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Numerical and Experimental Studies on a Syngas-Fired Ultra Low NOx Combustor

JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER-TRANSACTIONS OF THE ASME(2017)

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摘要
Simulations and exhaust measurements of temperature and pollutants in a syngas-fired model trapped vortex combustor for stationary power generation applications are reported. Numerical simulations employing Reynolds-averaged Navier-Stokes (RANS) and large eddy simulations (LES) with presumed probability distribution function (PPDF) model were also carried out. Mixture fraction profiles in the trapped vortex combustor (TVC) cavity for nonreacting conditions show that LES simulations are able to capture the mean mixing field better than the RANS-based approach. This is attributed to the prediction of the jet decay rate and is reflected on the mean velocity magnitude fields, which reinforce this observation at different sections in the cavity. Both RANS and LES simulations show close agreement with the experimentally measured OH concentration; however, the RANS approach does not perform satisfactorily in capturing the trend of velocity magnitude. LES simulations satisfactorily capture the trend observed in exhaust measurements which is primarily attributed to the flame stabilization mechanism. In the exhaust measurements, mixing enhancement struts were employed, and their effect was evaluated. The exhaust temperature pattern factor was found to be poor for baseline cases, but improved with the introduction of struts. NO emissions were steadily below 3 ppm across various flow conditions, whereas CO emissions tended to increase with increasing momentum flux ratios (MFRs) and mainstream fuel addition. Combustion efficiencies similar to 96% were observed for all conditions. The performance characteristics were found to be favorable at higher MFRs with low pattern factors and high combustion efficiencies.
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关键词
trapped vortex combustor,syngas,presumed probability density function (PPDF),low NOx combustor,Reynolds-averaged Navier-Stokes (RANS),large eddy simulations (LES)
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