Influence of Jet Inlet Conditions on Time-Average Behavior of Transverse Jets
Bibliographic record
Abstract
Dynamics and dispersion mechanisms in transverse jets are partially controlled by jet-exit conditions that are intimately linked to the coupling between issuing jet and crossflow. Accurate knowledge of this coupling is crucial to plan repeatable and effective experiments, perform accurate computations, and design dispersion control devices. A simplified geometry is focused on, representing a plenum/nozzle and a wind/water tunnel, to characterize the time-averaged extent of the coupling between the jet and crossflow and its effect on jet penetration, with specific emphasis on the following: 1) The relative importance of simulating/neglecting the coupling between jet and crossflow within the nozzle/plenum (pipe) is established to reproduce the jet penetration observed experimentally. 2) The distance down the pipe is characterized to determine how far down the presence of the crossflow modifies the flow with respect to the case of a jet issuing in a quiescent fluid. 3) Variations calculated in jet penetration are quantified when different boundary conditions are used to simulate the jet. 4) The effect of different crossflow velocities at jet exit on simulated jet penetration is evaluated. Results discussed may provide a guideline for future computational investigations on transverse jets and a useful reference to understand the discrepancies observed between experimental and numerical results.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".