Thermal Contact Resistance Between Gas Diffusion Layer and Graphite Bipolar Plate: Modeling and Experiments
Bibliographic record
Abstract
An analytic, mechanistic robust model is developed to predict the thermal contact resistance (TCR) between fibrous porous media such as GDLs and flat surfaces. The model, which accounts for the salient and realistic geometrical parameters, mechanical deformation, and thermal spreading/constriction resistances, is successfully validated with new experimental data of the TCR between GDLs and graphite bipolar plates. Several parametric studies are performed to reveal the effect of fiber specifications such waviness and also GDL properties on the TCR. For instance, it is found that, interestingly enough, fiber length does not have any effect on TCR at constant porosity. From the parametric studies, the critical values of key parameters effective on TCR are also identified, which can be very useful in GDL manufacturing and fuel cell design in viewpoint of heat management. The presented model can be readily plugged into fuel cell models for simulations and modeling purposes. Overall, the model is developed in a general form to be also applicable, with only minor modifications, to other fibrous media such as fibrous catalyst layers, metal foams, and heat exchangers.
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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) | 0.001 | 0.001 |
| 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".