Stochastic modelling of cavitation erosion in Francis runner
Bibliographic record
Abstract
Reaction turbines, of which Francis turbines, constitute a large proportion of low and medium head turbines installed in hydropower plants. Managing these machines represents a real challenge in terms of efficiency, competitiveness and demands on the energy market. Turbines runner blades exhibit loss of performance from damage due to several reasons. One common source of damage is erosion due to the cavitation phenomenon. Indeed, at a given operating region, rapid changes of velocity can create bubbles in the water flow due to local low pressures. When cavitation bubbles reach pressure recovery, they collapse and may induce wear or erosion in these regions. Even if this phenomenon has been intensively studied in the past decades, cavitation erosion is not fully understood as it is driven by several parameters such as flow dynamic, turbine design, environment, or material properties. Some of these parameters can be studied in laboratory to compare materials resistance between each other. This article aims to model the cavitation by a stochastic model using erosion experimental data observed in the laboratory. The benefit of such models is to consider both the uncertainties and natural fluctuations of the phenomenon. With the proposed framework, the study will highlight the differences observed in cavitation erosion experiments of two common materials used to manufacture Francis's runners. This study is the first step in a project aiming at the prediction of turbines mass loss due to cavitation erosion on actual operating Francis turbines.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".