VALIDATION AND ASSESSMENT OF A NUMERICAL METHODOLOGY FOR TURBULENT LIQUID FUEL JETS IN HIGH-SPEED CROSSFLOW

For lean combustion technology, sufficient mixing between fuel and air helps to move away from stoichiometric combustion, the flame temperature of which enormously promotes the production of harmful pollutants. Good fuel-air mixing is also essential to avoid fuel-rich pockets, where unburnt hydrocarbon and soot are likely to be produced. For low emissions gas turbines using liquid fuels, such as diesel, jet in crossflow is one the approaches to achieve efficient liquid jet breakup and evaporation as well as superior mixing characteristics. In order to develop low emissions fuel spray technologies that use transverse jets, it is important that the current numerical tools have the ability to accurately predict the jet breakup, both primary and secondary, evaporation mechanisms, to properly assess the performance of an injector design. However, because the numerical tools often use model constants that are calibrated with experimental data mostly only applicable to a specific range of operating conditions, a particular test fluid or injection method, it is very challenging to assess the accuracy of the predicted results without further experimental validation. To the author's best knowledge, the breakup mechanism and the predictive capabilities of the breakup models used to guide the design of novel injectors for fuel transverse jets at elevated pressure and high-speed turbulent crossflow have not been reported in full detail. Thus, the main focus of this study is to compare and assess the capabilities of the breakup models used in state-of-the-art CFD, including a stochastic approach that relies less on experimental model constants, to predict the spray characteristics of turbulent jets in crossflow at different pressures, gas Weber numbers and momentum flux ratios that are more representative of gas turbine applications. This study will be conducted by using Simcenter STAR-CCM+. The simulated results will be compared to experimental data to validate and calibrate the breakup models through a parametric and sensitivity study.