SCHOULER, Marc; PRÉVEREAUD, Ysolde; MIEUSSENS, Luc

doi:10.1016/j.actaastro.2022.12.039

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SCHOULER, Marc
DMPE, ONERA, Université de Toulouse [Toulouse]

PRÉVEREAUD, Ysolde
DMPE, ONERA, Université de Toulouse [Toulouse]

MIEUSSENS, Luc
Institut de Mathématiques de Bordeaux [IMB]

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Ce document a été publié dans

Acta Astronautica. 2022-12-28, vol. 204, p. 83-106

Elsevier

Résumé en anglais

Since their development at the end of the 50s, panel methods were widely used for the fast simulation of aerospace objects reentry. Although improvements were proposed for the continuum regime formulations, the bridging functions usually employed in the transitional regime did not go through major changes since then. With the current interest in designing Very Low Earth Orbit satellites and more efficient reentry vehicles, a greater level of preciseness is now required for the fast computation of the aerodynamic and aerothermodynamic wall quantities in rarefied regime. In this context, this paper presents a new approach to build Machine Learning based surrogates going from the choice of the design variables and the Design of Experiments, to the models training and evaluation. Hence, kriging and Artificial Neural Networks are respectively trained to predict the pressure and heat flux stagnation coefficients, and the pressure, friction and heat flux coefficient distributions in the rarefied portion of any aerodynamic shape’s re-entry.< Réduire

Mots clés

MODELES DE SUBSTITUTION

AEROTHERMODYNAMIQUE

AERODYNAMIQUE

ÉCOULEMENT HYPERSONIQUE RAREFIE

RENTREE DANS LA TERRE

Mots clés en anglais

SURROGATE MODELS

AEROTHERMODYNAMIC

AERODYNAMIC

RAREFIED HYPERSONIC FLOW

EARTH REENTRY