MétaCan
Menu
← Back to cohort
Record W2392523232 · doi:10.1149/ma2016-01/27/1339

(Invited) A Systematic Approach to Develop and Validate Models for the Design of Membrane Electrode Assemblies

2016· article· en· W2392523232 on OpenAlexaff
Andreas Pütz, Marc Secanell

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of AlbertaAutomotive Fuel Cell Cooperation (Canada)
Fundersnot available
KeywordsAgglomerateCombustionDiffusionSimulationComputer scienceMechanical engineeringMechanicsMaterials scienceEngineeringChemistryPhysicsComposite materialThermodynamics

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.015
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.013

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.

Opus teacher head0.028
GPT teacher head0.214
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting Abstracts→Same topicFuel Cells and Related Materials→French-language works237,207→