Investigating Inlet Condition Effects on PEMFC GDL Liquid Water Transport through Pore Network Modeling
Bibliographic record
Abstract
Polymer electrolyte membrane fuel cells (PEMFCs) are promising electrochemical energy conversion devices due to their high efficiency, zero-local emissions, and rapid start-up capability. Water management is a vital issue in the PEMFC, as the accumulation of water directly affects its operating efficiency. During operation the membrane must be sufficiently humidified in order to achieve high proton conductivity; however, at high current densities, accumulation of liquid water in the cathode gas diffusion layer (GDL) blocks oxygen pathways, preventing this reactant from reaching the catalyst, which in turn hinders performance. Effective water management strategies are essential to improve PEMFC performance and require a detailed understanding of liquid water transport in the GDL. In a fuel cell stack, the cell components are assembled under compressive loads to prevent gas leakage; however, high compression of the GDL may affect cell performance. The non-uniform compression of the GDL, due to its contact with the bipolar plate, significantly alters the microstructure and consequently the dynamics of liquid water transport through the GDL [1]. A number of attempts have been made in experimental visualization of liquid water transport within compressed GDLs, but more work is required to evaluate these effects quantitatively [1-3]. The purpose of this study is to numerically investigate the effects of compression on liquid water transport through PEMFC GDLs. In this work, two types of carbon paper GDLs are compressed. At each compression value, volumetric X-ray tomography images of the samples are obtained. The resulting greyscale images are segmented using a novel thresholding algorithm. A watershed algorithm [4] is used to extract pore networks of the GDL. Pore network modeling with invasion percolation is employed to determine the liquid water profile within the GDL over the range of compression states. The results of this study provide a deeper knowledge of how compression and the GDL microstructure affect the movement of liquid water. References: [1] Bazylak, A., Sinton, D., Liu, Z.-S., Djilali, N. (2007) “Effect of Compression on Liquid Water Transport and Microstructure of PEMFC Gas Diffusion Layers,” Journal of Power Sources, 163 (2), 784-792 [2] Yip, R., Bazylak, A. (2012) “Investigation of liquid water saturation of compressed PEMFC GDLs using micro-computed tomography.” ESFuelCell2012-91446, American Society of Mechanical Engineers (ASME), 10th International Fuel Cell Science, Engineering and Technology Conference, San Diego, California, July 23-26, 2012 [3] Challa, P., Hinebaugh, J., Bazylak, A. (2011) “Comparison of Water Thickness Profiles of Compressed PEMFC GDLs.” ESFuelCell2011-54340, American Society of Mechanical Engineers (ASME), 9th International Fuel Cell Science, Engineering and Technology Conference, Washington, D.C. August 7-10, 2011 [4] Hinebaugh, J. Bazylak, A. (2012) “Pore Network Modeling to Study the Effects of Common Assumptions in GDL Liquid Water Invasion Studies.” ESFuelCell2012 -91466, American Society of Mechanical Engineers (ASME), 10th International Fuel Cell Science, Engineering and Technology Conference, San Diego, California, July 23-26, 2012
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 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) | 0.000 | 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 | 0.001 |
| 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".