Dynamic Stress Prediction during Load Rejections in Hydraulic Turbines
Bibliographic record
Abstract
Abstract Load rejections occur when a hydraulic turbine, producing power, is disconnected from the electric grid. The sudden loss of load will trigger the emergency guide vane closing sequence, and the turbine will accelerate to a maximum speed before decelerating. During this event, the runner can experience large dynamic stresses, which can significantly decrease its fatigue life if load rejections occur frequently. So far in the reported literature the approach of simulating a load rejection is to perform a transient analysis, which includes the guide vane closing sequence. This approach is difficult to setup, due to the moving mesh required for closing the guide vanes and demands large computational effort. In the current work, an alternative quasi-steady approach of predicting the maximum dynamic stresses during load rejection is presented and validated against prototype measurements. The method involves a one-way fluid-structure interaction simulation with pressure loads obtained from an unsteady CFD simulation performed at the guide vane opening corresponding to the maximum speed during the load rejection. At this speed, the runner is momentarily in a no-load condition, and measurements show that the dynamic stresses are at a maximum. With this approach, it is shown that the maximum dynamic stresses are well predicted during a load rejection. Given the high level of uncertainty in the measurements and the stochastic nature of load rejections, it can be concluded that the approach gives conservative and satisfactory results, thus validating the quasi-steady assumption.
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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 | 0.000 |
| 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".