A conceptual methodology for the prediction of engine emissions
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Current emission prediction models in the conceptual design phase are based on historical data and empirical correlations. Two main reasons contributing to the current state of emission models are complexity of the phenomena involved in the combustor and relatively low priority of having a more detailed emissions model at the conceptual design phase. However, global environmental concerns and aviation industry growth highlight the importance of improving the current emissions prediction approaches. There is a need to have an emission prediction model in the conceptual design phase to reduce the prediction uncertainties and perform parametric studies for different combustor types and operating conditions. The research objective of this thesis is to develop a methodology to have an initial estimate of gas turbines' emissions, capture their trends and bring more information forward to the conceptual design phase regarding the emission levels. This methodology is based on initial sizing of the combustor and determining its flow-fractions at each section using a 1D flow analysis. A network of elementary chemical reactors is considered and its elements are sized from the results of the 1D flow analysis to determine the level of emissions at the design and operating conditions. Additional phenomena that have significant effects on the prediction of emissions are also considered which are: 1) droplet evaporation and diffusion burning, and 2) fuel-air mixture non-uniformity. A simplified transient model is developed to determine the evaporation rate for a given droplet size distribution and to obtain the amount of vaporized fuel before they ignite. A probabilistic unmixedness model is also employed to consider the range of equivalence ratio distribution for the fraction of the fuel that is vaporized and mixed with air. An emission model is created for the single annular combustor (SAC) configuration and applied to two combustors to test the prediction and parametric capabilities of the model. Both uncertainty and sensitivity analyses are performed to assess the capability of the model to reduce the prediction uncertainty of the model compared to the simpler models without considering the droplet evaporation and mixture non-uniformity. The versatility of the model is tested by creating an emission model for a Rich-Quench-Lean (RQL) combustor, and the results are compared to limited actual data. In general, the approach shows a good performance predicting the NOx emission level compared to CO emission level and capturing their trends. Especially in the RQL combustor case, a more detailed model is required to improve the prediction of the CO emission level.