Measurements of passive scalar diffusion downstream of regular and fractal grids
Bibliographic record
Abstract
The diffusion of heat injected from a line source into turbulence generated by regular and fractal grids with the same solidity and inlet velocity was investigated experimentally with particular interest in the effects of grid geometry and relative location of the source on the width of the thermal plume and the mixing efficiency. These grids included one fractal square grid (FSG) and three regular square grids with mesh sizes that were comparable to the first (RG160), second (RG80) and fourth (RG18) iterations of the fractal grid. The heated line source was inserted on the centre plane of the grids, spanning the entire width of the wind tunnel at either of two downstream locations, an upstream location or a location nearly coincident with a grid. It was found that, in all cases examined, RG160 produced the strongest diffusion of the thermal plume and the highest level of scalar mixing. These observations were consistent with the evolution of the corresponding turbulent diffusivities, which, according to Taylor’s theory of diffusion, are the product of the transverse turbulence intensity and the integral length scale. We argue that to maximise scalar diffusion and mixing of a scalar released from a concentrated source inside a duct, one should prefer a regular grid over a fractal square grid; we also recommend the use of a grid with a mesh size roughly equal to half the height of the duct and placed at approximately one duct height upstream of the source.
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 | 0.001 |
| 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".