Multi-fidelity design optimization of installed aero-engines with non-axisymmetric exhausts - Hueso Rebassa, Josep; MacManus, David G.; Sánchez-Moreno, Francisco; Goulos, Ioannis; Tejero, Fernando; Sheaf, Christopher T. (American Society of Mechanical Engineers (ASME), 2025)
Larger ultra-high bypass ratio (UHBR) aero-engines introduce an aerodynamic integration challenge. In close-coupled, podded underwing configurations, the aerodynamic interference between the propulsion system and the airframe could penalize the aircraft net vehicle force (NVF) and erode some of the novel cycle benefits and fuel burn reduction. Non-axisymmetric designs of the bypass nozzle can improve the performance of the aircraft by mitigating some of the penalizing effects induced by the integration of the powerplant. However, due to the prohibitive computational cost of the design methods, only lower-fidelity design approaches have been feasible in an industrial time-scale. This work develops a relatively low-cost multi-fidelity design optimization methodology for non-axisymmetric exhausts where the effects of the propulsion system installation are considered. The methodology combines inviscid and viscous aerodynamic data to formulate multi-fidelity surrogate models which drive a genetic algorithm (GA) optimization. The method enabled the incorporation of the viscosity effects in the optimization process at a reasonable computational cost and led to better designs relative to a methodology based only on lower-fidelity data. Overall, the optimization of non-axisymmetric exhausts can benefit the net vehicle force of the complete engine–aircraft system in cruise by up to 0.9% of the engine standard net thrust which can reduce fuel burn by a similar amount. The optimization with multi-fidelity surrogate models reduced the computational time by a factor of four relative to a method based only on viscous aerodynamic data.
Keywords: non-axisymmetric exhausts; propulsion system integration; computational fluid dynamics; multi-fidelity design optimization methods; response surface model; neural networks; 40 Engineering; 4001 Aerospace Engineering; Energy; 4004 Chemical engineering