Emissions Analysis for a Hydrogen-Fueled Low-Pressure-Ratio Split-Cycle Engine
Bibliographic record
Abstract
Recuperated low-pressure-ratio split-cycle engines represent a promising engine configuration for applications like transportation and stand-alone power generation by offering a potential efficiency as high as 60%. However, it can be challenging to achieve the stringent NOx emission standard, such as Euro 6 limit of 0.4 gNOx/kWh, due to the exhaust cylinder high intake temperature. This paper presents experimental investigation of hydrogen-air combustion NOx emissions for such engines for the first time. Experiments are carried out using a simplified constant-volume combustion chamber with glow-plug ignition. Two fuel injection techniques are performed: direct injection and injection via a novel convergent-divergent injector. For the direct injection scenario, NOx levels are unsatisfactory with respect to the Euro 6 standards over a range of operating temperatures from 200 °C to 550 °C. Recorded NOx levels can reach twice the permissible limit which necessitates the implementation of after-treatment techniques. Interestingly, NOx emissions are found to be less by around 50% in the case of the novel injector. The enhanced fuel-air mixing mechanism using the convergent-divergent injector increases the ignition delay and shifts the combustion flame towards the premixed conditions and results in lower NOx levels.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".