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Record W4392499133 · doi:10.2166/wst.2024.072

Particle separator with vortex claw: an efficient and new technology

2024· article· en· W4392499133 on OpenAlexafffund
Cheng He, P. Chittibabu, David Nguyen, Quintin Rochfort

Bibliographic record

VenueWater Science & Technology · 2024
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of GuelphEnvironment and Climate Change Canada
FundersEnvironment and Climate Change Canada
KeywordsSeparator (oil production)SettlingInflowMechanicsVortexTurbulenceParticle (ecology)Particle sizeEnvironmental scienceMarine engineeringMaterials scienceAerospace engineeringPhysicsEngineeringGeologyEnvironmental engineeringChemical engineeringThermodynamics

Abstract

fetched live from OpenAlex

Despite facing many challenges, the exploration of using natural forces and mechanisms besides gravity to enhance particle settling has never ceased. A novel particle separator design, which utilizes multiple vortexes to enhance particle settling, was proposed in this study. The basic principle is using the fluid's energy to generate small swirling currents in a specially designed vortex claw generator. These currents bring suspended particles from the rapid and turbulent inflow to relatively quiet water regions, separating them from the main flows and reducing their travel distance to the wall. To verify the new separator design's performance, comparison studies were carried out in the laboratory using physical models. The results showed that the new design had much higher particle capture rates for the same inflow rates and tested particle sizes. Most importantly, it was able to effectively remove small particles, and particle capture rates were much less affected by fluctuations in inflow rates. Since most existing particle separators failed to perform well under large inflow rates, these characteristics make the new design stand out from other separators. Due to its special structure, its treatment capacity can also be easily increased without changing its horizontal separator size.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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