Investigating the combinations of operating parameters of PEMFC computational results using the Taguchi Method
Bibliographic record
Abstract
Proton Exchange Membrane Fuel Cells (PEMFC) is considered a promising energy source due to higher energy efficiency, low pollution, fast startup time, and low operating temperature. Under simplified conditions of constant temperature and one-dimensional flow, through the channel, and zero-flux boundaries the model was solved. The model was validated using COMSOL Multiphysics software and experimental results with a 4.22 % and 5.5 % deviation. The Taguchi Method was used to study the operating parameters of PEMFCs and to identify optimal combinations for best output along with developing equations to predict maximum power density. The delta i.e. the difference between the mean of high- and low-level Signal to Noise (S/N) ratios was calculated and found that the relative humidity was significant with value 11.07. The optimum combination is found with the help of the S/N ratio graph based on the larger the better for the performance application. The maximum power density value was predicted using the equation and found to be deviated by 16.5 % with the help of an L8 orthogonal array. Modified Taguchi approach with L4 orthogonal array with a fixed level of higher derivation parameter, reduced the error deviation further to 6.5 % with respect to the simulation results. The approach will be handy for predicting the performance with fewer trials.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".