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Record W4389540931 · doi:10.17118/11143/20964

An experimental study of archimedes screw pump efficiency

2023· article· en· W4389540931 on OpenAlexafffund
Scott Simmons, Lian Miller, Mohammed Faraz Saudagar, Catarina Mendes, Arash YoosefDoost, William David Lubitz

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic and Pneumatic Systems
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract: Archimedes screw pumps (ASPs) are an established low head pump technology that has been used for millennia to lift water. However, there is little literature available on the design of ASPs, and ASP theory is not well enough developed to enable full simulation of an ASP in efficient parametric models that would allow optimization studies. Complete datasets in which all important operating parameters are reported are also extremely rare in the literature. The experimental study reported here is an initial effort to provide additional useful ASP experimental data for research use. Experimental measurements are reported for a laboratory-scale ASP to characterize pumping efficiency as a function of lower and inlet basin water levels, as well as screw rotation speed. Comparisons are made to existing literature to assess the findings through the experiment. Pumping efficiency was sensitive to lower basin water levels, with efficiency declining once the inlet water level exceeded 80% of the screw inlet height. It was confirmed that ASPs should be operated with a lower basin level of 70 % to 80 % of screw inlet height for optimal efficiency. The study also identified an upper basin water level for maximum efficiency. The effect of upper basin water level on power was also examined.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score0.232

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2023
Admission routes2
Has abstractyes

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