Surrogate-based aerodynamic optimisation of compact nacelle aero-engines

Genetic algorithms are a powerful optimisation technique for the design of complex engineering systems. Although computing power continuously grows, methods purely based on expensive numerical simulations are still challenging for the optimisation of aerodynamic components at an early stage of the design process. For this reason, response surface models are typically employed as a driver of the genetic algorithm. This reduces considerably the total overhead computational cost but at the expense of an inherent prediction uncertainty. Aero-engine nacelle design is a complex multi-objective optimisation problem due to the nonlinearity of transonic flow aerodynamics. This research develops a new framework, that combines surrogate modelling and numerical simulations, for the multi-objective optimisation of aero-engine nacelles. The method initially employs numerical simulations to guide the genetic algorithm through generations and uses a combination of higher fidelity results along with evolving surrogate models to identify a set of optimum designs. This new approach has been applied to the multi-objective optimisation of civil aero-engines which are representative of future turbofan configurations. Compared to the conventional CFD in-the-loop optimisation method, the proposed algorithm successfully identified the same set of optimum nacelle designs at a 25% reduction in the computational cost. Within the context of preliminary design, the method meets the typical 5% acceptability criterion with a 65% reduction in computational cost.