PIV Measurements Applied to Hydraulic Machinery: Cavitating and Cavitation-Free Flows
Bibliographic record
Abstract
Hydraulic machinery is an ideal field of application for Particle Image Velocimetry in terms of scientific interest, due to the complexity of the flow behaviour and to the need of detailed unsteady experimental data simultaneously recorded over large sections of the flow field. Within the same machine, a whole range of phenomena are encountered in the different components: wake patterns, separation, rotating vortex structures, vortex breakdown, etc. The unsteady interactions between the stationary and rotating frames, both upstream and downstream the runner, contribute to the efficiency loss. The generated quasi-periodic fluctuations overlay onto the average flow field, which may be symmetrical or not, issuing an unpredictable dynamic behaviour with respect to the operating regime. Whilst the intrinsic parameters of the local phenomena (e.g. sheared flow mixing length) vary, the flow topology may be modified drastically for conditions situated relatively close to one another in terms of head, flow rate and efficiency. The mapping of the unsteady velocity fields and corresponding turbulence levels is thus an essential tool in the analysis of these complex phenomena. The PIV technique opens large perspectives in the analysis of internal flows in hydraulic machinery, providing valuable insight towards an extensive understanding of the underlying physical mechanisms. Nevertheless, the use of PIV systems in this context is one of the most challenging applications, due to the structural constrains related to the optical access to the measurement areas, to the spatial and temporal scales of the phenomena that are to be investigated, two phase flow structure in cavitating regime and also due to the industrial aspects of the application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".