Development and validation of a multi-angle light scattering method for fast engine soot mass and size measurements
Bibliographic record
Abstract
A Fast Exhaust Nephelometer (FEN) is developed for light scattering measurement of particles produced by unsteady combustion processes, such as in diesel engines. The FEN simultaneously measures the light scattering intensity at three angles to infer the mass concentration (Cm), the geometric mass mean mobility diameter (dm,g), and the geometric standard deviation (σm,g) of polydisperse soot. A kernel is used to determine Cm, dm,g, and σm,g based on lookup tables generated with the Rayleigh-Debye-Gans light scattering model for fractal aggregates (RDGFA); the model incorporates the variation of the primary particle size (dp) with aggregate size (da), and nine parameters related to the soot properties, and one to the FEN optics. These parameters are determined a priori from literature and Transmission Electron Microscopy (TEM). The inverted Cm and dm,g are within ±10% of the gravimetric mass concentration and SMPS mobility diameter. This, however, largely depends on the choice of the parameters used to generate the lookup tables. A parametric study shows the inferred mass is most sensitive to uncertainties in the soot refractive index, the primary particle size, and the fractal pre-factor kf. Considering the wide range of soot refractive indices in the literature and the sensitivity of the morphological parameters to the processing of soot images, the uncertainty in mass concentration would be over 40%. Because of this, a novel approach of relating the size of primary particles to the size of aggregates is incorporated for the first time in the light scattering model, and reduces the uncertainty to ±25–30%.Copyright © 2020 American Association for Aerosol Research
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".