Machine Learning based reduced models for the aerothermodynamic and aerodynamic wall quantities in hypersonic rarefied conditions
| hal.structure.identifier | DMPE, ONERA, Université de Toulouse [Toulouse] | |
| dc.contributor.author | SCHOULER, Marc | |
| hal.structure.identifier | DMPE, ONERA, Université de Toulouse [Toulouse] | |
| dc.contributor.author | PRÉVEREAUD, Ysolde | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | MIEUSSENS, Luc | |
| dc.date.issued | 2022-12-28 | |
| dc.identifier.issn | 0094-5765 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | en | |
| dc.publisher | Elsevier | |
| dc.subject | MODELES DE SUBSTITUTION | |
| dc.subject | AEROTHERMODYNAMIQUE | |
| dc.subject | AERODYNAMIQUE | |
| dc.subject | ÉCOULEMENT HYPERSONIQUE RAREFIE | |
| dc.subject | RENTREE DANS LA TERRE | |
| dc.subject.en | SURROGATE MODELS | |
| dc.subject.en | AEROTHERMODYNAMIC | |
| dc.subject.en | AERODYNAMIC | |
| dc.subject.en | RAREFIED HYPERSONIC FLOW | |
| dc.subject.en | EARTH REENTRY | |
| dc.title.en | Machine Learning based reduced models for the aerothermodynamic and aerodynamic wall quantities in hypersonic rarefied conditions | |
| dc.type | Article de revue | |
| dc.identifier.doi | 10.1016/j.actaastro.2022.12.039 | |
| dc.subject.hal | Physique [physics] | |
| dc.subject.hal | Chimie | |
| dc.subject.hal | Sciences de l'ingénieur [physics] | |
| bordeaux.journal | Acta Astronautica | |
| bordeaux.page | 83-106 | |
| bordeaux.volume | 204 | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-03932397 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-03932397v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Acta%20Astronautica&rft.date=2022-12-28&rft.volume=204&rft.spage=83-106&rft.epage=83-106&rft.eissn=0094-5765&rft.issn=0094-5765&rft.au=SCHOULER,%20Marc&PR%C3%89VEREAUD,%20Ysolde&MIEUSSENS,%20Luc&rft.genre=article |
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