Mixture fraction enhanced physics-informed neural networks for flow reconstruction in pool fires

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...