OpenFCST: An Open-Source Mathematical Modelling Software for Polymer Electrolyte Fuel Cells
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it