Flow field study in a bulb turbine runner using LDV and endoscopic S-PIV measurements
Bibliographic record
Abstract
The flow in the inter-blade channels of a bulb turbine was measured using two different techniques. The first involved a classical laser Doppler velocimetry (LDV) setup whereas the second integrated endoscopic cameras to a stereoscopic particle image velocimetry (S-PIV) system. This paper presents results from both measurement campaigns and also provides some key conclusions based on the two datasets. Before getting into the thick of the data though, the technical aspect of both measurement configurations is addressed. A quick overview of the LDV setup is presented, but the main focus is on the novelties and challenges brought by the use of endoscopic cameras to achieve S-PIV measurements between the runner blades. Endoscopic PIV systems have already led to successful measurements of flow fields in a few studies concerning turbomachinery, especially in aerodynamics. However, to the author's knowledge, the realisation of such measurements in a hydraulic turbine is a first. After this outline of the techniques used, the results and conclusions are shown. First, the influence of the guide vanes wakes on the runner flow is described. The size, localisation, strength and dissipation of those structures are inferred from the information coming from both measurement techniques. Then, a flow imbalance is assessed circumferentially. On another subject, the blade tip vortices are identified and characterized using the LDV data. The size, position and direction of rotation of those structures are all extracted from the measured flow field. Finally, the PIV data allows the identification of yet another vortex located near the suction side of the blades and originating from the corner between the leading edge and the hub when operating the bulb turbine at part load.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".