Evaluation of a RANS-based Computational Framework for Predicting Sooting Turbulent Jet Flames
Bibliographic record
Abstract
A Reynolds-averaged Navier-Stokes (RANS)-based computational framework for predicting soot formation in turbulent flames using the commercial computational fluid dynamics (CFD) software, Ansys Fluent, is evaluated for two experimental turbulent, non-premixed, sooting jet flames. The computational framework includes modelling for turbulent combustion through the Steady Laminar Flamelet Model (SLFM) and Flame-Prolongation of Intrinsic Low-Dimensional Manifold (FPI) tabulated chemistry approaches coupled to a presumed conditional moment (PCM) method. Modelling of soot is treated via a semi-empirical two-equation model for the transport of soot mass fraction and number density with reaction mechanisms governing soot inception, surface growth, coagulation, and oxidation. Radiative heat transfer is modelled via three strategies: (i) the optically thin approximation (OTA), the P1 spherical harmonics closure, and (iii) the discrete ordinates method (DOM). Non-gray radiation is treated via the Statistical Narrow Band Correlated-κ (SNBCK) method for evaluation of the gas phase absorption coefficients and soot absorption is estimated using Rayleigh scattering theory. Turbulence-radiation interaction (TRI) is evaluated from tabulated thermochemical quantities for the emission TRI, whereas absorption TRI is treated via the optically thin fluctuation approximation (OTFA). Results for the proposed framework were evaluated for three commonly-used two-equation turbulence models: the 1998 and Shear Stress Transport (SST) k-ω models, and the standard k- model with some tuning of model constants applied. The capabilities of the proposed framework to predict temperature, soot volume fraction, axial velocity, fuel mass fraction, and normalized OH concentration is assessed via comparisons to experimental data. A discussion of the relative capabilities to other modelling options is also provided.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".