Emissions Variability in Gaseous Fuel Direct Injection Compression Ignition Combustion
Bibliographic record
Abstract
Measurements of ignition characteristics and emissions were made in a shock tube facility operating at engine-relevant conditions. Methane and methane/ethane fuels were injected down the centerline of the shock tube using an electronically controlled prototype gaseous fuel injector developed by Westport Innovations. Air was preheated and compressed using a reflected shock technique that produced run times of 4-5 ms. Particulate matter (PM) emissions were found to be highly intermittent. In only 6 out of 97 experiments was PM detected above background levels. In all of these 6 sooting experiments ignition kernels were located relatively close to the injector tip and ignition occurred prior to the end of fuel injection. Using the large orifice injector tip with pure methane fuel, PM was detected in 4 out of 28 experiments; using the small orifice with pure methane fuel, no PM was detected in any of 50 experiments. When 10% ethane was added to the fuel, PM was detected in 2 out of 20 experiments. Orifice size was found to significantly influence emissions of nitrogen oxides (NOx). Emissions were reduced by over an order of magnitude when the orifice size was reduced by a factor of 4. Run-to-run variability in NOx emissions was relatively high at nominally constant operating conditions. NOx variability was associated with variability in ignition timing. At fixed operating conditions, NOx emissions were found to increase significantly with advancing ignition timing. Over the range of conditions studied, NOx emissions were found to be relatively insensitive to injection pressure ratio and injection duration.
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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 | 0.000 | 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".