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Experimental Investigation of Archimedes Screw Pump

2020· article· en· W3035192072 on OpenAlexaff
Murray Lyons, Scott Simmons, Maxwell Fisher, James Sebastien Williams, William David Lubitz

Bibliographic record

VenueJournal of Hydraulic Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsInletRotational speedRotation (mathematics)Screw pumpVolumetric flow rateScalingMechanicsCentrifugal pumpStructural basinWater flowFlow (mathematics)Environmental scienceGeotechnical engineeringGeologyMechanical engineeringImpellerEngineeringMathematicsPhysicsGeometry

Abstract

fetched live from OpenAlex

There is essentially no experimental data on Archimedes screw pump performance available in the literature that is sufficiently detailed for model validation. Experiments were conducted on a laboratory scale (0.3-m diameter) Archimedes screw pump to characterize the pumping efficiency of the screw pump at various inlet basin water levels and screw rotation speeds. The results provide new insights into the effect of inlet and outlet basin level on screw pump efficiency. The flow rate of water pumped is proportional to the rotation speed of the screw, and increases with increasing inlet basin depth until the basin level exceeds that needed to fully fill the screw without overflowing. Comparisons are made to available empirical and analytically derived guidelines regarding optimal lower basin water levels, upper basin water levels and rotation rates for an Archimedes screw pump. Some differences are noted between recommended optimal conditions for full-size screws from the literature, and the optimum conditions found for the tested laboratory-size screw. These differences are consistent with expected effects of scaling between different size screws.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.018
GPT teacher head0.206
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2020
Admission routes1
Has abstractyes

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