Multiphysics Simulation of the Flow Battery Cathode: Cell Architecture and Electrode Optimization
Bibliographic record
Abstract
A model of a hydrogen – bromine redox flow battery cathode with interdigitated flow channels was developed to investigate the effect of both the morphology of the fibrous electrode as well as the overall architecture of the cell. The fiber morphology was determined by the fiber diameter and porosity while the cell architecture was determined by the electrode thickness as well as the channel and rib widths. A comprehensive parametric study was performed looking at the effects these parameters had on the overall performance of the cell. A kinetic parameter was also varied to allow for different catalytic systems. This generalized the scope of the work and made it applicable for a large variety of systems. The importance of fiber morphology was found to be heavily dependent on kinetics. As expected, slow reacting systems performed better with smaller fibers and lower permeability while the opposite was true for highly kinetic systems. The width of the domain was the most important characteristic, in all cases the narrowest channel had the highest reaction rate. This was also largely true for the rib widths; in most cases the narrowest rib performed best. The effect of the electrode thickness was found to vary depending on the permeability and kinetics of the system. A non-dimensional group was proposed to represent the entire dataset and visualize the relative importance of each parameter.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".