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Record W4285677270 · doi:10.1002/cjce.24556

Performance characteristics of an axial‐flow gas induction impeller

2022· article· en· W4285677270 on OpenAlexvenueno aff
Shannon M. Hoffman, Eric E. Janz, Kevin J. Myers, Nicholas A. Brown

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerMechanicsRotational speedVolumetric flow rateAxial compressorFlow (mathematics)TurbulenceFlow coefficientSlip factorPressure coefficientMaterials scienceControl theory (sociology)Mechanical engineeringPhysicsEngineeringGas compressorComputer science

Abstract

fetched live from OpenAlex

Abstract Power number, pressure coefficient, and induced gas flow rate of a novel axial‐flow gas induction impeller with large openings for gas flow are reported. The effects of rotational speed, impeller diameter, submergence, and pumping mode are considered in turbulent operation. The pressure coefficient is measured using the reduction in pressure at speeds below the minimum induction speed as well as from the minimum induction speed. The minimum induction speed approach consistently yields lower pressure coefficient values, indicating the existence of impeller exit losses that are required to force gas from the centre of the impeller into the liquid. The induced gas flow rate is modelled using two approaches. The first approach relates the gas flow rate to the available pressure difference, and in this case, the relation is found to be strongly affected by impeller diameter. The second approach is a literature model that likens the induction process to water jet injection. While this approach appears promising, flow measurement device pressure losses make it difficult to definitively evaluate this method.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.260

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 head0.006
GPT teacher head0.149
Teacher spread0.143 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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