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Record W4230317617 · doi:10.32920/ryerson.14649780

Proton exchange membrane (PEM) fuel cell parametric study via mathematical modeling and numerical simulation

2021· preprint· en· W4230317617 on OpenAlexafffund
Rihab Jaralla

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsProton exchange membrane fuel cellFinite element methodParametric statisticsComputer scienceMechanicsMechanical engineeringEngineeringFuel cellsPhysicsMathematicsStructural engineering

Abstract

fetched live from OpenAlex

In the proton exchange membrane (PEM) fuel cell study, numerical analysis of complex and coupled multi-disciplinary processes involving the subjects of fluid dynamics, heat transfer, mass transport, and electrochemistry has been attempted over the past few decades. However, many resulting models are, in spite of fancier functionalities such as three-dimensionality, too complex to implement on account of the digital hardware requirement as well as computation time consumption. On the other hand, three-dimensional analytical models reported in literature look much simple, but they are embedded by a number of fairly unrealistic assumptions and, hence, lead to significantly weakened usability. In this thesis, a set of detailed two-dimensional non-isothermal computational models for PEM fuel cells in x-y and y-z planes are developed, which aims at the equivalency with the 3D PEM fuel cell model and, moreover, gains more insights with significantly reduced computational cost. The complete model consisting of the equations of continuity, momentum, energy, species concentrations, and electric potentials in different regions of a PEM fuel cell are numerically solved using the finite element method implemented into a commercial CFD (COMSOL) code. A comprehensive comparison with the experimental data has been performed to validate the 2D models developed in this study. On the basis of simulations of various flow and transport phenomena in an operational PEMFC, a systematic parametric study is conducted using the present developed PEM fuel cell models. A number of operating and design parameters are examined, including the operating pressure, ambient temperature, relative humidity, the porosity of the gas diffusion layer (GDL), the effective porosity of catalyst layer (CL), the porosity of membrane (M), the proton conductivity and the air inlet velocity at cathode side. The obtained results of this study revelaed that the membrane porosity, and air inlet velocity have considerable effects on the water content in the membrane, thus it is essential to select the proper values of these parameters to improve water management in the cell and avoid dehydration the membrane or flooding the electrode. Also, it is found that increasing air velocity at the inlet of the cathode gas channel has a significant effect on the temperature distribution in PEM fuel cell, as the temperature a noticeably dropped with higher inlet air velocity. The numerically results also found that with higher porosities of gas diffusion layers (GDLs) and catalyst layers (CLs), the performance of PEM fuel cell improved. In addition, it found that a higher performance can be achieved when fuel cell operated with reasonably higher operating temperature, operating pressure, proton conductivity and ensuring a full hydration of the reactants. The outcome of this study demonstrates that the present developed PEM fuel cell models can serve as a useful tool for understanding of transport and electrochemical phenomena in PEM fuel cell as well as for optimization of cell design and operating conditions.

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)0.000
metaresearch head score (Gemma)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.024
GPT teacher head0.247
Teacher spread0.224 · 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
GenreEmpirical

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

Citations1
Published2021
Admission routes2
Has abstractyes

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