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Study of H2 - ammonia combustion kinetics: numerical modeling coupling physical models and machine learning

Study of H2 - ammonia combustion kinetics: numerical modeling coupling physical models and machine learning

Goals

Development of reduced-order models coupling chemical reactor networks and machine learning, to predict the behavior of hydrogen/ammonia mixtures with a focus on minimizing pollutant emissions.

Innovations

  1. New methodology for the development of optimized reduced-order kinetic mechanisms for computational fluid dynamics applications
  2. Machine-learning-assisted development of physics-preserving reduced-order models

PhD student:

Asija Tatiana Inciardi

Promoter

Alessandro Parente
Université Libre de Bruxelles

Co-promoter

Dr. Véronique Dias
Université Catholique de Louvain

Research Center

Tariq Benmara
Cenaero
Cécile Goffaux
Cenaero
Christophe Le Pen
Centre de Recherches Métallurgiques
Dr. Caroline Sainvitu
Cenaero
Xavier Vanden Eynde
Centre de Recherches Métallurgiques

Tasks

T11-1
Reduction and optimization of kinetic mechanisms
T11-2
CFD modeling of hydrogen-ammonia combustion in laboratory furnaces and experimental validation.
T11-3
Reduced-order modeling based on laboratory-scale furnace data
T11-4
Characterization of the material impact of H2 NH3 flames

Asija Tatiana Inciardi presents her project during the Kick-off - September 13, 2024.

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