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.

在飞机应用的背景下，由于多种操作条件下的不同空气动力学要求以及相关的总计算开销，整体设计过程可能具有挑战性。出于这个原因，有时会使用低阶模型来优化复杂的非线性问题。本文通过集成一组用于积分值预测、流动特征分类和流场特征估计的神经网络来解决跨声速空气动力学设计优化的挑战。相对于由计算流体动力学 (CFD) 评估驱动的昂贵设计过程，该设计方法提高了计算效率。该方法用于多点，紧凑型航空发动机短舱的多目标优化，其中使用 CFD 在环优化策略验证设计结果。结果表明，基于神经网络能力的方法可以识别相似的机舱设计，总体计算成本降低 75%，阻力不确定性预测在 2.8% 以内，分类指标的预测准确度为 98%。对于向下选择的配置，与基于 CFD 的评估相比，神经网络模型可以很好地预测峰值马赫数、冲击前马赫数和冲击位置方面的主要流动特性。结果表明，基于神经网络能力的方法可以识别相似的机舱设计，总体计算成本降低 75%，阻力不确定性预测在 2.8% 以内，分类指标的预测准确度为 98%。对于向下选择的配置，与基于 CFD 的评估相比，神经网络模型可以很好地预测峰值马赫数、冲击前马赫数和冲击位置方面的主要流动特性。结果表明，基于神经网络能力的方法可以识别相似的机舱设计，总体计算成本降低 75%，阻力不确定性预测在 2.8% 以内，分类指标的预测准确度为 98%。对于向下选择的配置，与基于 CFD 的评估相比，神经网络模型可以很好地预测峰值马赫数、冲击前马赫数和冲击位置方面的主要流动特性。