Refinement of the two-color pyrometry method for application in a direct injection diesel and natural gas compression-ignition engine
Bibliographic record
Abstract
The soot emissions from internal combustion engines have significant health and environmental impacts and, as such, are subject to increasingly stringent regulations. Two-color pyrometry provides the in-cylinder soot cloud temperature and soot volume fraction and can provide insight to the in-cylinder soot formation and oxidation processes to guide research for reducing engine-out soot emissions. This work demonstrates improvements to the two-color pyrometry methodology, with a focus on low-temperature, low-soot regimes such as low-temperature combustion or combustion of direct injected natural gas. Through selection of a fast and robust numerical algorithm, characterizing and increasing the detection envelope, performing static and dynamic perspective adjustments, accounting for non-uniform and non-linear system response, as well as localized signal-to-noise ratio enhancement through image filtering, the performance of the pyrometric method was improved by a 40% increase in the resolved signal fraction. The refined two-color method was evaluated for both direct injected diesel and natural gas fueling strategies using a pilot-ignited direct injected natural gas fuel system and facilitated evaluation of local temperatures and soot concentrations in pilot-ignited direct injected natural gas combustion, despite the generally low soot levels in this combustion strategy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".