Large eddy simulations of a Francis turbine operating at ultra-low load to overload conditions
Bibliographic record
Abstract
Due to the increasing global power demand, hydropower plants face the challenge of operating at flow rates far from their best efficiency point to comply with the electrical grid. Consequently, turbine units experience unstable high swirling flows under different load conditions, reducing the life of system components. Understanding the mechanisms behind the onset and sustenance of these instabilities in the swirling flow leaving the turbine runner is crucial for improving turbine performance. To achieve this, large eddy simulations were conducted to characterize the unsteady flow inside an industrial-sized Francis turbine. The turbine was studied under a wide range of flow rates, 100%, 80%, 60%, 40%, and 120% of the design flow rate. A high swirl velocity was introduced as the flow rate deviated from 100% load, causing significant changes in the flow structure. An organized structure of vortex filaments in a helical pattern is seen inside the draft tube at 80% and 60% load, and the 40% load case showed an unorganized collection of small-scale vortices. A straight vortex cavity is seen in the draft tube center at 120% excess load. At the best design point, the magnitude of the swirl velocity was 0.1 times the tip speed. However, as the flow rate dropped to 40% of the design flow rate, the magnitude increased to 0.4 times the tip speed near the wall. In addition, the 40% load case had the highest magnitude of pressure fluctuations, over 25 times the peak design case near the runner. This showed that a very unsteady flow field could damage the system or cause unstable operation at ultra-low partial loads. Generated power dropped dramatically as the flow rate decreased, and more noise was present in the power signal.
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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".