Design Optimization of a Vaneless “Fish-Friendly” Swirl Injector for Small Water Turbines
Bibliographic record
Abstract
Large-scale power generation and delivery to remote locales is often prohibitively expensive, due in part to the excessive costs of delivering the materials required to build the necessary infrastructure. In addition, these facilities can have deleterious effects on the local ecosystem. With their reduced physical and environmental footprints, small-scale run-of-river hydroelectric facilities capable of generating power from the modest head provided by streams and rivers are attractive alternatives. Concern remains, however, for the health and safety of the local fish population in these waterways. In order to further reduce the impact of small-scale axial turbine-based hydroelectric facilities on the local fauna, AlfaStar Hydro has proposed a vaneless swirl injector to replace traditional inlet guide vanes (IGVs), as well as a “fish-friendly” rotor designed to rotate relatively slowly and with wide passages between the blades to enable the safe egress of fish drawn into the turbine. Herein, we perform a numerical study of the flow development in the vaneless swirl injector as a function of the number of revolutions and the pitch angle of the rifling in the absence of a rotor toward maximizing turbine efficiency. Swirl intensity, pressure loss in the injector, and axial and circumferential velocity distributions are incorporated as performance metrics into an objective function to optimize the casing design. Results indicate that the number of revolutions of the injector has considerably less influence on overall injector performance than does pitch angle. The casing with the best predicted performance consists of four revolutions at a pitch angle of 25 deg.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".