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REVERSE ENGINEERING GAS TURBINE EMISSION PERFORMANCE: APPLIED TO AN AIRCRAFT AUXILIARY POWER UNIT

机译:逆向工程燃气轮机的排放性能:应用于飞机辅助动力装置

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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 fiiels. 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-l experimental emission data obtained from an APU. Results from the optimised CRN emission predictions closely matched the Jet A-l 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-l) and biodiesel. This technique is expected to enhance the emission databank of aircraft and airside emissions.
机译:预计环境和未来的供应压力将推动航空业转向替代燃料来源。然而,关于替代燃料电池燃烧产生的飞机起降(LTO)循环气体排放的文献很少。考虑到当今商用航空机队中存在的不同发动机配置,几乎不可能在所有发动机类型上进行替代燃料的排放实验。建模可以提供解决方案,但是公共领域中燃烧器数据(几何形状和空气分离细节)的可用性受到限制。 开发了一种逆向工程技术,以通过CRN恢复燃气涡轮发动机中的空气分流和燃烧过程,并向前预测发动机废气的排放量。针对从APU获得的Jet A-1实验发射数据,使用遗传算法开发并优化了模型。来自优化的CRN排放预测的结果与Jet A-1气体排放数据非常匹配。该建模技术还成功地证明了预测从合成石蜡煤油(SPK)(纯净的和50-50与Jet A-1混合的)和生物柴油获得的APU气体排放数据的能力。预计该技术将增强飞机和空侧排放的排放数据库。

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