Phenomenological Simulation of Non-Premixed Gas Jet Flames Including Soot Formation and Oxidation
Bibliographic record
Abstract
A phenomenological model (called here “Slice Model”) has been developed to simulate non-premixed gas jet flames including soot formation in the domain. The Slice Model is based on the self-similarity solution of gas jets and forced to satisfy momentum, mass and energy balances in every cross section. The Slice Model can predict the velocity, mass fraction and temperature field of non-reacting and reacting jets over a wide range of changes in the jet parameters. A sub-model for soot formation based on Hiroyasu’ model is applied to predict soot formation in non-premixed flames. Cantera, an open-source chemical kinetics software, is integrated with the Slice Model to predict the temperature distribution (based on equilibrium composition) of reacting jets. The soot formation prediction of the Slice Model is compared with experimental data in the literature. For velocity, soot mass fraction and temperature, agreement with experiment is about as good as it is for the much more computationally intensive RANS CFD simulations. On this basis, the Slice Model is promising as the core of a non-premixed natural gas engine simulation package under development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".