Determining Local Transport Properties in Gas Diffusion Layers Using Pore Network Modelling
Bibliographic record
Abstract
Understanding the relationship between local liquid water distribution in gas diffusion layers (GDLs) and transport properties in polymer electrolyte fuel cells can lead to improved GDL and flow-field channel configurations for improved performance. Modelling studies have shown that transport properties can spatially vary in the through-plane direction and under different regions of the flow-field [1], [2]. Further modelling studies may unveil unique insights on spatially varying properties of the GDL at different operating conditions. In this study, x-ray synchrotron imaging was used in conjunction with pore network modelling to reveal transport properties local to the GDL in the land and channel regions. First, liquid water distribution within the GDL was obtained using in operando x-ray synchrotron imaging. Micro computed tomography images of the GDL were obtained and used to generate representative pore network models of the materials. Pore network models were partially invaded from stochastically selected inlet pores using invasion percolation to match the average experimental saturation of land, channel, and overall regions. Next, we performed transport simulations on the partially saturated pore networks to determine the diffusivity, permeability, and oxygen transport resistance of each GDL region. Oxygen transport resistance values determined from the simulations were compared to experimental values obtained via limiting current experiments to isolate the contribution of the GDL to transport resistance at limiting current. The methodology presented in this work can be used to evaluate transport properties of various GDL designs. The local transport properties obtained in this work will be incorporated into computational fluids dynamics modelling to improve simulation accuracy. [1] P. A. García-Salaberri, J. T. Gostick, G. Hwang, A. Z. Weber, and M. Vera, "Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of local saturation and application to macroscopic continuum models," Journal of Power Sources , vol. 296, pp. 440–453, 2015, doi: 10.1016/j.jpowsour.2015.07.034. [2] N. Ge et al. , "Resolving the gas diffusion layer substrate land and channel region contributions to the oxygen transport resistance of a partially-saturated substrate," Electrochimica Acta , p. 135001, 2019, doi: 10.1016/j.electacta.2019.135001.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".