1 VLH: Development of a new turbine for Very Low Head sites
Bibliographic record
Abstract
In Canada as well as worldwide, much of the small hydro potential has already been developed and the remaining valuable potential is very low head hydro having heads typically less than 2 meters. The deployment of this very low head hydro potential is in general technically feasible but economically unviable largely because the associated insurmountable civil works costs. Therefore new approaches and technologies to significantly lower the civil costs and balance the overall project costs are essential to pave the way to harness the very low head hydro potential. In this context, a Very Low Head turbine (VLH) project is under development through an internationally cooperated project which combines the joint effort of French and Canadian partners. The VLH turbine was designed in particular to equip very low head sites for head range between 1.4 and 3.2 meters. Extensive Computational Fluid Dynamics (CFD) modeling and finite element calculations were used to design a reliable, efficient and fish friendly turbine by the French inventor MJ2 and Institut National Polytechnique de Grenoble, France. A model turbine was manufactured by Ateliers Onmec Inc. through the support from Natural Resources Canada. A special test rig has been set up at the Hydraulic Machinery Laboratory (LAMH) at Laval University, Canada, to test and to validate the turbine design and CFD prediction, and various turbine configurations are being measured to optimize the turbine layouts and to minimize civil works. A first prototype turbine will be installed in Millau, France as a pilot demonstration project to show the effectiveness of civil works and costs reductions, and to evaluate the on-site turbine performance and the fish turbine passage capability. This paper will focus on the technical aspects of the turbine hydraulic design and the analysis based on both CFD prediction and model tests results.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".