A Kriging-based method for the efficient computation of debris impact zones

To prevent or assess launch risk, evaluation of launchers impact zones is a key element. Several methods are currently used to predict impact zones at the French space agency (CNES), but the highest-fidelity method uses a series of computationally costly Monte Carlo simulations. This process can be very time consuming and the computation time can become prohibitive. A machine learning method called Kriging or Gaussian Process Regression is studied as a potential avenue to speed up the impact zones evaluation. This Kriging-based method, is tested in this paper in different flight phases and its potential for estimating debris impact zones is evaluated in terms of processing time, accuracy and genericity. (c) 2024 International Association for the Advancement of Space Safety. Published by Elsevier Ltd. All rights reserved.