CFD simulation of the TR-Francis turbine under no-load andlow-load-operation and comparison with flow measurements
Bibliographic record
Abstract
It is becoming increasingly common for Francis machines to operate at no load and low load conditions to compensate for the variation in the energy production of other renewable sources, such as solar and wind.These conditions are characterized by various flow phenomena such as the presence of large backflow regions, cavitation volumes, interblade vortices, and, in some cases, vaneless space vortices.All these phenomena are responsible for increasing the pressure pulsations and, consequently, the dynamic stresses, which can significantly affect the fatigue lifespan of the runner.Interaction between these various phenomena and other types of hydraulic instabilities can lead to unstable and intermittent cavitation volumes, which will further increase the pressure pulsations and dynamic stresses on the runner.Therefore, accurate prediction of these flow phenomena is of high importance in order to accurately predict the stochastic dynamic stresses under these conditions.In the current work, the TR-Francis speed-no-load (EXP1) and deep-part-load (EXP11 and EXP13) measured operating points will be simulated using Ansys CFX with the SAS-SST turbulence model.The corresponding discharge coefficient normalized by the discharge coefficient at the best efficiency point Qnd/Qnd^ at these operating points are: 0.0125, 0.21, and 0.46, respectively.The CFD results will be analyzed qualitatively in terms of backflow, cavitation volumes, interblade vortices, and vaneless space vortices.The results at the different operating conditions will be compared with each other to better understand the evolution of these flow phenomena as we move from no load to higher load conditions.To verify the validity of the CFD simulations, the cavitation volumes obtained from CFD will be compared to the ones present in the videos from the measurements.Furthermore, the pressure pulsations from the CFD results, at different locations along the hydraulic passages, will be analysed and compared with the measurements.
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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".