Bibliographic record
Abstract
Archimedes screw generators (ASGs) are beginning to be widely adopted at low head hydro sites in Europe, due to high efficiency (greater than 80% in some installations), competitive costs and low environmental impact. Compared to other microhydro generation technologies, ASGs have greatest potential at low head sites (less than about 5 m). The performance of an Archimedes screw used as a generator depends on parameters including screw inner and outer diameter, slope, screw pitch and number of flights, and inlet and outlet conditions, as well as site head and flow. Despite the long history of the Archimedes screw, there is very little on the dynamics of these devices when used for power generation in the English literature. Laboratory tests of small Archimedes screws (approximately 1 W mechanical power) have been conducted to support the design and validation of ASG design tools. This paper reports experimental results examining the relationship between torque, rotation speed and power. The laboratory screw maintained reasonable efficiency over wide ranges of operating conditions, although distinct efficiency peaks were found to occur. The cause of changes in power output caused by varying the water level at the outlet of the screw were attributed primarily to the corresponding variation in head, and dynamic limiting of screw rotation speed causing corresponding limits in volume flow through the screw. Test results were qualitatively consistent with data from a prototype ASG installed by Greenbug Energy in southern Ontario, Canada, and recent data reported from European laboratory tests and commercial installations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".