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Record W2564160455 · doi:10.1002/cjce.22766

Impeller diameter and submergence effects in solids drawdown with up‐pumping impellers

2016· article· en· W2564160455 on OpenAlexvenueno aff
Kevin J. Myers, Anand Kumar Pandit, Eric E. Janz, Julian B. Fasano

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsImpellerDrawdown (hydrology)Slip factorMaterials scienceMechanicsPower (physics)Flow (mathematics)TorqueMechanical engineeringEngineeringGeotechnical engineeringPhysicsThermodynamics

Abstract

fetched live from OpenAlex

Floating solids drawdown speeds of up‐pumping pitched‐blade and hydrofoil impellers increase with increasing impeller submergence, with the pitched‐blade speed increasing more rapidly than that of the hydrofoil. For submergences greater than 40 % of the vessel diameter, performance of the pitched‐blade impeller is adversely affected by the discharge flow impinging on the vessel wall rather than the free liquid surface. Drawdown speeds of both impeller types decrease in a similar manner with increasing impeller diameter. For both impeller types, drawdown torque continually increases with increasing impeller diameter, while drawdown power exhibits a minimum at an intermediate impeller to tank diameter ratio; however, power is not a strong function of impeller diameter to tank diameter ratio over the range of this parameter typically used industrially (D/T ranging from 0.2 to 0.5 was studied in this work). Drawdown torque and power of the hydrofoil impeller are lower than those of the pitched‐blade impeller, with the differences between the impeller types increasing rapidly with submergence.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.003
GPT teacher head0.149
Teacher spread0.146 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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