Effects of Detection Wavelengths on Soot Volume Fraction Measurements Using the Auto-Compensating LII Technique
Bibliographic record
Abstract
Soot particles in the detection volume in general have different temperatures due to non-uniform laser fluence and particle size distribution. The thermal radiation intensity displays different temperature dependence at different wavelengths. The effective soot temperature inferred from the ratio of laser-induced incandescence (LII) signals in the auto-compensating LII (AC-LII) technique is dependent on the detection wavelengths and affects the measured soot volume fraction. This paper numerically investigates the effects of detection wavelengths on the inferred soot effective temperature and volume fraction under conditions relevant to laminar diffusion flames at atmospheric pressure and for three representative laser fluence distributions. Numerical calculations were conducted using an LII model for a laser pulse of 5.8 ns FWHM and 1064 nm and assuming a lognormal primary particle size distribution and neglecting the aggregation effect. LII signals were modeled at four wavelength bands centered at 420, 560, 680, and 790 nm and the effective soot temperature was derived over three pairs of LII signals, namely [420, 560 nm], [560, 680 nm], and [680, 790 nm]. The two shortest and two longest detection wavelengths respectively result in the highest and lowest effective soot temperature when the laser fluence is non-uniform. The inferred soot volume fraction displays the opposite trend as the effective soot temperature. The effective soot temperature is biased toward the highest values and AC-LII always underestimates the soot volume fraction. The detection wavelengths should be carefully selected to minimize the impact of non-uniform laser fluence and at the same time to maximize the accuracy of effective soot temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".