Coupling Micro-Mixing Combustion With a Pre-Reactor Operating Under Ultra-Rich Conditions for Low-NOx Combustion of Hydrocarbons
Bibliographic record
Abstract
Abstract Micro-mixing combustion is promising because of its non-premix like combustion stability while having the potential to achieve NOx emissions near premix levels. However, based on our previous work, the heavier the hydrocarbons, the higher the thermal NOx because of the increase of the mixing timescale. Consequently, there is a need to achieve faster mixing with heavy hydrocarbons to allow their pairing with micro-mixing combustion. This paper presents an experimental proof-of-concept of a combustor architecture composed of a micro-mixing injector coupled with a pre-reactor operating under ultra-rich conditions. Air is introduced in the fuel at high equivalence ratio, between 15 and 25, and the reaction is activated with the heat of the main combustion. The thermally coupled reactor forms lighter species from heavy hydrocarbons through partial oxidation of the fuel, balancing the effects of heavier fuels and decreasing mixing timescale. Experimental tests with propane at atmospheric pressure, and at adiabatic flame temperatures between ∼1500 K and ∼2000 K, were performed to demonstrate the concept. NOx emissions were reduced by a factor up to 3 with this combustion architecture compared to baseline tests, achieving near premix NOx emissions. Increasing the air injected in the reactor decreased further the NOx emissions. Additionally, tuning of the combustion stability was possible by adjusting the reactor parameters.
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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 | 0.000 |
| 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 | 0.001 |
| 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".