Experimental Investigation of a Cavitating Water Flow With the Addition of Drag-Reducing Agents
Bibliographic record
Abstract
Local reduction of the pressure in a flow field to levels lower than the saturation pressure triggers the production of tiny vaporous bubbles and causes the cavitation phenomenon. Higher pressure reductions significantly increase the cavitation bubbles’ population, which can coalesce and generate large-scale cavitation structures such as cloud cavitation that shed downstream of the flow channel. The chaotic collapse of cavities produces strong shockwaves in regions with a recovered pressure which causes serious erosion on solid surfaces and high levels of noise. Hence, there is a growing interest in cavitation control methods. In this study, drag-reducing polymer additives are utilized as cavitation reducing (CR) agents in a converging-diverging mesoscale nozzle to verify the applicability of these agents in the control of the cavitation process. Analysis of high-speed images of the cavitating flow fields reveals that the viscoelastic flow of a 400 ppm polymer solution reduced the cavitation intensity by nearly 60 % relative to the pure water flow at a similar Reynolds number. Ultra-high-speed imaging of single cavitating bubbles at the inception showed that in a viscoelastic flow, the collapse period of cavities is longer, and their sizes are shrunk at a lower rate relative to their counterparts in the puer water. Particle image velocimetry (PIV) was used to study the near-wall turbulent flow fields at the flow conditions close to the cavitation inception, at different flow locations with non-zero pressure gradients present on the curved surfaces. Preliminary analysis of the results reveals that viscoelasticity alters the near-wall turbulence and distribution of the pressure gradient fluctuations, which might link to the significant reduction of cavitation intensity in polymeric flows.
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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 | 0.000 | 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".