This research contributes to addressing climate change challenges through the examination of hydrogen combustion. It investigates the flow dynamics within a simplified model of Ansaldo Energia's GT36 reheat combustor using Large Eddy Simulation (LES) at a high pressure of 20 bar, focusing on the autoignition flashback phenomena observed. More specifically, it explores predicting the apparition of this event through the use of a modularity-based clustering algorithm and its subsequent suppression by using water injection.
In the LES simulation, lean, premixed and high-pressure conditions are used, revealing an unsteady behaviour in the flame dynamics, i.e. an autoignition event in the mixing duct which repeats itself due to high-amplitude pressure waves and their subsequent convergence in the mixing duct. Using this simulation, time-series of multiple variables are acquired over the course of 8 flashback events. These variables are then pruned using a co-kurtosis PCA based dimensionality reduction technique. This method, by measuring the joint occurrence of outliers in the flow variables, identifies successfully which are the most important variables (temperature, density, pressure, velocity in the axial direction, mass fractions of HO2 and OH) that introduce a lasting change in the system and are potentially useful in finding a precursor to the flashback event.
The time-series of these variables are then introduced in the modularity-based clustering algorithm. This algorithm tessellates the phase space of the system and then transforms it into a graph, thus retaining the information about the dynamics of the system. Then, it clusters this graph using a metric called modularity. The method proves effective in finding a precursor for the autoignition event, resulting in an average prediction time for the 8 flashback events of 32.13 microseconds, which is over 50% of the time that the combustor is in the normal operating state. Furthermore, the algorithm performs very well in the number of false positives, and, due to a change made to the algorithm in this study, the number of true positives is increased to 100%. The algorithm is then put through several robustness tests, which include the use of different sampling locations, less features, unseen data and relying only on temperature and pressure information sampled at the combustor walls. Here, the algorithm retained its performance, with only a small decrease in the prediction time, demonstrating its potential towards its use in an online prediction scenario. Lastly, the level of fidelity of the LES simulation was increased by using the digital filter method to simulate turbulent fluctuations at the inlet and fully-developed velocity profile, where once again it was found that the algorithm retains a good prediction time.
For the second part of this research, a flashback event and its afferent prediction time were chosen to investigate the use of water injection and its potential at suppressing the flashback when the water is injected based on the prediction time. Here, following an empirical design approach, where the Sauter Mean Diameter (SMD), the mass flow, the diameter of the nozzle, the angles of the cone were varied, a preliminary design was sought. It was found that the SMD is highly influential towards the spread of the spray, with larger particles being preferred to their ability to retain their momentum and more quickly cover the mixing duct. In addition, large angles for the cone and a high injection velocity are again necessary for a good spread and a quick response time. An attempt was also made to place a set of sprays at the walls of the mixing duct, where it was found that the flashback event now takes place upstream of the spray due to aerodynamic blockage. The design process culminates in a setup where six spray are placed at the inlet of the mixing duct. In this setup, the spray is able to quickly cover the mixing duct and the flashback is suppressed, while also retaining an evaporation efficiency of 96.3%.