Consistent Conditional Moment Closure Modelling of a Lifted Turbulent Jet Flame Using the Presumed<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>β</mml:mi></mml:mrow></mml:math>-PDF Approach
Bibliographic record
Abstract
A lifted H2/N2 turbulent jet flame issuing into a vitiated coflow is investigated using the conditional moment closure. The conditional velocity (CV) and the conditional scalar dissipation rate (CSDR) submodels are chosen such that they are fully consistent with the moments of the presumed β probability density function (PDF). The CV is modelled using the PDF-gradient diffusion model. Two CSDR submodels based on the double integration of the homogeneous and inhomogeneous mixture fraction PDF transport equations are implemented. The effect of CSDR modelling is investigated over a range of coflow temperatures ( Tc ) and the stabilisation mechanism is determined from the analysis of the transport budgets and the history of radical build-up ahead of the stabilisation height. For all Tc , the balance between chemistry, axial convection, and micromixing, and the absence of axial diffusion upstream of the stabilisation height indicate that the flame is stabilized by autoignition. This conclusion is confirmed from the rapid build-up of HO2 ahead of H , O , and OH . The inhomogeneous CSDR modelling yields higher dissipation levels at the most reactive mixture fraction, which results in longer ignition delays and larger liftoff heights. The effect of the spurious sources arising from homogeneous modelling is found to be small but nonnegligible, mostly notably within the flame zone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".