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Three-dimensional mean flow field around two cubes in tandem

2025· article· en· W4412433045 on OpenAlexafffund
Bárbara L. da Silva, David S. Sumner, Donald J. Bergstrom

Bibliographic record

VenueInternational Journal of Heat and Fluid Flow · 2025
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaUniversity of Saskatchewan
KeywordsTandemFlow (mathematics)MechanicsMaterials sciencePhysics

Abstract

fetched live from OpenAlex

The mean flow field around two surface-mounted cubes in tandem was investigated through large-eddy simulations at a Reynolds number of Re = 1 × 1 0 4 and with a turbulent boundary layer of thickness δ / D = 0 . 8 at the location of the upstream cube. Center-to-center spacing ratios of L / D = 2 , 2.5 and 4 were considered to describe the intermittent reattachment, cavity-locked and synchronized shedding regimes, respectively. The mean flow features were related to the near-surface flow field of the cubes and their drag and normal force coefficients. Although an arch vortex was always present behind the upstream cube, the flow in the gap changed significantly depending on the different flow regimes, affecting the shape, size and strength of the arch vortex. The flow field surrounding the upstream cube did not change significantly with L / D , presenting a similar near-surface flow field to an isolated cube. The near-surface flow for the downstream cube changed from a reattachment pattern for L / D = 2 , to impingement for L / D = 2 . 5 , to flow separation from the leading edges and the appearance of a second horseshoe vortex for L / D = 4 . The arch vortex of the downstream cube was uniquely shaped and similar for all L / D , due to flow separation from the cube’s rear edges. Base-like streamwise vorticity regions were present in the downstream cube’s wake, which suggest they may be the time-averaged signature of the vortices shed from the cube.

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 score0.086
Threshold uncertainty score0.458

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 head0.234
Teacher spread0.227 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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