To reduce aviation’s climate impact, the industry must significantly reduce greenhouse gas emissions, necessitating the development of more sustainable and efficient propulsion technologies. The Water-Enhanced Turbofan (WET) engine is such a promising future aero-engine concept.
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To reduce aviation’s climate impact, the industry must significantly reduce greenhouse gas emissions, necessitating the development of more sustainable and efficient propulsion technologies. The Water-Enhanced Turbofan (WET) engine is such a promising future aero-engine concept. The engine integrates a Joule-Brayton cycle with a semi-closed Rankine steam cycle. Superheated steam is injected before the combustion chamber and downstream before the core nozzle. While previous studies have explored the fundamental thermodynamics of the concept and assessed its potential for reducing climate impact, this study explores the design space of the WET engine and examines key design parameters and their influence on cycle performance using NASA’s pyCycle and OpenMDAO framework. Moreover, the high-fidelity in-house software Hexacode is used to model the heat recovery steam generator (HRSG). Based on the design exploration, a water-to-air ratio (WAR) of 0.20 is found optimal for the WET cycle, reducing the thrust-specific fuel consumption (TSFC) by 7.3% with respect to a LEAP-1A-type engine. The best design solution comes with a significantly higher bypass ratio, and lower overall pressure ratio and turbine inlet temperature. Nozzle velocity ratios higher than 1 have been demonstrated to enhance the overall engine efficiency. Besides this, the condenser is identified as the main critical component of the proposed engine concept, while the HRSG design becomes challenging at high water-to-air ratios. Fuel burn can be further reduced by increasing the OPR and steam injection temperature. Future work should focus on detailed condenser modeling and the integration of the thermodynamic cycle model with preliminary heat exchanger (HEX) design models to improve system-level performance predictions.