An Analytical Model of a Hollow Fiber Membrane Humidifier in Hydrogen Fuel Cell Systems Using Response Surface Method
Bibliographic record
Abstract
Hydrogen fuel cell is a potential alternative power source for vehicles, which has a significant role in decarbonizing the future transport sector.Proton exchange membrane fuel cell is widely used because of its suitable temperature and power density.Performance and durability of stacks are important factors in the development of hydrogen fuel cell-powered vehicles.As a key subsystem, a hollow fiber membrane humidifier is investigated in this study to manage the water entering fuel cell electrodes.Parametric experiments of water transport through the membrane were done before applying the response surface method to establish a regression model based on fundamental operating parameters.The reliable regression equation of water transport performance (𝑅 2 = 0.988) was used to develop an analytical model of a hollow fiber membrane humidifier.The performance of the humidifier including the water transfer rate and outlet relative humidity were evaluated and scrutinized to process a better system for hydrogen vehicles.Fluid flow and transfer process were investigated under the isothermal conditions and cross-counter flow arrangements.The proposed Simulink-based model was properly validated with data from a practical humidifier, meaning that the model can be used to design humidification subsystems and further develop fuel cell systems.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".