Topics Covered
Main Research Activities
The PhD project, which is a collaboration between LRCS (France) and Forschungszentrum Jülich (Germany), targets the development of a computational methodology to predict and optimise the impact of manufacturing parameters on the architectural and electrochemical properties of solid oxide cells for electrolysis production of hydrogen and fuel cells for electricity generation. This methodology will be based on an original coupling of mechanistic multi-scale modelling with machine learning, and using experimental data from a prototyping line. Although the methodology is demonstrated in the PhD thesis for these solid oxide electrochemical devices, it could also find a strong interest in the manufacturing and engineering of composite materials in general.Supervisors: Prof. Dr. Alejandro A. Franco (LRCS, France), Prof. Dr. Olivier Guillon (FZ Jülich, Germany).
Academic Training
- Master of Science in Computational Engineering, Ruhr-Universität Bochum, Germany.
- Bachelor of Science in Mathematics, Vietnam National University Ho Chi Minh City - University of Science.
Professional skills
A Hybrid Modelling Approach Coupling Physics-based Simulation and Deep Learning for Battery Electrode Manufacturing Simulations
Utkarsh Vijay, Diego E. Galvez-Aranda, Franco M. Zanotto, Tan Le-Dinh, Mohammed Alabdali, Mark Asch, Alejandro A. Franco
Energy Storage Materials, 2024
Time-Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction
Diego E. Galvez-Aranda, Tan Le Dinh, Utkarsh Vijay, Franco M. Zanotto, Alejandro A. Franco
Advanced Energy Materials, 2024
