IMT: Development of LES and RANS models for H2 turbulentcombustion leveraging DNS data

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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 wouldmake 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.