A layer-peeling method for signal trapping correction in planar LII measurements of statistically steady flames
Bibliographic record
Abstract
Abstract This work presents a layer-peeling (LP) algorithm to correct the signal trapping effect in planar laser-induced incandescence (LII) measurements of soot volume fraction. The method is based on measurements of LII signals captured by an intensified CCD camera at a series of parallel planes across a diffusion flame. A method based on presumed function (PF) of soot volume fraction is also proposed for comparison. The presented methods are numerically tested based on synthetic LII signals emitted from a simulated axisymmetric laminar diffusion flame using the CoFlame code. Numerical results showed that the LP method is able to correct the signal trapping effect, even for fairly large optical thicknesses and in a wide range of detection wavelengths. The correction decreases the relative errors induced by neglecting the trapping effect considerably. The signal trapping effect correction is less important for the determination of integrated soot quantities such as radially integrated soot volume fraction or total soot loading. Planar LII measurements were carried out and calibrated in order to test the method experimentally in a coflow flame. The LP, PF and a simplified analytical (SA) model were compared. The results indicate that the differences in soot volume fraction of 1 ppm or about 15% are obtained in zones of maximum soot loading of 6.5 ppm when the trapping effect is accounted for. Also, the LP and SA methods were found computationally efficient and accurate compared to the PF method. Although the study was performed in a canonical laminar axisymmetric flame, the proposed method can be applied to any statistically steady 3D flame.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".