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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".