Postdoc ML-Accelerated Chemistry and Soot Modeling for 3D CFD H/F

Détail de l'offre

Informations générales

Référence

2025-1926  

Attributs du poste

Intitulé du poste

Postdoc ML-Accelerated Chemistry and Soot Modeling for 3D CFD H/F

Statut

Post-Doc

Contrat

Contrat Post-Doctorant

Durée du CDD (exprimée en mois)

12

Temps de travail

Temps plein

Localisation du poste

Lieu d'exercice

Rueil-Malmaison

Description du poste

Contexte

As part of the OXY3C project (PEPR SPLEEN), IFP Energies Nouvelles (IFPEN) is seeking a postdoctoral researcher to develop innovative approaches for modeling soot formation in Computational Fluid Dynamics (CFD) simulations of oxygen-free reactors. The study focuses on the fuel reactor of Chemical Loop Combustion (CLC) processes, where gaseous fuel can undergo pyrolysis into soot due to high temperatures and the absence of gaseous oxygen.

The objective is to accelerate the resolution of gas-phase chemical kinetics and the prediction of soot emissions to make them compatible with CFD simulations. This starts with the reduction of detailed mechanisms. The reduced mechanisms will be used to create databases for neural network training, replacing kinetic solvers to further accelerate simulations.
The coupling of accelerated kinetic models with soot models will be investigated to ensure prediction consistency. Machine Learning techniques will also be explored to further improve the acceleration of soot modeling. The postdoctoral researcher will validate these models and explore their application in 3D CFD simulations if feasible.

The Ph.D. degree must have been obtained no more than three years before the start of the postdoctoral contract.

Mission(s) principale(s) et activités

Reduction of detailed gas-phase chemical mechanisms for application in CFD simulations of CLC processes.

  • Generation of databases for chemistry tabulation or neural network training to accelerate soot modeling.
  • A priori validation of the trained models by comparison with experimental data from project partners.
  • Collaboration with internal and external partners to ensure consistency between model development and experimental validation.

Déplacements à prévoir

Déplacements occasionnels de quelques jours en France et à l’étranger.
Le poste est partiellement télétravaillable

Critères candidat

Compétences techniques et aptitudes

  • Programming skills (e.g., Python, C++) and familiarity with database management are highly appreciated.
  • Experience with CFD simulations and/or chemical mechanism reduction.
  • Proficiency in English is required. Knowledge of French or a willingness to learn it is appreciated.

Diplôme(s), niveau d'études

Ph.D. in Chemical Engineering, Applied Mathematics, Combustion, or a related field.

Expérience(s) professionnelle(s) souhaitée(s)

For further information and to apply, please contact:
damien.aubagnac@ifpen.fr

Information publication

Accessible aux personnes porteuses d'un handicap :

Oui