The Operational Mixture Limits in Engines Fueled With Alternative Gaseous Fuels
Bibliographic record
Abstract
The operation of engines whether spark ignition or compression ignition on a wide range of alternative gaseous fuels when using lean mixtures can offer in principle distinct advantages. These include better economy, reduced emissions, and improved engine operational life. However, there are distinct operational mixture limits below which acceptable steady engine performance cannot be sustained. These mixture limits are usually described as the “lean operational limits,” or loosely as the ignition limits which are a function of various operational and design parameters for the engine and fuel used. Relatively simple approximate procedures are described for predicting the operational mixture limits for both spark ignition and dual fuel compression ignition engines when using a range of common gaseous fuels such as natural gas/methane, propane, hydrogen, and some of their mixtures. It is shown that good agreement between predicted and corresponding experimental values can be obtained for a range of operating conditions for both types of engines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".