Carmen Márquez García

PhD Thesis

Mixing dynamics in turbulent flows with high-density ratios.

Supervisors

Miguel Pérez Encinar (UC3M) & Alberto Vela Martín (UC3M)

Abstract

This project aims to advance the understanding and prediction of turbulent mixing in flows with varying density differences, ranging from uniform mixtures to highly contrasting cases such as hydrogen-air. High-precision pseudo-spectral simulations will be implemented and optimised on GPUs, generating large datasets of turbulent flows. These datasets will enable the characterization of concentration probability density functions (PDFs) across different scenarios, providing key insights into how density contrast affects mixing. Building on this foundation, probabilistic and machine-learning models will be developed to predict the likelihood of reaching concentrations of interest, enhancing the safety and sustainability of carbon-free energy technologies.