진학프로석·박사 채용 접수 플랫폼

Carbon Capture and Storage (CCS) has been identified as the sole solution to significantly reduce carbon footprint of industries involving high temperature processes and with high carbon emissions. While different kind of carbon capture technologies are considered (pre-combustion capture, post-combustion capture, and oxy-fuel combustion), Chemical Looping Combustion (CLC) has been identified as one of the most viable solutions to capture CO2 with minimal cost. The key to the process lies in the use of an oxygen carrier material, which perform combustion under airless atmosphere allowing the production of high purity CO2 flue gas easy to store or use. Moreover, when biomass is used as a single fuel or in coprocessing, carbon capture efficiency might be enhanced up to negative emissions.

Within this context, the Oxy3C project has been launched as part of PEPR SPLEEN, aiming to deliver industry-ready results and tools for innovative developments. In this framework, 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 application case focuses on the fuel reactor of the CLC process. In this fluidized bed reactor, the gas-phase fuel may undergo pyrolysis in high-temperature, oxygen-free, regions leading to significant soot formation. Minimizing this phenomenon is essential to maintaining reactor performance and efficiency. This research aims to develop numerical tools to better understand soot formation mechanisms and support the design of future CLC reactors to mitigate clogging risks.

The study will focus on accelerating detailed gas-phase chemical mechanisms and soot models to make them suitable for integration into CFD simulations. Reference gas-phase mechanisms and validation measurements will be provided by project partners specializing in combustion kinetics and soot formation. These detailed mechanisms will first be reduced and then used to generate extensive databases for neural network training, aiming to replace kinetic solvers in 3D CFD codes and significantly accelerate simulations. In addition, the coupling of the accelerated kinetic model with detailed soot models will be investigated to ensure consistency and accuracy. Machine Learning techniques will also be explored to further enhance the acceleration of soot modeling.

The primary objective of this postdoctoral position is to train and a priori validate the accelerated models, ensuring they meet the required accuracy and generalization criteria. If feasible within the contract duration, their application in 3D CFD simulations will also be considered.

IFPEN supervisor: Dr. AUBAGNAC Damien, Numerical Modelling of Energetic Systems department, damien.aubagnac@ifpen.fr, https://orcid.org/0000-0003-3995-4920
Location: IFP Energies nouvelles, Rueil-Malmaison, France
Duration and start date: 18 months, starting in third or fourth quarter 2025
Employer: IFP Energies nouvelles, Rueil-Malmaison, France