Development and Validation of a Partially Coupled Soot Model for Turbulent Kerosene Combustion in View of Application to Gas Turbines
Bibliographic record
Abstract
Soot emissions are by-products of combustion that are well documented to have adverse effects on human health and the environment. Consequently, these emissions are becoming a target for stricter regulations. However, obstacles exist in the implementation of soot models in Computational Fluid Dynamics codes with complex geometry, such as ensuring carbon mass conservation as soot forms. This challenge is due to the thermochemistry interactions in turbulent codes being preprocessed (included in look-up tables), and not solved for directly. This study considers the development of a soot model for kerosene combustion. Coupling is introduced between the soot and gas phase by including nucleation rates within the flamelet library, and by adjusting the concentrations of key soot precursors through additional transport equations. Validation has been performed for turbulent coflow kerosene flames at pressures of 1 and 4.8 bar. This simplified model reasonably predicts the soot volume fraction without tuning of the inception rate.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".