Bayesian Models for Uncertainty Estimation in Aerodynamic Databases of Reusable Launch Vehicles

Abstract

Defining aerodynamic databases is an important yet very complex and labor-intensive task during the design of new aerospace vehicles. This is particularly true for reusable launch vehicles, as observed during the development of CALLISTO, a demonstrator for a vertical-takeoff vertical-landing first stage that is jointly developed, manufactured, and tested by German Aerospace Center (DLR), Japan Aerospace Exploration Agency (JAXA), and French National Centre for Space Studies (CNES). In this paper, we present an inference-based methodology to define various types of Bayesian models exemplarily for a subset of CALLISTO's Aerodynamic Database to assess their usability and prediction qualities. First, a short introduction to the underlying aerodynamic dataset will be given, which has been aggregated from various computational fluid dynamics simulations and wind tunnel test campaigns. Then, the different Bayesian models will be defined and their inference results compared against each other, according to common error metrics. It will be shown that, within the limits and assumptions of this study, several types of Bayesian models provide better accuracy in the prediction of uncertain aerodynamic coefficients compared to classical expert-fitted models for the given CALLISTO dataset. Generally, it can be concluded that Bayesian models provide a promising approach for the definition of aerodynamic databases and could also find many potential applications in other engineering domains.