(Invited) A Systematic Approach to Develop and Validate Models for the Design of Membrane Electrode Assemblies
Bibliographic record
Abstract
The modeling and simulation landscape for fuel-cells has increased dramatically over last years. A number of commercial and OpenSource (e.g. OpenFCST, FAST-FC, DuMux) software packages are available to the aspiring researcher or engineer in academia and industry. The complexity ranges from purely analytic models with O(1) number of parameters to fully numerically models with O(100) input parameters. However, despite the apparent abundance on model and simulation tools, their practical use for the design of next generation MEA components like the catalyst layers or porous transport layers has been very limited and progress has been mainly driven by large, slow and expensive experimental studies. This stands in stark contrast to combustion engine design where virtual prototypes are built, tested and optimized through numerical simulations before a physical prototype is built. In the fuel-cell community this lack of application tends to be explained by the large number of equations and the corresponding computational resources needed to tackle the problem. The fact that combustion engineering does not suffer this problem, despite an equally high level of complexity, seems to indicate that numerical complexity is not the root cause. Browsing the numerical codes and the modeling literature shows little consensus about the correct description of the individual physical effects. A few of the topics of disagreement are: Gas transport: Fickian diffusion, Maxwelll-Stefan diffusion, Dusty Gas models, ... Membrane water transport: concentration driven, liquid pressure driven, ... Reaction at the catalyst surface: macro-homogeneous models, ionomer filled agglomerate models, water filled agglomerate models,thin film models, ... Electrochemical reaction mechanisms: Tafel, Butler-Volmer, Double-Trap, Dual-Path, ... ... In this tutorial, a consistent validation strategy with a focus on gas mass transport is outlined and demonstrated. We will take AFCCs baseline MEA model platform OpenFCST through a validation with ex-situ experiments: Permeability and Diffusivity validation through in-situ experiments: Limiting current methods Polarization curves under different operating conditions Thickness variations validation through imaging techniques: Diffusion simulations on reconstructed catalyst layers The impact of different model configurations will be studied and we will investigate if a validation strategy like this can pinpoint the weaknesses or strengths of the model configuration chosen. References OpenFCST (www.openfcst.org) M. Bhaiya, A. Putz and M. Secanell, "Analysis of non-isothermal effects on polymer electrolyte fuel cell electrode assemblies", Electrochimica Acta, 147C:294-309, 2014. DOI: 10.1016/j.electacta.2014.09.051 Figure 1
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".