Computational Fluid Dynamics (CFD) is a relatively new tool in the scientific community compared to
classical fluid mechanics and dynamics. The continuously increasing computational capabilities of
modern hardware, make solving equations such as the Navier Stokes, with iterative solvers, a common
day occurrence for even a mere laptop. However, many flow phenomena remain very hard to model
accurately, turbulence being one of them [1]. Many relatively successful models exist today for
modelling turbulence using Reynolds Averaged Navier Stokes (RANS) and Large Eddy Simulation (LES),
which is why many of these techniques are used to significantly speed up the design process in
industrial settings from aeroplanes to automobiles. Combustion and flame propagation is another
area where CFD is expected to be the cornerstone for future development. With a clear shift towards
sustainability, Hydrogen combustion is at the forefront of energy generation and propulsion due to its
advantage of not producing CO2 [2].
Unfortunately, despite H2 being the simplest molecule, its combustion process is anything but that. It
is strongly affected by thermodiffusive instabilities that produce a range of non-linearities in flame
front propagation when coupled with turbulence [3],[4]. These non-linearities are critical to
understand so that the use of Hydrogen in Gas Turbine (GT) and Internal Combustion Engines (ICE) is
performed as efficiently as possible to maximise its energy output as well as ensure their safe
operation. This is necessary since the energy stored in a given volume of hydrogen is much less than
that of fossil fuels, making efficiency, the designer's number one priority.
The complex 3D phenomena of thermodiffusive instability, and its coupling with turbulence, produce
an increased flame speed and flame wrinkling, heavily dependent on the variation of local reaction
rates. This means that, in the turbulent regime, the flame is highly irregular which is due to the
combined contributions of turbulence and hydrogen's thermodiffusive instabilities. This produces
tongue-like structures which penetrate in the unburned gas area which do not exist in turbulent
combustion of traditional fuels such as methane. This highly irregular flow is very hard to model
successfully which leads to the aim of this project.
It is therefore envisioned to create accurate models of these phenomena to assist industrial design
processes. Direct Numerical Simulations (DNS) simulations are extremely computationally expensive
and cannot deemed feasible anywhere other than an academic setting. Therefore, by using existing
DNS data and performing further simulations, it is planned to produce low order models that can be
used in LES or even RANS. Furthermore, in addition to classical modelling approaches which are based
on explicit equations [5], Machine Learning (ML) could be used [6]. ML is a powerful tool that allows
relationships to be drawn between parameters arising from a dataset. Therefore, it could be possible
to create the aforementioned models by training an algorithm based on the DNS data. This would
make use of the existing hardware of High-Performance Computing (HPC) available to the university
which could then be used to retest the produced models against the initial dataset or other existing
LES and RANS models. Thus, aiming to produce updated or even new models for Hydrogen turbulent
combustion by the end of the project would pave the way for applying them in more complex settings
and simulations and validate their performance against experimental data, in making the first step of
implementing Hydrogen combustion in industrial applications.