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Record W3199755187 · doi:10.32393/csme.2021.195

Assessment Of Turbulent Mixing In A Static Mixer Using Mean Age

2021· article· en· W3199755187 on OpenAlex
Kanishk N Patel, Alexandra Komrakova

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProgress in Canadian Mechanical Engineering. Volume 4 · 2021
Typearticle
Languageen
FieldEngineering
TopicParticle Dynamics in Fluid Flows
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMixing (physics)TurbulenceStatic mixerStatistical physicsMechanicsMaterials scienceComputer scienceMathematicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Static mixers are in-line motionless devices that are placed into a pipe to promote the blending of miscible fluids or dispersion of immiscible liquids. New designs of static mixers are continuously proposed to meet certain requirements of the final product. Instead of manufacturing numerous prototypes of inserts and conducting costly experiments to assess spatial and temporal mixing, it is suggested to use computational fluid dynamics (CFD) to visualize and quantify the efficiency of mixing in new insert designs. In this study, the mixing assessment of the Kenics mixer with six Kenics elements was performed by evaluating the mean age distribution for a range of Reynolds numbers between 1 and 12 000 covering laminar, and turbulent flow regimes. A single Kenics element can be described as a twisted ribbon like structure which can be fitted at the center of the pipe to act as an obstruction. The Kenics elements with clockwise and counterclockwise twist can be placed alternatively to form a Kenics mixer.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.258
Teacher spread0.245 · 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