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

What is it about?

This study applies a novel approach for creating more accurate and efficient Aerodynamic Database (AEDB) models, which are crucial for predicting the behavior of aerospace vehicles, particularly Reusable Launch Vehicles (RLVs) like the CALLISTO project. CALLISTO is a collaboration between the German Aerospace Center (DLR), the French National Centre for Space Studies (CNES), and the Japan Aerospace Exploration Agency (JAXA), focusing on the development of a reusable rocket demonstrator. By employing Bayesian Inference methods, various types of AEDB models have been developed that can predict aerodynamic characteristics under uncertainty better than traditional methods. This research could significantly improve the design and reliability of aerospace vehicles and potentially also be applied to other engineering domains.

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