Multi-dimensional energetic performance modeling of an aircraft engine with the aid of enhanced least-squares estimation based genetic algorithm method

Abstract

Aircraft must have reliable and sustainable sources of power systems and must use energy efficiently. Aircraft engine advances have dramatically improved aircraft fuel efficiency. Variable cycle engine (VCE) has been promising in both civil and military areas in the last decades. An important challenge for energy saving regarding the engine is to find optimum flight conditions. For this aim, this study investigated energetic and exergetic off-design performance modeling of the variable cycle turbofan engine with respect to flight conditions and potentially energy benefits for a new-generation combat aircraft. Current analysis mainly focused on how to simplify off-design performance model of the VCE in terms of thrust, specific fuel consumption (SFC), overall and exergy efficiencies with the aid of enhanced least-squares estimation-based genetic algorithm (LSEGA) in flight phases. According to analyses, the results show that the proposed method is capable of modeling all the thermodynamic off-design parameters with root mean square error values of as low as 0.000162 for exergy efficiency, 0.000311 for overall efficiency, 1.007 for thrust and 0.0763 for SFC. It is observed from the study that the proposed LSEGA algorithm has successfully converged into optimal solutions for all indexes and flight regimes and all the off-design performance models of the VCE energetic indexes are obtained with high accuracy.