Effect of Hydrogen Content in Hydrogen Natural-Gas Fuel Mixtures on Emissions in a Lean-Burn IC Engine
Bibliographic record
Abstract
The influence of hydrogen content in hydrogen-natural gas fuel mixtures on the emissions of a lean-burn spark ignition engine has been examined under representative operating conditions, a mid load and a high load. The hydrogen content in the fuel gas mixtures was varied from 0 to 30% with the balance made up of natural gas. The primary effect on emissions was to influence the tradeoff between NOx and hydrocarbon emissions. At the mid-load condition, increasing the hydrogen content from 0 to 15% at constant equivalence ratio reduced the HC emissions by 80% with little change in NOx emissions. Increasing from 15 to 30% hydrogen content reduced the HC emissions a further 50% but increased the NOx emissions by 16%. At the high load condition, the overall result of increasing the hydrogen content was to increase the NOx emissions substantially without significantly reducing the HC emissions. The impact of increasing hydrogen content on engine efficiency is similar to the impact on hydrocarbon emissions. At the mid-load condition, engine efficiency was increased by increasing hydrogen content, but with diminishing returns. An increase from 0 to 5% hydrogen content provides a significant benefit under marginal combustion conditions but further increases in hydrogen content are less effective.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".