Orifice versus Converging-Nozzle Grid Turbulence: A Wavelet Perspective
Bibliographic record
Abstract
Grids such as perforated plates are of fundamental importance in flow turbulence study and are commonly utilised to promote mixing. An orificed perforated plate (OPP) and its reversed counterpart, the converging-nozzle perforated plate (CNPP), were applied to produce quasi-isotropic turbulence inside a wind tunnel. The three orthogonal velocity components were measured using a triple hotwire at 10D downstream of the perforated plate for Reynolds numbers, ReD, 18,700 and 28,400, where D is the diameter of the perforated holes. The statistics of the grid-generated turbulence was analysed using the time-averaged local velocity profile and turbulence intensity, which revealed a more homogeneous distribution of the flow field with a higher level of turbulence for the OPP. Fourier and wavelet analyses were employed to investigate the energy of the eddies as a function of frequency and multiscale characteristics of the fluctuating velocity, respectively. At ReD = 18,700, the turbulent energy remains prominently with large-scale vortical structures which are non-intermittently present in the flow for both perforated plates. The thickness of the converging channels of the CNPP appears to provide the venue for spawning intermittent fluctuations. At higher ReD 28,400, the effect of this intermittent behaviour becomes evident for the CNPP, leading to a multiscale distribution of turbulent energy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".