Detection of Hydraulic Phenomena in Francis Turbines with Different Sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nowadays, hydropower is demanded to provide flexibility and fast response into the electrical grid in order to compensate the non-constant electricity generation of other renewable sources. Hydraulic turbines are therefore demanded to work under off-design conditions more frequently, where different complex hydraulic phenomena appear, affecting the machine stability as well as reducing the useful life of its components. Hence, it is desirable to detect in real-time these hydraulic phenomena to assess the operation of the machine. In this paper, a large medium-head Francis turbine was selected for this purpose. This prototype is instrumented with several sensors such as accelerometers, proximity probes, strain gauges, pressure sensors and a microphone. Results presented in this paper permit knowing which hydraulic phenomenon is detected with every sensor and which signal analysis technique is necessary to use. With this information, monitoring systems can be optimized with the most convenient sensors, locations and signal analysis techniques.
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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) | 0.000 | 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) | 0.000 | 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 it