Rashen Lou Omongos

rashen.omongos@u-picardie.fr

322825341

Rashen Lou
Omongos

• PhD

Tél. : 0322825341

Contact

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Descriptif des activités de recherche

This project is a collaboration between AC2T research GmbH, the Laboratoire de Réactivité et Chimie des Solides (joint research unit be-tween CNRS & Université de Picardie Jules Verne) and the R&D Advanced Technologies Division of Toyota Motor Europe.
The goal of this research is to develop a physics-based model of the membrane electrode assembly resulting from the manufacturing process of the proton-exchange membrane fuel cell (PEMFC). Our aim is simulate, numerically, the impact of the assembly conditions on the mesostructural and operational properties of the functional layers, namely electrode, microporous layer (MPL), and gas diffusion layer (GDL).

Parcours

·         Master (University of Trieste, 2023)

Compétences

Ab initio, DFT, Gaussian, Amsterdam Density Functional, ORCA, LAMMPS, Geodict

Projets en cours

Physics-Based Modelling of the Membrane Electrode Assembly / 36 months / Horizon Europe Marie Sk?odowska-Curie Actions (MSCA) Doctoral Networks project ?BLESSED’

Digital correlation analysis and optimization of microporous layer through a machine learning workflow for PEMFC applications

Rashen Lou Omongos, Diego E. Galvez-Aranda, Francisco Fernandez, András Vernes, Alejandro A. Franco

Journal of Power Sources,

2025

Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties

Francisco Fernandez, Soorya Saravanan, Rashen Lou Omongos, Javier F. Troncoso, Diego E. Galvez-Aranda and Alejandro Franco

npj computational materials,

2025

Machine learning-driven optimization of gas diffusion layer microstructure for PEM fuel cells

Rashen Lou Omongos, Diego E. Galvez-Aranda, Franco M. Zanotto, András Vernes, Alejandro A. Franco

Journal of Power Sources,

2025

Machine Learning-Driven Optimization of Gas Diffusion Layer Microstructure for PEM Fuel Cells

Rashen Lou Omongos ,Diego Galvez-Aranda ,Franco Zanotto, Alejandro A. Franco

ChemRxiv,

2024

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Publications

Machine learning-driven optimization of gas diffusion layer microstructure for PEM fuel cells

Digital correlation analysis and optimization of microporous layer through a machine learning workflow for PEMFC applications

Transfer learning assessment of small datasets relating manufacturing parameters with electrochemical energy cell component properties