Multi-fidelity approach for aerodynamic modelling and simulation of uncontrolled atmospheric destructive entry

Morgado, Fábio and Peddakotla, Sai Abhishek and Garbacz, Catarina and Vasile, Massimiliano L. and Fossati, Marco; (2022) Multi-fidelity approach for aerodynamic modelling and simulation of uncontrolled atmospheric destructive entry. In: AIAA SCITECH 2022 Forum. AIAA SCITECH 2022 Forum . American Institute of Aeronautics and Astronautics Inc, AIAA, USA. ISBN 9781624106316 (

[thumbnail of Morgado-etal-Scitech-2022-Multi-fidelity-approach-for-aerodynamic-modelling-and-simulation-of-uncontrolled-atmospheric]
Text. Filename: Morgado_etal_Scitech_2022_Multi_fidelity_approach_for_aerodynamic_modelling_and_simulation_of_uncontrolled_atmospheric.pdf
Accepted Author Manuscript
License: Strathprints license 1.0

Download (13MB)| Preview


This paper proposes a multi-fidelity approach to the modelling and simulation of destructive atmospheric re-entry of human-made space objects. The presence of fragments, generated during the demise process, and the complex geometries of the objects determine the formation of complex flow features that need to be accurately resolved to limit the uncertainty on the ground impact risk. Critical to the determination of the dynamics of the fragments is the ability to correctly predict aerothermodynamic loads. The paper proposes an approach to the integration of expensive high-fidelity Computational Fluid Dynamics (CFD) solvers with fast low-fidelity methods for aerothermodynamics load calculation, that achieves a favourable trade-off between cost and accuracy. This multi-fidelity aerothermal approach is coupled with a 6-dof dynamic model to determine the motion of the fragments. For the high-fidelity modelling, a quasi-steady approach is used to determine the dynamics of the fragments in the instant following the breakup. The approach is validated with experimental data. Finally, a test case is presented to demonstrate the effectiveness of the proposed multi-fidelity at reducing the uncertainty in destructive re-entry predictions.


Morgado, Fábio, Peddakotla, Sai Abhishek, Garbacz, Catarina, Vasile, Massimiliano L. ORCID logoORCID: and Fossati, Marco ORCID logoORCID:;