Artificial intelligence-assisted optimization and multiphase analysis of polygon PEM fuel cells
Bibliographic record
Abstract
This study introduces innovative, optimized hexagonal and pentagonal PEM fuel cell models. The inlet pressure and temperature serve as input parameters, while power consumption and output power are objective parameters. The results of Computational Fluid Dynamics (CFD) analysis are then trained with deep neural networks and modeled using polynomial regression. Target functions are derived using the Response Surface Method (RSM) and optimized with the NSGA-II genetic algorithm. Compared to the base model, our optimized pentagonal and hexagonal PEM fuel cells significantly boost the output current density by 21.8% and 39.9%, respectively. Additionally, power consumption is lower: the pentagonal model uses 0.198%, and the hexagonal model uses 6.21% of the production power on average. Our proposed designs enhance PEM fuel cell performance by significantly boosting power production while minimizing power consumption.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".