Purpose Aerodynamic shape optimisation is complex because of the high dimensionality of the problem, the associated non-linearity and its large computational cost. These three aspects have an impact on the overall time of the design process. To overcome these challenges, this paper aims to develop a method for transonic aerodynamic design with dimensionality reduction and multifidelity techniques. Design/methodology/approach The developed methodology is used for the optimisation of an installed civil ultra-high bypass ratio aero-engine nacelle. As such, the effects of airframe-engine integration are considered during the optimisation routine. The active subspace method is applied to reduce the dimensionality of the problem from 32 to 2 design variables with a database compiled with Euler computational fluid dynamics (CFD) calculations. In the reduced dimensional space, a co-Kriging model is built to combine Euler lower-fidelity and Reynolds-averaged Navier stokes higher-fidelity CFD evaluations. Findings Relative to a baseline aero-engine nacelle derived from an isolated optimisation process, the proposed method yielded a non-axisymmetric nacelle configuration with an increment in net vehicle force of 0.65% of the nominal standard net thrust. Originality/value This work investigates the viability of CFD optimisation through a combination of dimensionality reduction and multifidelity method and demonstrates that the developed methodology enables the optimisation of complex aerodynamic problems.

The next generation of civil turbofan engines are likely to have increased bypass ratios and lower fan pressure ratios to improve propulsive efficiency and to reduce specific fuel consumption. However, the larger size of these engines may result in increased overall aircraft drag partially that could offset the fuel consumption benefits. Non-axisymmetric exhaust configurations can contribute to the mitigation of these effects through an improved alignment of the thrust vector relative to the drag axis. However, there is a lack of knowledge on how to experimentally test non-axisymmetric exhaust designs. To address this, the study develops a non-axisymmetric configuration of the Dual Stream-Flow Reference Nozzle (DSFRN) and assesses it with computational fluid dynamics in various configurations and conditions. The objective is to establish a baseline approach for testing non-axisymmetric exhausts. Overall, it is recommended to test non-axisymmetric exhausts with the ambient wind-on effects included and to evaluate the three-dimensional exhaust characteristics using thrust vector angles, in addition to overall velocity and discharge coefficients. Moreover, the interaction between a swept wing and the non-axisymmetric exhaust was found not to have a notable impact on the exhaust characteristics. Nomenclature𝛿 𝑋𝑍 , 𝛿 𝑋𝑌 Vertical and lateral force vector angles, respectively [rad]

In order to reduce fuel consumption, the next generation of aero-engines are expected to operate with higher bypass ratios and lower fan pressure ratios. This will improve the propulsive efficiency of the power plant and reduce specific fuel consumption. Higher bypass ratios will be mostly accommodated with larger fan diameters. However, this will increase the size and mass of the powerplant, which could penalise the overall aircraft drag and erode some of the aero-engine cycle benefits. In addition, future configurations may require more closecoupled installations with the airframe due to structural and ground clearance requirements. This tendency may further exacerbate the adverse aerodynamic installation effects. A better integration of UHBR aero-engines with the airframe could be achieved with non-axisymmetric separate-jet exhausts. Non-axisymmetric configurations of the bypass nozzle can improve the performance of the aircraft by mitigating some of the penalising aerodynamic effects induced by the installation of the power plant. In this context, three-dimensional configurations of exhaust systems are parametrised and integrated with the propulsion system through a refined control of the geometry. The power plant is installed on the NASA Common Research Model and assessed with CFD. The design of non-axisymmetric exhausts is embedded in a relatively low-cost optimisation process. The method is based on response surface models and targets the optimisation of the aircraft net vehicle force for different design concepts of non-axisymmetric exhaust systems and several installation configuration. It is concluded that the optimisation of installed non-axisymmetric exhausts can benefit the overall aircraft net vehicle force between 0.5 − 0.9% of the engine nominal thrust, depending on the installation position.

No abstract

Computational fluid dynamics (CFD) methods have been widely used for the design and optimisation of complex non-linear systems. Within this context, the overall process can typically have a large computational overhead. For preliminary design studies, it is important to establish design capabilities that meet the usually conflicting requirements of rapid evaluations and accuracy. Of particular interest is the aerodynamic design of components or subsystems within the transonic range. This can pose notable challenges due to the non-linearity of this flow regime. There is a need to develop low order models for future civil aero-engine nacelle applications. The aerodynamics of compact nacelles can be sensitive to changes in geometry and operating conditions. For example within the cruise segment different flow-field characteristics may be encountered such as shock-wave boundary layer interaction or shock induced separation. As such, an important step in the successful design of these new architectures is to develop methods for fast and accurate flow-field prediction. This work studies two different metamodelling approaches for flow-field prediction of 3D non-axisymmetric nacelles. Firstly, a reduced order model based on an artificial neural network (ANN) is considered. Secondly, a low order model that combines singular value decomposition and an artificial neural network (SVD+ANN) is investigated. Across a wide geometric design space, the ANN and SVD+ANN methods have an overall uncertainty in the isentropic Mach number prediction of about 0.02. However, the ANN approach has better capabilities to predict pre-shock Mach numbers and shock-wave locations.