OpenFCST: An Open-Source Mathematical Modelling Software for Polymer Electrolyte Fuel Cells
Bibliographic record
Abstract
OpenFCST (open-source fuel cell simulation toolbox) is an open-source, finite element method based, multi-dimensional mathematical modeling software for polymer electrolyte fuel cells. The aim of the software is to develop a platform for collaborative development of fuel cell mathematical models. The philosophy, structure and main components of openFCST are presented. OpenFCST currently includes physical models for gas, electron, ion, ionomer-bound water and heat transport. It also contains effective transport media relations to estimate transport properties for gas diffusion layers, micro-porous layers and catalyst layers as well as several kinetic models for the fuel cell electrochemical reactions. OpenFCST has been structured as a toolbox such that it is easier for new users to integrate new physical models with existing framework. OpenFCST is used to analyze the impact of different kinetic models on a multidimensional cathode model and to study the main differences between a macro-homogeneous and several agglomerate models. Finally, openFCST is used to develop a three-dimensional model of a patterned catalyst layer. Results show that multi-step kinetic models improve fuel cell performance predictions, macro-homogeneous and ionomer-filled agglomerate models show similar performance for 100 nm radii agglomerates up to current densities of 2 A/cm 2 , and water-filled agglomerate models require negative surface charges to exist at the pore walls in order to provide results in-line with experimental data. Finally, a patterned catalyst layer with micro-pores is shown to improve electrode performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".