Acoustic discharge measurements for the performance testing of low-head hydroelectric turbines under disturbed flow conditions
Bibliographic record
Abstract
Field performance testing of a low-head hydroelectric turbine is essential to evaluate the efficiency and economics of an operation. For low-head hydroelectric turbines, it is difficult to accurately measure the discharge through a unit. Transit-time velocity measurement technology has recently been used to develop, in a laboratory setting, a unique traversing acoustic discharge meter for low-head hydroelectric applications. This technology was recently combined with Gauss-Legendre quadrature integration as an alternative method of measuring the flow through a low-head hydroelectric turbine. However, laboratory testing of this technology has only dealt with undisturbed or ideal flow conditions. Additional physical modeling has been performed to compare the relative accuracy of the continuous traversing acoustic discharge meter with that of a multilevel Gauss-Legendre quadrature integration in disturbed or nonideal flow conditions. The data indicate that while Gauss-Legendre quadrature may provide more accurate estimates in ideal flow conditions, the continuous traversing acoustic discharge meter is better suited to disturbed flow condition because it can better resolve an intricate velocity profile. The accuracy of this instrumentation is sensitive to relatively large scale vorticity rotating in the plane of the acoustic transducers, although accuracies within 2% are still attainable, which is better than the conventional velocity-area method. Key words: acoustic discharge measurement, disturbed flow, turbine, performance testing.
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 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 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".