Experimental and Numerical Analysis of the Cavitating Part Load Vortex Dynamics of Low-Head Hydraulic Turbines
Bibliographic record
Abstract
The draft tube flow is a two-sided challenge for the operation of a hydraulic turbine. On one side, it is an important component for the performance of low to medium head turbines, where it can provide up to 40% of the extracted energy from the flow. On the other side, being a diffuser with a complex vorticity distribution at the inlet, vortex breakdown instability can occur at part load and generate a corkscrewed precessing vortex that can be associated with cavitation. The cavitating vortex rope, may generate undesired power output fluctuation and/or structural vibration. Therefore, draft tubes are much studied components but hard to tackle both numerically and experimentally. Within the framework of the AxialT project, the flow in the draft tube of a propeller turbine model operating at part load was studied using a combination of two-phase Particle Image Velocimetry (PIV) measurements and Unsteady Reynolds Averaged Navier-Stokes (URANS) simulations. The paper main focus is on the experimental methodology and results. It explains how Particle Image Velocimetry measurements were implemented, validated and post-treated to provide flow measurements in the draft tube cone at part load in the cavitating and non-cavitating regimes. It also describes various image processing techniques used to extract the velocity field around the cavitating vortex rope and to estimate the location of the water-vapour interface of the cavitating region. In the spirit of feeding experimental data to numerical simulations, an analysis of measured velocity profiles just under the runner is presented. Comparison between PIV measurements and preliminary URANS simulations is also illustrated.
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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) | 0.002 | 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".