Thermochemical Mechanism Optimization for Accurate Predictions of CH Concentrations in Premixed Flames of C1-C3 Alkane Fuels
Bibliographic record
Abstract
Increasingly stringent regulations on NOx emissions are enforced by governments owing to their contribution in the formation of ozone, smog, fine aerosols, acid rains and nutrient pollution of surface water, which affect human health and environment. The design of high-efficiency, low-emission combustors achieving these ever-decreasing emission standards requires thermochemical mechanisms of sufficiently high accuracy. Recently, a comprehensive set of experimental data, collected through laser-based diagnostics in atmospheric, jet-wall, stagnation, premixed flames, was published for all isomers of C1-C4 alkane and alcohol fuels [1–3]. The rapid formation of NO through the flame front via the prompt (Fenimore) route was shown to be strongly coupled to the maximum concentration of the methylidyne radical, [CH]peak, and the flow residence time within the CH layer. A proper description of CH formation is then a prerequisite for accurate predictions of NO concentrations in hydrocarbon-air flames. However, a comparison against the Laser-Induced Fluorescence (LIF) experimental data of [3] revealed that 1) modern thermochemical mechanisms are unable to accurately capture the stoichiometric dependence of [CH]peak, and 2) for a given equivalence ratio, the predictions of different mechanisms span over more than an order of magnitude. This paper presents an optimization of the specific rate of a selection of nine elementary reactions included in the San Diego combustion mechanism [4]. A quasi-Newton algorithm is used to minimize an objective function defined as the sum of squares of the relative difference between the numerical and experimental CH-LIF data of [3], while constraining the specific rates to physically reasonable values. A mechanism properly describing CH formation for lean to rich, C1-C3 alkane-air flames is obtained. This optimized mechanism will enable accurate predictions of prompt-NO formation over a wide range of equivalence ratios and alkane fuels. Suggestions regarding which reactions require further investigations, either through experimental or theoretical assessments of the individual specific rates, are 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".