A parametric study of turbulence modulation in high-Reynolds-number, particle-laden flow in a vertical pipe
Bibliographic record
Abstract
Turbulence modulation is the increase or decrease in velocity fluctuation intensity in a turbulent flow due to the presence of an additional phase. Prediction of turbulence modulation is critically important in the design of many multiphase flow processes. Several criteria exist to predict whether an increase or a decrease of turbulence will occur for a given set of flow conditions. However, most industrial flows have high Reynolds number (Re), and the existing criteria have been developed from primarily low-Re flow data. Additionally, the criteria do not predict the magnitude of turbulence modulation, but only the type. This paper presents experimental data for high-Re ( 52000 ≤ Re ≤ 320000 ) liquid pipe flow laden with large solid particles ( 0 . 5 mm ≤ d p ≤ 2 mm ) at various solids loadings ( ≤ 1.6%vol.). Using Particle Image/Tracking Velocimetry measurements, we examine the velocity statistics of each phase along the radius of the pipe. We show that the behaviour of the statistics depends significantly on radial position as well as the velocity component considered (streamwise or radial). We also evaluate the applicability of three common turbulence modulation prediction criteria and show that all three correctly predict the modulation at Re = 52000 . They do not however predict the lack of modulation at high Re or the differences between centreline and near-wall or streamwise and radial modulation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".