, which only require snapshots of the unsteady flow fields.