Numerical and experimental comparisons of pressed blades for large Francis turbine runners manufactured with a reconfigurable pressing setup and a conventional setup
Bibliographic record
Abstract
Quebec hydraulic power plants are mainly operated with large Francis turbines due to their ability to comply with a wide range of rates of flow and falls' heights. The variation of hydraulic conditions from one site to another forced engineers to create each time a new design for the runners and consequently lead to different thicknesses, sizes and shapes for the blades. The unit production costs are inevitably high since the punch and die matrices are completely redesigned for the production of a specific batch of blades. In this paper, the authors focus on a reconfigurable setup of punch and die matrices for forming blades from very thick plates offering a flexible alternative to the conventional setup. First, in order to validate the numerical model, a finite element simulation of the pressing process with a continuous conventional punch and die pair is performed. Second, the methodology for setting up a reconfigurable punch and die pair, based on a dense distribution of spherical headed poles, is presented. The model is then inserted in the previous finite element procedure. Numerical results agree well with data collected on blades pressed for the rehabilitation of a hydropower plant in Quebec.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".