A Computational Model for Hydraulic Labyrinth Seals
Bibliographic record
Abstract
Every Francis turbine has a thin gap between rotating and non-rotating parts, which prevents contact between the two units. Although necessary, hydraulic seals create energetic losses: some fluid does not flow through the runner (leakage loss) and exerts a torque on the rotor (friction loss). Only analytical and empirical prediction methods of a seal efficiency had been developed before 1980. Numerical methods are now used to predict seals performance. However, most of the studies known to the authors deal with gas labyrinth seals and use the k–ε turbulence model. In hydraulic seals, since the viscous losses in the boundary layer influence the leakage loss, low Reynolds turbulence models appear more appropriate. Our study aims to implement an accurate model to predict losses in labyrinth seals using a low Reynolds model, and validate it using experimental results. The issues of the mesh and boundary conditions are addressed. The commercial code ANSYS CFX 12 is used.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".