Well-known tools developed for satellite and debris re-entry perform break-up and trajectory simulations in a deterministic sense and do not perform any uncertainty treatment. The treatment of uncertainties associated with the re-entry of a space object requires a probabilistic approach. A Monte Carlo campaign is the intuitive approach to performing a probabilistic analysis, however, it is computationally very expensive. In this work, we use a recently developed approach based on a new derivation of the high dimensional model representation method for implementing a computationally efficient probabilistic analysis approach for re-entry. Both aleatoric and epistemic uncertainties that affect aerodynamic trajectory and ground impact location are considered. The method is applicable to both controlled and un-controlled re-entry scenarios. The resulting ground impact distributions are far from the typically used Gaussian or ellipsoid distributions.
Bibliographical noteFunding Information:
Funding for Piyush Mehta is provided by the European Commission through the Marie Curie Initial Training Network (ITN) STARDUST under Grant No. 317185 . Partial support for Martin Kubicek is provided by ’OPTIMAD Engineering Srl’. The authors would like to acknowledge the use of the EPSRC funded ARCHIE-WeSt High Performance Computer ( www.archie-west.ac.uk ), EPSRC Grant No. EP/K000586/1 .