Detailed Multi-dimensional Study of Pollutant Formation in a Methane Diffusion Flame
Bibliographic record
Abstract
This paper describes a method to produce chemical reactor networks (CRNs) consisting of large numbers of perfectly stirred reactors (PSRs) from computational fluid dynamics (CFD) simulations to predict pollutant emissions from combustion systems accurately, flexibly, and efficiently using detailed kinetic schemes and the kinetic post-processor (KPP) developed at Politecnico di Milano. Benefits of the method described here include its applicability to a wide range of combustion systems, its ability to predict emissions of a variety of pollutant species, and its speed. CFD and CFD–CRN simulation results of the Sandia D piloted methane–air diffusion round-jet flame are successfully validated against experimental data for axial velocity, mixture fraction, temperature, and speciation, including CO and NO mass fractions. A CRN consisting of a large number of PSRs is found to be required to simulate the system accurately, while ensuring independence of the solution from CRN size. The results of CFD–CRN analysis for a 1114 PSR network are used to study the pathways (thermal, prompt, N 2 O, and NO 2 ) by which NO and NO 2, the constituents of NO x, are formed in the flame. Results of CFD–CRN analysis show that NO is produced in the high-temperature ( T > 1850 K) flame brush by a combination of the prompt, N 2 O, and thermal pathways and in the intermediate-temperature (1000 < T < 1600 K) post-flame region by a reversal of the NO 2 pathway. NO is consumed in the fuel-rich (mixture fraction, f > 0.43) region, where a low O atom concentration encourages a reversal of the prompt pathway (i.e., NO reburning), and in low-temperature ( T < 1000 K) regions by the NO 2 pathway, which oxidizes NO to NO 2 . Rate of production analysis, performed using CHEMKIN PRO at specified locations throughout the flame, shows that the trends of NO production and consumption observed in these simulations agree with expected and published results. Finally, the study predicts that, of the total NO x produced by the Sandia D flame, 47% is due to the prompt pathway, 32% is due to the N 2 O pathway, and 21% is due to the thermal pathway. As future steps in this work, the CFD–CRN method will be adapted and used to predict and study emissions from a range of more complex combustion systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".