Purpose

Aerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.

Design/methodology/approach

The nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.

Findings

Ratios of low- and high-fidelity training samples to degrees of freedom of n_LF/n_DOFs = 50 and n_HF/n_DOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.

Originality/value

The application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.

中文翻译：

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通过多保真神经网络优化机舱

 目的

空气动力学形状优化是一个复杂的问题，通常受跨音速非线性空气动力学、高维设计空间和高计算成本的影响。因此，数值模拟方法的使用对于某些应用来说可能会变得令人望而却步。本文旨在提出一种计算高效的多保真度方法来优化二维轴对称航空发动机短舱。

设计/方法论/途径

机舱优化方法将无梯度算法与多保真代理模型相结合。基于人工神经网络 (ANN) 的机器学习因其处理非线性行为的能力而被用作建模技术。多保真度方法将雷诺平均纳维斯托克斯和欧拉 CFD 计算分别结合为高保真度和低保真度。

 发现

低保真度和高保真度训练样本与自由度 n _LF /n _DOF = 50 和 n _HF /n _DOF = 12.5 的比率提供了均方根误差小于 5% 的替代模型，并且与与等效的 CFD 在环优化相比，具有最佳设计空间。向下选择的设计获得了类似的机舱几何形状和气动流动拓扑，计算成本降低了 92%。这凸显了这种多保真方法在初步设计阶段进行空气动力学优化的潜在好处。

 原创性/价值

基于人工神经网络的多保真度技术在孤立短舱气动形状优化问题中的应用是这项工作的关键创新点。该方法的多保真度方面推进了基于单保真度代理模型的当前实践，并进一步降低了计算成本以满足工业设计时间尺度。此外，还建立了相对于设计变量数量的低保真度和高保真度样本量的指南。