Diagnostics and Modeling of Stagnation Flames for the Validation of Thermochemical Combustion Models for NO<sub><i>x</i></sub> Predictions
Bibliographic record
Abstract
This article presents a combined experimental and modeling approach for the study of NO formation in premixed stagnation flames. In the experiments, one-dimensional (1-D) Particle Image Velocimetry (PIV), 1-D spectrally resolved Planar Laser-Induced Fluorescence (PLIF), and 1-D NO-LIF thermometry are used to measure high resolution profiles, which are directly compared to numerical simulations. The simulations are performed with Cantera using 1-D hydrodynamics, transport, and detailed thermochemical models. Accurate measurements of premixed gas composition, gas velocity, temperature, and spread rate yield all necessary inlet boundary conditions. Use of a temperature-controlled stagnation plate allows for first-order heat loss effects to be imposed on the numerical simulation, rather than relying on external temperature corrections. The experiments provide a sensitive test of NO formation, result in multiple validation targets which do not rely on extrapolations, and allow for accurate specification of measurement uncertainties when comparing experiments to simulations. This article provides a discussion of the diagnostic techniques and compares experimental results for methane–air flames to numerical predictions using a number of natural gas thermochemical models (GRI-Mech 3.0, GDF-Kin 3.0, CRECK 1212, and Konnov 0.6) and their associated submodels for NO x formation. The reported results indicate that further adjustments to thermochemical combustion models are needed to accurately predict NO x pollutant formation in small hydrocarbons under atmospheric-pressure conditions.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".