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Low order models can be used to accelerate engineering design processes. Ideally, these surrogates should meet the conflicting requirements of large design space coverage, high accuracy and fast evaluation. Within the context of aerospace applications at transonic conditions, this can be challenging due to the associated non-linearity of the flow regime. Different methods have been investigated in the past to predict the flow-field around shapes such as airfoils or cylinders. However, they usually have reduced spatial resolution, limiting the prediction capabilities within the boundary layer which is of interest for transonic wall-bounded flows. This work proposes a novel Point-Enhanced Convolutional Neural Network (PCNN) method that combines the advantages of the well-established PointNet and convolutional neural network approaches. The PCNN model has relatively low memory requirements in the training process, preserves the spatial correlation in the domain and has the same resolution as a traditional computational method. The architecture is used for the flow-field prediction of civil aero-engine nacelles in which it is demonstrated that the flow features of peak isentropic Mach number (M i s), pre-shock isentropic Mach number and shock location (X / L n a c) are captured within Δ M i s = 0.02, Δ M i s = 0.04 , Δ X / L n a c = 0.007 , respectively. The PCNN model successfully predicts the integral parameters of the boundary layer, in which the incompressible displacement thickness, momentum thickness and shape factor are typically within 5% of the CFD. Overall, the PCNN method is demonstrated for transonic wall-bounded flows for a range of flow physics that include shock waves and shock-induced separation. [ABSTRACT FROM AUTHOR]

Future turbo-fan engines are expected to operate at low specific thrust with high bypass ratios to improve propulsive efficiency. Typically, this can result in an increase in fan diameter and nacelle size with the associated drag and weight penalties. Therefore, relative to current designs, there is a need to develop more compact, shorter nacelles to reduce drag and weight. These designs are inherently more challenging and a system is required to explore and define the viable design space. Due to the range of operating conditions, nacelle aerodynamic design poses a significant challenge. This work presents a multi-objective optimisation approach using an evolutionary genetic algorithm for the design of new aero-engine nacelles. The novel framework includes a set of geometry definitions using Class Shape Transformations, automated aerodynamic simulation and analysis, a genetic algorithm, evaluations at various nacelle operating conditions and the inclusion of additional aerodynamic constraints. This framework has been applied to investigate the design space of nacelles for high bypass ratio aero-engines. The multi-objective optimisation was successfully demonstrated for the new nacelle design challenge and the overall system was shown to enable the identification of the viable nacelle design space. [ABSTRACT FROM AUTHOR]

In the context of aircraft applications, the overall design process can be challenging due to the different aerodynamic requirements at several operating conditions and the total associated computational overhead. For this reason, the use of low order models for the optimisation of complex non-linear problems is sometimes used. This paper addresses the challenge of transonic aerodynamic design optimisation through the integration of a set of neural networks for the prediction of integral values, the classification of flow features and the estimation of flow field characteristics. The design method improves the computational efficiency relative to an expensive design process driven by Computational Fluid Dynamics (CFD) evaluations. The approach is used for the multi-point, multi-objective optimisation of a compact aero-engine nacelle in which the design outcomes are validated using a CFD in-the-loop optimisation strategy. It is demonstrated that the method based on the neural network capability identifies similar nacelle designs at a 75% reduction in the overall computational cost, a drag uncertainty prediction within 2.8%, and a predictive accuracy for the classification metric of 98%. For downselected configurations, the main flow characteristics in terms of peak Mach number, pre-shock Mach number and shock location are well predicted by the neural network models compared with the CFD-based evaluations. [ABSTRACT FROM AUTHOR]

For many aerodynamic design tasks, a key challenge is the balance between the non-linearity of the transonic flow, the inherent 3D nature of the geometry and flow field, the computational cost and the level of accuracy. Within an optimisation process this is further compounded by the high degrees of freedom to define the geometry and the requirement for 3D computations at both design and off-design conditions. An example of this is the design of compact nacelles for future civil aero-engines which will have larger bypass ratios than current in-service architectures. This paper presents an approach for the design and optimisation of 3D drooped and scarfed non-axisymmetric nacelles. To reduce the computational expense, a range of surrogate-based adapted methods are investigated. Relative to the conventional approach of full numerical simulations in the optimisation loop, the adapted method identifies an acceptable design space with a 65% reduction in the total computational cost. Overall, this demonstrates a useful approach for reducing the time and cost of high-dimensional, aerodynamic design problems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is envisaged that the next generation of civil aero-engines will employ high bypass ratios to lower specific thrust and improve propulsive efficiency. This trend is likely to be accompanied with the integration of compact nacelle and exhausts in podded under-wing installation positions that are close coupled to the airframe. This leads to the requirement for a comprehensive methodology able to predict aerodynamic performance for combined airframe-engine architectures. This paper presents a novel thrust and drag accounting approach for the aerodynamic analysis of integrated airframe-engine systems. An integral metric is synthesised based on the concept of net vehicle force. This is accomplished through the consolidation of aerodynamic coefficients, combined with the engine cycle characteristics obtained from a thermodynamic matching model. The developed approach is coupled with an in-house tool for the aerodynamic design and analysis of installed aero-engines. This framework is deployed to quantify the impact of engine installation position on the aerodynamic performance of a future large turbofan installed on a commercial wide-body airframe. The governing flow mechanisms are identified and their influence is decomposed in terms of the impact on airframe, nacelle, and exhaust performance. It is shown that it is essential to include the impact of installation on the exhaust for the correct determination of overall airframe-engine performance. The difference in net vehicle force for a close coupled position can reach up to -0.70% of nominal standard net thrust relative to a representative baseline engine location. [ABSTRACT FROM AUTHOR]

The inherent mathematical smoothness of intuitive class shape transformation (iCST) curves has been shown to be suitable for the design of aerodynamic shapes. However, this property means that any changes to a constraint are not local but will result in a modification to the whole curve. This poses a problem to the aerodynamic designer when different parts of the curve are required to fulfil particular design requirements. A Hybrid iCST (HiCST) parameterisation approach is proposed which allows two sections of a single aero-line curve to be decoupled, without geometric discontinuity, whilst maintaining the dimensionality of a design problem. The HiCST approach has been tested on two key aerodynamic components of an aero-engine. Firstly, a design space exploration and optimisation were carried out for an aero-engine fan cowl. A comparison of Pareto fronts showed a 3.9% reduction in the minimum achievable nacelle drag from the iCST to the HiCST parameterisation. Secondly, aero-engine intakes were designed with both the iCST and HiCST parameterisations. The HiCST intake showed improved aerodynamic performance in terms of DC 60 and IPR and proved more insensitive to changes in massflow and incidence. This development of the method for an aero-engine fan cowl and intake highlights the potential aerodynamic benefit from the proposed HiCST method. [ABSTRACT FROM AUTHOR]