Effects of Parameter Scaling on Archimedes Screw Generator Performance
Bibliographic record
Abstract
Archimedes screws are an ancient pumping technology that has more recently found use as a technology for hydropower generation. Currently, the literature is lacking reliable data, performance predicting models, and design guidelines. Most performance models presented in the literature are theoretical or were developed and evaluated using laboratory-scale data. This paper presents novel experimental and numerical simulation data to the literature from screw generators with a wide range of sizes (laboratory to full-scale powerplant scale) and orientations. The data suggest that the components of power production (pressure-driven and viscous/friction) scale differently depending on system size, configuration, and operating conditions. So, for the robust validation of models and the development of reliable design guidance, data from a wide range of sizes and configurations are crucial. The paper presents data collected from laboratory experiments, field measurements from operating powerplants, and data from numerical simulations. The numerical simulations were evaluated for accuracy with experimental data, then used to collect performance data from a wide range of screw geometries and scales. The length-scale (diameter), number of blades, fill height of water, inclination angle, and surface roughness were all varied. The data gathered in these experiments were analyzed and used to develop back-of-the-envelope estimations for the effect of each parameter on overall system performance; the relationships are intended to serve as a useful reference for designers, though they should not be used in lieu of a design model. The length-scale and number of blades were related to power in a way that could be reasonably approximated with a constant value. The fill height, inclination angle, and surface roughness were related to power in a way that could be approximated reasonably with first-order polynomial fits. Altogether, this paper presents much-needed, novel data to the literature; the data are integral for future model development and evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".