Preliminary experimental modal analysis of a model Francis runner inoperation using piezoelectric actuators
Bibliographic record
Abstract
Nowadays, hydraulic turbines are used more and more to regulate the power grid to compensate the production fluctuation of other power sources.Therefore, turbines operate more often outside the operating condition they were designed for, reducing the life span of the runners.The phenomena that lead to the reduction of the life span at those regimes are still not well known.Knowing the exact natural frequency and damping factor of each mode in operation is essential to investigate the dynamic behavior of the runner.However, performing an experimental modal analysis of a model runner in operation is challenging because of the rotation of the runner, the close space, the small clearance, and because the sensors and the actuators need to have a minimal impact on the flow.Also, the natural frequencies of the runner are closely spaced and highly damped, making the identification of the natural frequencies and the damping challenging.This paper presents an experimental modal analysis of a model Francis runner in operation using piezoelectric actuators and semiconductor strain gauges.Because of their small size, strain gages, and piezoelectric actuators are chosen.Also, piezoelectric actuators have been successfully used on other submerged structures in the literature to perform experimental modal analysis.Different excitation signals are used to excite the specific mode of the runner and help identify the natural frequencies and damping at different regimes.The runner is installed in the Hydraulic Machines Laboratory (LAMH) test stand and is excited with eight piezoelectric actuators during the speed-no-load operating condition and at the best efficiency point.It will be shown that it is possible to excite a specific nodal diameter mode by injecting a specific excitation shape to the piezoelectric actuators, using stationary and traveling wave excitation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".