Bibliographic record
Abstract
The Archimedes Screw hydrokinetic turbine (AST) is garnering considerable interest because of its potential applicability in harvesting river and tidal energy. The turbine is well suited to bi-directional flows, low-velocity flows, and shallow watercourses. Because the AST is a reasonably new hydro-technology, very little literature is available on its design and performance optimization. This study experimentally investigates the torque and power generation of the AST. Laboratory scale turbine models with one, two, and three flights (blades) were tested in a water tunnel to measure torque and angular velocity at different flow velocities and varying inclination angles ( β ) of the turbine. A maximum coefficient of performance ( C P ) of 0.41 was obtained at a tip speed ratio ( λ ) of 0.52 at a flow velocity ( U ∞ ) of 0.45 m/s and β = 30 ∘ for a turbine with two flights. In the case of the 3-flight turbine, the highest value of C P was also obtained at 30 ∘ (0.40 at λ = 0.53). For the 1-flight turbine, a maximum C P of 0.23 was obtained at a β of 28 ∘ and λ of 0.30. The results also showed a time-varying fluctuation in the torque, which reduced in magnitude with an increase in number of flights. The ripple was found to occur once-per-revolution and not once-per-flight, irrespective of the number of flights.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".