Unsteady Simulation for Francis Turbine During Load Rejection Events
Bibliographic record
Abstract
This paper presents an automated tool chain for simulating Francis turbine behavior during the transient processes induced by a load rejection event. The proposed methodology combines a commercial CFD solver and a user function and scripts to address the simulation challenges caused by the wicket gate motion and runner speed variation during emergency shutdown. Mesh deformation and re-meshing techniques are used to simulate the large displacement of the wicket gates. The runner speed variation is computed using an angular momentum equation implemented in a user defined function. The proposed methodology was developed and validated by performing 2D unsteady simulations on a high head model Francis turbine used in the Francis-99 workshop, followed by a 3D unsteady simulations on a medium head Francis turbine. These simulations allow computing the evolution of engineering quantities such as turbine angular speed, flow physics and unsteady load on blades during the process. The validation of CFD results with experiments showed 9% discrepancy in the prediction of runaway speed. The investigation of flow physics reveals the presence of complex flow structures such as reversed flow (pumping flow) near the draft tube cone center and a downward tangential flow near the cone wall of the draft tube. Pressure fluctuations are captured when the Francis turbine operating point moves through conditions of zero and negative torque. The proposed methodology is fast and simple to present a qualitative analysis of the flow physics and the turbine behavior during load rejection.
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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".