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...

Dynamic wind turbine wake reconstruction: A Koopman-linear flow estimator

A challenging topic arising in dynamic wind turbine wake is modeling, especially the low-order approximation. The central problem is the fact that it has high-dimensional and nonlinear wake characteristics. In this paper, a Koopman-linear flow estimator is designed according to the Koopman operator theory. Different from the conventional flow...

Numerical optimization techniques for practical problems in Aerodynamics

The continuous increase in the number of flights in the last decades caused a steepgrowth of aviation-related pollution to the point that the aviation sector is responsible for3% of the global greenhouse gas emissions. Regulators have been slow at catching up withthis problem, and stringent emission targets have been put in place only very...