This thesis explores the use of physics-informed neural networks (PINNs) to reconstruct the flow fields in a pool fire flame, a canonical configuration in non-premixed combustion. Due to the difficulty in obtaining adequate experimental characterizations of such flows, reacting flows like pool fires stand in need of novel methods capable of reconstructing the principal flow features from limited measurement data.

The present work extends the capabilities of previous PINN reconstruction frameworks through the addition of a new physical prior that embeds information about the flame structure: the mixture fraction. This passive scalar quantifies the local state of mixing in diffusion flames, playing a crucial role in their analysis.

A novel reconstruction framework that incorporates the mixture fraction has been developed. The mixture fraction is found to be a valuable addition to the reconstruction framework: it either reduces the amount of flow variables required to obtain successful velocity reconstructions, leads to higher reconstruction accuracies, alleviates some of the PINN’s well-known failure modes, or a combination of the above. Furthermore, it allows for adequate first order estimates of the heat release rate (HRR) of the flame even in the absence of pressure, density, and temperature data.