Development and assessment of a hydrogen car operated by a vertical axis wind turbine
Bibliographic record
Abstract
In this study, an electrolysis unit which is driven by a vertical wind turbine in a fuel cell passenger car is investigated for hydrogen production thermodynamically through exergy and energy approaches. The proposed system essentially consists of a vertical axis wind turbine, an electrolysis unit, and a fuel cell unit, as well as the auxiliary units. The electrical energy generated by the wind turbine is used to operate the electrolyzer for hydrogen generation. The hydrogen generated is then sent to fuel cell for driving the car and/or hydrogen tank for storage. Both energy and exergy efficiencies of the electrolysis unit are found to be approximately 63% at 975 mA/cm2 current density. Additionally, the energy and exergy efficiencies of the proposed system are calculated as 39% and 56%, respectively. Furthermore, hydrogen production amount from the electrolyzer is calculated using two different test-driving procedures, which are accepted worldwide. At the end of driving cycle, the amount of hydrogen produced per cell in the WLTP (Worldwide Harmonized Light Vehicles Test Procedure) driving cycle is calculated as 17.06 mol and in the FTP −75 (Federal Test Procedure) driving cycle is calculated as 6.88 mol. The amounts of oxygen are found as 8.53 mol for the WLTP driving cycle and 3.44 mol for the FTP-75 driving cycle, respectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".