Evaluating the Ability of SLW Model in Numerical Simulation of Radiative Turbulent Reacting Flow in Industrial Application
Bibliographic record
Abstract
In this paper, the turbulent reacting flow in an industrial furnace is numerically simulated using the RANS equations. The two-equation standard k-ε and the eddy dissipation models are used respectively to close the turbulent closure problem and to consider the turbulence-chemistry interaction. The radiation transfer equation is solved using the discrete ordinates method (DOM). To calculate the radiation absorption coefficient in participating combustion gases, we use the spectral line-based weighted sum of grey gases (SLW) model and compare the achieved results with famous gray-based model, i.e., the weighted-sum-of-gray-gases (WSGG) model. The results of this research show that using the SLW model, the predicted heat transfer from the flame to the furnace walls is reduced due to the thermal radiation. So, the predicted temperature filed increases up to 5% near the outlet of furnace in comparison with the results of WSGG model, which is in more agreement with the experimental data. These results indicate that if one wishes to accurately predict the temperature field and the temperature sensitive quantities such as the NOx emission, one should use the spectral-based models to calculate the radiation absorption coefficient. The details are discussed in the results section.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".