Experimental and Computational Analyses of Methane and Hydrogen Mixing in a Model Premixer

摘要

The mixing of fuel and air in combustion systems plays a key role in overall operability and emissions performance. Such systems are also being looked to for operation on a wide array of potential fuel types, including those derived from renewable sources such as biomass or agricultural waste. The optimization of premixers for such systems is greatly enhanced if efficient design tools can be utilized. The increased capability of computational systems has allowed tools such as computational fluid dynamics to be regularly used for such purpose. However, to be applied with confidence, validation is required. In the present work, a systematic evaluation of fuel mixing in a specific geometry, which entails cross flow fuel injection into axial nonswirling air streams has been carried out for methane and hydrogen. Fuel concentration is measured at different planes downstream of the point of injection. In parallel, different computational fluid dynamics approaches are used to predict the concentration fields resulting from the mixing of fuel and air. Different steady turbulence models including variants of Reynolds averaged Navier-Stokes (RANS) have been applied. In addition, unsteady RANS and large eddy simulation are used. To accomplish mass transport with any of the RANS approaches, the concept of the turbulent Schmidt number is generally used. As a result, the sensitivity of the RANS simulations to different turbulent Schmidt number values is also examined. In general, the results show that the Reynolds stress model, with use of an appropriate turbulent Schmidt number for the fuel used, provides the best agreement with the measured values of the variation in fuel distribution over a given plane in a relatively time efficient manner. It is also found that, for a fixed momentum flux ratio, both hydrogen and methane penetrate and disperse in a similar manner for the flow field studied despite their significant differences in density and diffusivity.