Authors: Eric Langner | Hamidreza Dehghani | M. Hachemi | Elias Belouettar–Mathis | Ahmed Makradi | Thomas Wallmersperger | Sylvain Gouttebroze | Heinz A. Preisig | Casper Welzel Andersen | Qian Shao | Heng Hu | Salim Belouettar
Source: International Journal of Hydrogen Energy (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

Physics-based and data-driven modelling and simulation of Solid Oxide Fuel Cells This paper presents a comprehensive approach to multiscale and multiphysics modelling of Solid Oxide Fuel Cells (SOFCs) by combining physics-based simulations with data-driven techniques. The modelling approach is tailored to specific end-use scenarios, ensuring that parameter selection aligns with operational requirements for accurate and efficient SOFC design. The study begins by constructing Representative Volume Elements ( RVE s) from reconstructed microstructures, applying first-order homogenisation to upscale material properties, which are then incorporated into a macroscopic SOFC model. A major contribution is the structured model definition based on physical process entities, using a graphical representation of model topology. This approach simplifies complex system interactions by representing capacities (such as reservoirs, distributed systems, and interfaces) and transport processes (e.g., diffusion, convection, thermal diffusion), thereby enhancing clarity and improving the accuracy of SOFC performance simulations. A machine learning framework complements the physics-based modelling by training Artificial Neural Networks (ANNs) on simulation-generated datasets, delivering fast and reliable performance predictions. The study compares two optimisation techniques — Levenberg–Marquardt (LM) and Adam optimiser — demonstrating that LM is more effective for sparse datasets and smaller networks, whereas Adam performs better with large datasets and higher learning capacities. This hybrid modelling approach not only boosts predictive accuracy for SOFC performance but also lowers computational costs. By integrating physics-based simulations, machine learning, and a knowledge-driven simulation platform, this work advances SOFC design and optimisation, contributing to more efficient and cost-effective clean energy solutions. Additionally, the paper introduces a knowledge-driven simulation platform to enhance data management and integrate multiscale, multiphysics models. The platform leverages structured data models and ontological mappings to improve semantic interoperability, allowing for dataset reuse and validation across different simulation stages. This ensures a robust, reusable, and well-organised workflow, facilitating large-scale simulations and improving overall modelling accuracy. • Microstructure modelling links to SOFC properties. • Graphical-based modelling framework enhances clarity of complex SOFC processes. • Finite element analysis with non-isothermal fluid flow. • Machine learning evaluates SOFC performances with LM and Adam optimisers. • Semantic platform streamlines multi-scale modelling and data management. Fuel cells ; Oxide ; Solid oxide fuel cell ; Solid fuel ; Experimental data ; Physics ; Nuclear engineering ; Materials science ; Process engineering ; Chemistry ; Chemical engineering ; Physical chemistry ; Combustion ; Engineering ; Metallurgy ; Anode ; Mathematics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalysis and Oxidation Reactions