Numerical and Experimental Study of Mixing Processes Associated with Hydrogen and High Hydrogen Content Fuels

As simulation capability improves exponentially with increasingly more cost effective CPUs and hardware, it can be used ?routinely? for engineering applications. Many commercial products are available and they are marketed as increasingly powerful and easy to use. The question remains as to the overall accuracy of results obtained. To support the validation of the CFD, a hierarchical experiment was established in which the type of fuel injection (radial, axial) as well as level of swirl (non-swirling, swirling) could be systematically varied. The effort was limited to time efficient approaches (i.e., generally RANS approaches) although limited assessment of time resolved methods (i.e., unsteady RANS and LES) were considered. Careful measurements of the flowfield velocity and fuel concentration were made using both intrusive and non-intrusive methods. This database was then used as the basis for the assessment of the CFD approach. The numerical studies were carried out with a statistically based matrix. As a result, the effect of turbulence model, fuel type, axial plane, turbulent Schmidt number, and injection type could be studied using analysis of variance. The results for the non-swirling cases could be analyzed as planned, and demonstrate that turbulence model selection, turbulence Schmidt number, and the type of injection will strongly influence the agreement with measured values. Interestingly, the type of fuel used (either hydrogen or methane) has no influence on the accuracy of the simulations. For axial injection, the selection of proper turbulence Schmidt number is important, whereas for radial injection, the results are relatively insensitive to this parameter. In general, it was found that the nature of the flowfield influences the performance of the predictions. This result implies that it is difficult to establish a priori the ?best? simulation approach to use. However, the insights from the relative orientation of the jet and flow do offer some guidance for which approach to take. Overall, the results underscore the importance of model ?anchoring? (i.e., ?tuning? the model to provide ?reasonable? agreement with a well characterized geometry/flow). Finally, the results obtained have been carefully compiled into a standalone database following a standard format that is contained in an Appendix. This database is thus available for use by others for CFD modeling evaluations