Investigation of Swirl Distributed Combustion with Experimental Diagnostics and Artificial Intelligence Approach

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Swirl Distributed Combustion was fundamentally investigated with experimental diagnostics and predictive analysis using machine learning and computer vision techniques. Ultra-low pollutants emission, stable operation, improved pattern factor, and fuel flexibility make distributed combustion an attractive technology for potential applications in high-intensity stationary gas turbines. Proper mixing of inlet fresh air and hot products for creating a hot and low-oxygen environment is critical to foster distributed combustion, followed by rapid mixing with the fuel. Such conditions result in a distributed thick reaction zone without hotspots found in (thin reaction front) of conventional diffusion flames leading to reduced NOx and CO emissions. The focus of this dissertation is to develop a detailed fundamental understanding of distributed combustion in a lab-based swirl combustor (to mimic gas turbine can combustor) at moderate heat release intensities in the range 5.72- 9.53 MW/m3-atm using various low-carbon gaseous fuels such as methane, propane, hydrogen-enriched fuels. The study of distributed combustion at moderate thermal intensity helped to understand the fundamental aspects such as reduction of flame fluctuation, mitigation of thermo-acoustic instability, flame shape evolution, flow field behavior, turbulence characteristics, variation of Damkӧhler number, vortex propagation, flame blowoff, and pollutant and CO2 emission reduction with gradual mixture preparation. Initial efforts were made to obtain the volumetric distribution ratio, evolution of flame shape in terms of OH* radical imaging, variation of flame standoff, thermal field uniformity, and NO and CO emissions when the flame transitions to distributed reaction zone. Further investigation was performed to study the mitigation of flame thermo-acoustics and precession vortex core (PVC) instabilities in swirl distributed combustion compared to swirl air combustion using the acoustic pressure and qualitative heat release fluctuation data at different dilution CO2 dilution levels with and without air preheats. Proper orthogonal decomposition (POD) technique was utilized to visualize the appearance of dynamic coherent structures in reactive flow fields and reduction of fluctuation energy. Vortex shedding was found responsible for the fluctuation in swirl air combustion while no significant flame fluctuation was observed in distributed combustion. Distributed combustion showed significantly reduced acoustic noise and much higher stability quantified by local and global Rayleigh index. This study was extended with hydrogen-enriched methane (vol. = 0, 10, 20, 40% H2) to compare the stability of the flow field in conventional air combustion and distributed combustion. Results were consistent and distributed reaction zones showed higher flame stability compared to conventional swirl air combustion. The study of lean blowoff in distributed combustion showed a higher lean blowoff equivalence ratio with gradual increase in heat release intensity, which was attributed to higher flow field instability due to enhanced inlet turbulence. Extension of lean blowoff (ϕLBO) was observed with gradual %H2 which showed decrease of lean blowoff equivalence ratio in distributed reaction zones. Additionally, the reduction in ϕLBO was achieved by adding preheats to the inlet airstream for different H2 enrichment cases due to enhanced flame stability gained from preheating. Examination of non-reactive flow field with particle image velocimetry (PIV) was performed to understand the fundamental differences between swirl flow and distributed reaction flow at constant heat release intensities. Higher rms fluctuation leading to healthy turbulence and higher Reynolds stress were found in distributed reaction flow cases signifying enhanced mixing characteristics in distributed combustion. Reduction of pollutant emission was an important focus of this research. Measurement of NO and CO emission at different mixture preparation levels exhibited significant reduction in NO emission (single digit) compared to swirl air combustion due to mitigation of spatial hotspots and temperature peaks. Additionally, better mixing and uniform stoichiometry supported reduced CO emissions in distributed combustion for every fuel. With increased H2 in the fuel, NO gradually increased for air combustion while reduction of NO was found in distributed combustion due to decrease in thermal and prompt NO generation. Finally, the use of machine learning and computer vision techniques was investigated for software-based prediction of combustion parameters (pollutants and flame temperature) and feature-based recognition of distributed combustion regimes. The primary goal of using artificial intelligence is to reduce the time of experimentation and frequent manual interference during experiments in order to enhance the overall accuracy by reducing human errors. Such predictions will help in developing data-driven smart-sensing of combustion parameters and reduce the dependence on experimental trials.