Simulation of stochastic pressure loads on a medium head Francis runner
Bibliographic record
Abstract
Abstract In recent years, major advances have been made in simulating dynamic and transient flows in hydraulic turbines. Overall, computational fluid dynamics (CFD) and finite element analysis (FEA) simulations have been quite successful and have helped to advance knowledge beyond the best efficiency point. The simulation of transient operations like startup and runaway has nearly become part of the standard toolbox. However, numerically predicting stochastic flows, such as those occurring during speed-no-load (SNL) regime, remains a challenge. Since these flows can have a significant impact on turbines’ life expectancy, CFD methods need to be improved. In this paper, we examine the SNL regime in a Francis turbine in which extensive dynamic fluctuations were measured. The approach used was to first focus on the CFD methodology required to better predict stochastic pressure loads with hybrid turbulence models such as the Scale-Adaptive Simulation–Shear-Stress Transport (SAS-SST) model and the relatively new Stress-Blended Eddy Simulation (SBES) model. This will then allow us to determine the amount of LES content required to accurately predict large-scale turbulent structures and the corresponding pressure fluctuations that they generate on blades.
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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".