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Record W3087518143 · doi:10.1139/cjp-2020-0240

Numerical analysis of blockage effects on the flow between parallel plates by using lattice Boltzmann method

2020· article· en· W3087518143 on OpenAlexvenueno aff
Naqib Ullah, Chao Zhou

Bibliographic record

VenueCanadian Journal of Physics · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsLattice Boltzmann methodsReynolds numberMechanicsPhysicsCylinderDrag coefficientDragLift (data mining)Flow (mathematics)Numerical analysisComputational fluid dynamicsComputer simulationGeometryTurbulenceMathematicsMathematical analysisComputer science

Abstract

fetched live from OpenAlex

In this study the two-dimensional flow over a square cylinder placed in between parallel plates is simulated numerically by using the lattice Boltzmann method (LBM) at low Reynolds numbers. Both the plates are obstructed by solid rectangular blocks of variable length. The fluid was allowed to flow between the parallel plates for Reynolds number (Re) from 75 to 150, and blockage ratio (g*) from 1 to 3. The numerical investigation does not simply yield the predictable primary region of recirculating flow connected to the obstructions; it also shows supplementary regions of the flow downstream of the single cylinder placed in a computational domain. These supplementary separation zones were not already described in the research. The numerical analysis shows that the flow downstream of obstructions and the single cylinder remained two-dimensional for Re varied from 75 to 150. Results available in previous research are reported and compared with both of the available experimental and numerical results for code validation with a single cylinder. Furthermore, the effects of various Re and blockage ratio on the lift forces and drag coefficient is analyzed. Under these circumstances, good agreement between experimental and numerical results are obtained. The hydrodynamic forces of the cylinder are strongly influenced by the spacing ratios.

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: none
Teacher disagreement score0.709
Threshold uncertainty score0.380

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 head0.018
GPT teacher head0.238
Teacher spread0.221 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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