Archimedes screw generators for sustainable micro‐hydropower production
Bibliographic record
Abstract
Summary Archimedes screws have been used as pumps since antiquity and have more recently been implemented in micro‐hydropower plants as an ecologically advantageous technology. They are regarded as a hydropower technology with lower environmental impact since they allow safe passage of aquatic flora and fauna through slow turning, widely spaced blades during operation. Archimedes screw generators (ASGs) operate at river‐to‐wire efficiencies at approximately 75% with relatively low installation and maintenance costs when compared to other hydropower technologies of the same scale. ASGs are relatively simple and cost‐efficient to manufacture—simple enough to create in the seventh century BCE. Modern manufacturing techniques for sheet metal and fabrication have refined ASG production. The literature contains various parametric models for predicting screw power output, and more recent numerical simulations have provided insight into the fluid mechanics of screw generators. The knowledge gained from these studies has allowed researchers to suggest more optimal designs for Archimedes screws. However, much can be done to further improve the accuracy of power prediction models. This paper discusses the current state of the literature for ASGs, and highlights areas for future research to improve power prediction and optimization capabilities for researchers and industrial designers. Highlights Archimedes screws are an ancient pumping technology that have more recently found use as a hydropower‐producing technology. Archimedes screw generators (ASGs) are a small‐scale hydropower technology that may be installed as a run‐of‐river installation. ASGs are an eco‐friendly technology that allow for the safe passage of sediments, small debris, fish, and other aquatic wildlife through their flights during operation. There are few experiments and computational fluid dynamic simulations that have sought to extend the literature's data on ASG operation. Current performance models for ASGs lack robust validation to be properly implemented in power plant design.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".