An aerodynamic variable-fidelity modelling framework for a low-observable UCAV

Abstract

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.