Experimental and Numerical Studies on the Soot Formation of Liquid Fuel Relevant Compounds
Bibliographic record
Abstract
In the present thesis, fundamental experimental and numerical studies are performed for the soot formation of liquid fuel relevant compounds. The thesis is composed of four research studies. The first develops an improved data analysis approach for the combined laser extinction and two-angle elastic light scattering diagnostics to relate the various measured optical cross sections to soot aggregate properties. Compared to previously reported studies, the proposed approach can be applied to a wider range of soot sources by removing the assumption made to scattering regime or moment ratio of aggregate size distribution. The second study investigates the effects of n-propylbenzene addition to n-dodecane on soot formation and aggregate structure in a laminar coflow diffusion flame using the combined laser extinction and two-angle elastic light scattering method. It is shown that the relative importance of soot inception and surface growth affected by n-propylbenzene addition is different along the flame wing and centerline, with the aromatic fuel chemistry effect being stronger along the centerline. The third study extends the investigation on the same issue using a numerical model. The simulation results show that mixing n-propylbenzene into the liquid fuel mixture accelerates soot inception, and increases soot surface growth per unit surface area by PAH addition, while soot surface growth per unit surface area by HACA is shown to decrease modestly with n-propylbenzene addition. The fourth and final study investigates the soot formation from jet fuel in a laminar coflow diffusion flame using both numerical and experimental methods. The results demonstrate the robustness of the soot model to changes of fuel and also show that the HyChem model (i.e., lumped fuel breakdown approach, Xu et al., 2017) can be used to predict soot formation from real jet fuel combustion in laminar coflow diffusion flames by adding a PAH growth scheme to the model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".