A Critical Evaluation of NOx Modeling in a Model Combustor
Bibliographic record
Abstract
Reliable NOx modeling depends on the accurate prediction of both velocity and temperature fields. The velocity and temperature fields of a propane diffusion flame combustor, with interior and exterior conjugate heat transfers, were first numerically studied. The results from three combustion models, together with the renormalization group (RNG) k-ε turbulence model and the discrete ordinates radiation model are discussed, and compared with comprehensive experimental measurements. The flow patterns and the recirculation zone length in the combustion chamber are excellently predicted, and the mean axial velocities are in fairly good agreement with the experimental data for all three combustion models. The mean temperature profiles are fairly well captured by the probability density function (PDF) and eddy dissipation (EDS) combustion models. However, the EDS-finite-rate combustion model fails to provide an acceptable temperature field. Based on the acceptable velocity and temperature fields, a number of NO modeling approaches were evaluated in a postprocessing mode. The partial-equilibrium approach of O and OH radical concentrations shows a significant effect on the thermal NO formation rate. In contrast, the prompt NO, the NO reburn mechanism and the third reaction of the extended Zeldovich mechanism have negligible effects on the overall NO formation in the present study. This study indicates that the semiempirical, postprocessing NO model can provide valuable NO simulations as long as the velocity and temperature fields are adequately predicted.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".