Evolution of nano-particles within a diesel car exhaust plume via TEMOM-LES method
Bibliographic record
Abstract
A study on the evolution of secondary nanoparticles in a diesel engine exhaust is presented, coupling the large eddy simulation(LES) and the Taylor expansion method of moments(TEMOM), with the aim to reveal the differences in total particle number concentration, volume concentration and geometric mean diameter of diesel engines from Canada, Singapore, European, USA,Russia, China and Japan. The investigated aerosol system involved nanoparticle diffusion, nucleation, coagulation and condensation.The nucleation model is the binary homogeneous nucleation of water- acid system, which is valid for the engine exhaust dilution conditions. Considering the particle diameter range in the particle evolution, the free molecule regime coagulation formula is used.The particle condensation growth rate is obtained by calculating the arrival and loss of acid molecules at the entire particle surface.Five different diesel fuel sulfur contents, 10, 15, 30, 50, 350 mg/kg, are chosen as the initial conditions to investigate the particle evolution. It is shown that particles mainly form in the exhaust shear layer while the maximum values of particle numberconcentration and particle diameter appear in the downstream 0.6 m from the tailpipe. It is obvious that the fuel sulfur content has a significant effect on the formation of secondary nanoparticles. The total particle number concentration and total volume concentration are about four and six orders of magnitude smaller in the lowest fuel sulfur standard than those in the highest fuel sulfur standard,respectively.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".