Environmental and future supply pressures are expected to drive aviation towards alternative fuel sources. However little is available in the literature on aircraft landing-takeoff (LTO) cycle gaseous emissions resulting from the combustion of alternative fuels. Considering the different engine configurations existing in today’s commercial aviation fleet, emission experiments of alternative fuels on all engine types are almost impossible. Modelling may provide a solution but the availability of combustor data (geometry and air split details) in the public domain is limited. A reverse engineering technique is developed to recover the air splits and combustion process in gas turbine engine by a CRN and forward predicting the emissions from the engine exhaust. The model was developed and optimised with a Genetic Algorithm against the Jet A-1 experimental emission data obtained from an APU. Results from the optimised CRN emission predictions closely matched the Jet A-1 gaseous emission data. The modelling technique also successfully demonstrated an ability to predict APU gaseous emission data obtained for Synthetic Paraffinic Kerosene (SPK) (neat and 50–50 blended with Jet A-1) and biodiesel. This technique is expected to enhance the emission databank of aircraft and airside emissions.

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