Fluid-structure simulations of the stochastic behaviour of a medium head Francis turbine during startup
Bibliographic record
Abstract
The use of dynamic CFD and FEA simulations of hydraulic turbines for steady operation is now widespread for fatigue analysis. However, machines will undergo a major role change from the traditional base load operation in the coming years. This can have a significant impact on life expectancy. In this regard, a lot of research efforts have been recently devoted to improve understanding of hydraulic turbines transient operations such as startup and runaway. Our recent experiences have shown that almost all turbines present unique behaviour from a stochastic point of view during transient operation, and we consider there is still a lot to learn about how to correlate load patterns to life expectancy. In past work, startup simulations and no-load stochastic predictions were presented separately. To our knowledge, no one has attempted to combine them to predict stochastic stresses by simulations in the case of turbine startup. The challenge is thus to capture much more physics while respecting fluid-structure interaction modelling requirements. This paper presents specifically transient CFD and FEA simulations of a medium head Francis turbine startup aimed at getting stochastic dynamic loads on the runner. The CFD simulations involve, among other things, variable rotating speed, mesh deformation, labyrinth seals, resistive torque, roughness, SAS turbulence modelling. 1-way fluid-structure simulations with time-dependent pressure loads are used to determine the stochastic stresses. The stochastic loads are challenging, and only a part of them were captured. Encouraging results are obtained at the leading edge, but the trailing edge deformations lack most of the content. Simulations and experiments might indicate that a stronger coupling is required to get both the fluid load and the mechanical answer right.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".