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Record W3164628230 · doi:10.1063/5.0047531

Direct numerical simulation of turbulent heat transfer in concentric annular pipe flows

2021· article· en· W3164628230 on OpenAlexafffund
Edris Bagheri, Bing-Chen Wang

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsNusselt numberCylinderTurbulenceMechanicsHeat transferRADIUSReynolds numberBoundary layerConvective heat transferThermodynamicsGeometryMathematics

Abstract

fetched live from OpenAlex

The effect of radius ratio on turbulent convective heat transfer within a concentric annular pipe has been studied using direct numerical simulation. Four radius ratios (Ri/Ro = 0.1–0.7) have been compared at a fixed Reynolds number, where Ri and Ro denote the radii of the inner and outer pipes, respectively. The statistical moments of the temperature field, budget balances of the temperature variance and turbulent heat fluxes, and turbulence structures that dominate the heat transfer process have been thoroughly studied in both physical and spectral spaces. It is observed that the radius ratio has a significant impact on the Nusselt numbers and skin friction coefficients of the inner and outer cylinder walls, and on the interaction of thermal boundary layers developed over these two curved walls. Owing to the curvature difference between the two cylinder surfaces, the thermal boundary layer developed over the outer cylinder wall is thicker than that over the inner cylinder wall. Also, turbulent heat transfer is more intense on the outer cylinder side than on the inner cylinder side. As the radius ratio decreases, the difference in turbulence statistics between the inner and outer cylinder sides becomes increasingly pronounced. It is also observed that both axial and azimuthal characteristic length scales of the most energetic turbulent thermal structures are larger on the inner cylinder side than on the outer cylinder side.

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 score0.069
Threshold uncertainty score0.627

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations24
Published2021
Admission routes2
Has abstractyes

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