Optimal Design and Operation of Dual-Ejector PEMFC Hydrogen Supply and Circulation System
Bibliographic record
Abstract
A proton exchange membrane fuel cell (PEMFC) system requires an adequate hydrogen supply and circulation to achieve its expected performance and operating life. An ejector-based hydrogen circulation system can reduce the operating and maintenance costs, noise, and parasitic power consumption by eliminating the recirculation pump. However, the ejector’s hydrogen entrainment capability, restricted by its geometric parameters and flow control variability, can only operate properly within a relatively narrow range of fuel cell output power. This research introduced the optimal design and operation control methods of a dual-ejector hydrogen supply/circulation system to support the full range of PEMFC system operations. The technique was demonstrated on a 70 kW PEMFC stack with an effective hydrogen entrainment ratio covering 8% to 100% of its output power. The optimal geometry design ensured each ejector covered a specific output power range with maximized entrainment capability. Furthermore, the optimal control of hydrogen flow and the two ejectors’ opening and closing times minimized the anode gas pressure fluctuation and reduced the potential harm to the PEMFC’s operation life. The optimizations were based on dedicated computational fluid dynamics (CFD) and system dynamics models and simulations. Bench tests of the resulting ejector-based hydrogen supply/circulation system verified the simulation and optimization results.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".