This paper investigates water injection effects in a simplified Ansaldo GT36 reheat system under realistic conditions of 20 atm using large eddy simulation (LES) coupled with thickened flame modeling and adaptive mesh refinement. The water injection conditions are optimized by performing a parametric study based on global sensitivity analysis (GSA) with a surrogate model based on Gaussian process (GP) to reduce computational cost. In particular, the influence of four design parameters, namely, Sauter mean diameter (SMD), water mass flow, and the angles of the spray's hollow cone, is tested to achieve an optimized solution. In the "dry"case, the LES simulations show several flashback events attributed to compressive pressure waves resulting from auto-ignition in the core flow near the crossover temperature. The use of water injection is found to be effective in suppressing the flashback occurrence. In particular, the global sensitivity analysis shows that the external angle of the spray cone and the mass flow of water are the most important design parameters for flashback prevention. NOx emissions are reduced by about 17% with water injection. Once an optimized condition with water injection is found, a recently proposed method to downscale the combustor to lower pressures is applied and tested. Additional LESs are performed for this purpose at the dry, unstable condition and the "wet,"stable condition. Results show that similar dynamics are predicted at 1 atm, validating the method's robustness. This provides avenues for experimentally testing combustion dynamics at simplified conditions which are still representative of high-pressure practical configurations.

This paper presents an investigation of the effects of water injection within a simplified version of the Ansaldo GT36 reheat system. The investigation is carried out under realistic operating conditions of 20 atm and using large eddy simulation (LES) coupled with the thickened flame model (TFM) and an adaptive mesh refinement. The water injection conditions are optimized by performing a parametric study based on global sensitivity analysis and a surrogate model based on Gaussian process is employed as a way to reduce computational cost. In particular, the influence on the system performance of four design parameters, namely Sauter mean diameter, water mass flow and the angles of the spray's hollow cone, is tested to achieve an optimized solution. In the 'dry' case, the LES simulations show several flashback events, which are a defining aspect of the considered conditions, and attributed to compressive pressure waves resulting from autoignition in the core flow near the crossover temperature. The use of water injection is found to be effective in suppressing the flashback occurrence. In particular, the global sensitivity analysis shows that the external angle of the spray cone and the mass flow of water are the most important design parameters for flashback prevention. Moreover, NOx was shown to be reduced by about 17% by the use of the water injection at the tested conditions. Once an optimised condition with water injection is found, a recently proposed method to downscale the combustor to lower pressures is applied and tested. Additional LES are performed for this purpose at the 'dry', unstable condition and the 'wet', stable condition. Results show that similar dynamics, respectively unstable and stable, is predicted at 1 atm, suggesting the robustness of the method. This provides avenues for experimentally testing combustion dynamics at simplified conditions which are still representative of high-pressure practical configurations.

In this work, we propose a data-driven framework to identify precursors of extreme events in turbulent reacting flows. Specifically, we tackle the problem of flashback prediction in a lean hydrogen reheat combustor. Our framework is composed of two parts. The first consists in the use of a co-kurtosis based approach to identify the components of the thermochemical and flow state which are the most relevant for the onset of flashback. This allows for an efficient low-dimensional representation. From this reduced representation, a modularity-based clustering algorithm is then employed to segregate between clusters which contain normal and extreme (flashbacking) states, and the cluster located in-between these states, which are the precursor states of extreme events. We show that this method is able to identify the most important features at the onset of flashback in the considered reheat combustor and then provide precursor states based on those. The prediction time obtained with the identified precursors is relatively large when compared to the duration over which the combustor is stable. Additional analyses on the specific choice of features for the precursor identification and the sampling locations are made. The robustness of the method when using shorter time series to identify the precursor is also investigated. Results show that the method is generally robust with respect to such changes. A first step towards practical measurements is also attempted with wall pressure measurements, which shows only a moderate reduction in prediction time. This work proposes for the first time a data-driven technique to automatically identify precursors of flashback in hydrogen combustion opening the path for such applications on other extreme events in reacting flows.