The extensive amount of aerodynamic data needed for the creation of a Stability & Control data set requires efficient and reliable tools. For this reason, the task of producing a large and dense data set in general relies on ground-facility testing and is conducted after the product definition phase, where, nevertheless, already plenty of aerodynamic data is generated by use of simple low-fidelity aerodynamic methods. However, these methods are not capable of predicting complex non-linear aerodynamic phenomena, which complicates a reliable assessment of new designs and demands more accurate predictions early in the design process. Variable-fidelity surrogate modelling offers an opportunity to include high-fidelity simulation tools at affordable effort by enriching a low-fidelity data set with just a few selected highly accurate results. In this paper, an automated aerodynamic variable-fidelity modelling process is proposed to create such an aerodynamic data set at product definition phase. It is demonstrated on an industrial-relevant, agile, low-observable unmanned combat aerial vehicle. An adaptive sampling strategy is applied which proves to be both efficient and accurate. This is supplemented by a model selection algorithm. Moreover, a comparison with a single-fidelity surrogate modelling process shows the variable-fidelity modelling strategy to be superior.

创建稳定性和控制数据集所需的大量空气动力学数据需要高效且可靠的工具。因此，产生大量密集数据集的任务通常依赖于地面设施测试，并且是在产品定义阶段之后进行的，尽管如此，通过使用简单的低保真空气动力学已经生成了大量的空气动力学数据。方法。但是，这些方法无法预测复杂的非线性空气动力学现象，这使对新设计的可靠评估变得复杂，并且需要在设计过程的早期进行更准确的预测。可变保真度替代模型提供了一个机会，可以通过负担得起的努力来添加高保真度仿真工具，方法是仅用一些选定的高精度结果来丰富低保真度数据集。在本文中，提出了一种自动的空气动力学可变保真度建模过程，以在产品定义阶段创建这样的空气动力学数据集。在与工业相关的，敏捷的，低可见性的无人机上进行了演示。应用了一种自适应采样策略，该策略被证明既高效又准确。通过模型选择算法对此进行补充。此外，与单保真代理建模过程的比较表明，可变保真建模策略是更好的。应用了一种自适应采样策略，该策略被证明既高效又准确。通过模型选择算法对此进行补充。此外，与单保真代理建模过程的比较表明，可变保真建模策略是更好的。应用了一种自适应采样策略，该策略被证明既高效又准确。通过模型选择算法对此进行补充。此外，与单保真代理建模过程的比较表明，可变保真建模策略是更好的。