PREDICTING THE LENGTH OF LOW-MOMENTUM JET DIFFUSION FLAMES IN CROSSFLOW
Bibliographic record
Abstract
A phenomenological model is presented for predicting the size of low-momentum jet diffusion flame in crossflow. This model relates the length of the flame (L f) to the diameter of the issuing jet (d s ), the exit velocity (V j ) and density (ρ j ) of the jet gases, the crossflow velocity (U ∞), and dilution of the fuel by an inert gas. Flame shape and size are modeled with two basic principles: flame shape is set by the fuel jet and crossflow properties and flame size is set by the timescale for the stoichiometric amount of oxygen to mix with the fuel jet. Experimental data are presented in which d s , V j , U ∞, and fuel jet dilution were all varied. The data show two regimes in which flame length either increases or decreases with increasing crossflow velocity. The predicted flame lengths agree with the measurements, within a root-mean-square error of 15%.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".