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...

Velocity Reconstruction in Pool Fires using Physics-Informed Machine Learning

In many flow experiments it is complex to measure all flow states of interest, leading to the need for a method to retrieve unmeasured flow states from measured ones. This work focuses on Hidden Fluid Mechanics (HFM), which refers to a Physics-Informed Neural Network (PINN) able to incorporate the Navier-Stokes (NS) equations into the loss...

The influence of thermal gradients in steel columns due to pool fires

During this thesis the fire safety engineering approach has been used to investigate the influence of hydrocarbon pool fires on steel columns. Using a fire curve which closely resembles a natural fire curve the temperatures occurring in the steel have been investigated using a Computational Fluid Dynamics (CFD) model. The model consists of the...