Inference methods for gas/surface interaction models: from deterministic approaches to Bayesian techniques

Autor: Anabel del Val, Olivier P. Le Maître, Olivier Chazot, Pietro M. Congedo, Thierry E. Magin
Přispěvatelé: Uncertainty Quantification in Scientific Computing and Engineering (PLATON), Centre de Mathématiques Appliquées - Ecole Polytechnique (CMAP), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), von Karman Institute for Fluid Dynamics (VKI), Centre National de la Recherche Scientifique (CNRS), UTOPIAE, European Project: 722734,H2020 Pilier Excellent Science,H2020-MSCA-ITN-2016,UTOPIAE(2017)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: International Conference on Uncertainty Quantification & Optimisation
UQOP 2020-International Conference on Uncertainty Quantification & Optimisation
UQOP 2020-International Conference on Uncertainty Quantification & Optimisation, UTOPIAE, Nov 2020, Brussels, Belgium
Advances in Uncertainty Quantification and Optimization Under Uncertainty with Aerospace Applications ISBN: 9783030805418
Popis: International audience; In this work we review selected experiments and inference methods for the determination of atmospheric entry gas/surface interaction models for air catalysis and nitrogen ablation. Accurate prediction of the gas/surface interaction during spacecraft reentry remains a challenging problem for thermal protection system design. Attempts to model the surface chemistry of catalytic and ablative materials must account for experimental and model uncertainties. We review two sets of experiments and models adopted in the relevant literature for the rebuilding of catalytic properties and nitridation reaction efficiencies. The review is enriched with new perspectives to these problems by using dedicated Bayesian methods.
Databáze: OpenAIRE