Experimental and Numerical Investigation of the Novel Low NO<sub>x</sub>CGRI Burner
Bibliographic record
Abstract
This paper reports on an experimental and numerical investigation of the near field and combustion zone of a burner that realizes a FODI (Fuel/Oxidant Direct Injection) strategy for furnace firing. FODI is an non-premixed method of reactants delivery and employs a direct discharge of fuel and oxidant jets into the furnace chamber. The jets entrain significant quantities of furnace gases that have been cooled by furnace heat transfer. The fuel and oxidant streams arrive at the reaction zone diluted by furnace gases, lowering the temperature of the reaction and reducing NOx emissions. FODI involves three-feed mixing processes linked with non-adiabatic reactions at unusually low temperatures and reactant concentrations. The burner investigated here consists of fourteen fuel and air ports arranged in a circle around a central pilot flame. The global performance characteristics of the burner demonstrate the effectiveness of FODI in NOx reduction and its visually flameless oxidation process. The mathematical modelling of the burner using a commercial CFD package was capable of adequately predicting jet trajectories and primary flow structures in the furnace. The predicted temperature and species concentrations depart from measured values thus raising a question of how to effectively model three-feed processes under severe non-adiabatic 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".