In-Situ Real-Time Characterization of Particulate Emissions from a Diesel Engine Exhaust by Laser-Induced Incandescence
Bibliographic record
Abstract
Diesel engines face tightening particulate matter emissions regulations due to the environmental and health effects attributed to these emissions. There is increasing demand for measuring not only the concentration, but also the size distribution of the particulates. Laser-induced incandescence has emerged as a promising technique for measuring spatially and temporally resolved particulate volume fraction and size. Laser-induced incandescence has orders of magnitude more sensitivity than the gravimetric technique, and thus offers the promise of real-time measurements and adds the increasingly desirable size and morphology information. The usefulness of LII as a diagnostic instrument for the precise measurement of particulate concentration and primary particle size has been demonstrated. Measurements have been performed in the exhaust of a single cylinder DI research diesel engine. Simultaneous gravimetric filter measurements were made for direct comparison with the LII technique. Quantitative LII is shown to provide a sensitive, precise, and repeatable measure of the particulate concentration over a wide dynamic range. LII and gravimetric measurements are shown to correlate well over a wide range of operating conditions. A novel method for determining the primary particle size is shown to be precise enough to distinguish particle sizes for different engine operating conditions, and subsequently the number density of primary particles was determined. LII has also been shown to be sensitive in differentiating the PM performance between four different fuels. The LII technique is capable of real-time particulate matter measurements over any engine transient operation. The wide dynamic range and lower detection limit of LII make it a potentially preferred standard instrument for particulate matter measurements.
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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) | 0.001 | 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".