A calibration-independent laser-induced incandescence technique for soot measurement by detecting absolute light intensity
Bibliographic record
Abstract
Laser-induced incandescence (LII) has proved to be a useful diagnostic tool for spatially and temporally resolved measurement of particulate (soot) volume fraction and primary particle size in a wide range of applications, such as steady flames, flickering flames, and Diesel engine exhausts. We present a novel LII technique for the determination of soot volume fraction by measuring the absolute incandescence intensity, avoiding the need for ex situ calibration that typically uses a source of particles with known soot volume fraction. The technique developed in this study further extends the capabilities of existing LII for making practical quantitative measurements of soot. The spectral sensitivity of the detection system is determined by calibrating with an extended source of known radiance, and this sensitivity is then used to interpret the measured LII signals. Although it requires knowledge of the soot temperature, either from a numerical model of soot particle heating or experimentally determined by detecting LII signals at two different wavelengths, this technique offers a calibration-independent procedure for measuring soot volume fraction. Application of this technique to soot concentration measurements is demonstrated in a laminar diffusion flame.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".