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Record W2970713464 · doi:10.1615/thmt-18.1100

Dynamics of pulsating low-Reynolds number channel flow

2018· article· en· W2970713464 on OpenAlexaff
Richard J. Lozowy, D. Kuhn

Bibliographic record

VenueProceeding of THMT-18. Turbulence Heat and Mass Transfer 9 Proceedings of the Ninth International Symposium On Turbulence Heat and Mass Transfer · 2018
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsReynolds numberTurbulenceMechanicsLaminar flowReynolds stressShear stressPhysicsOpen-channel flow

Abstract

fetched live from OpenAlex

Direct numerical simulation of pulsating channel flow is performed at a low-Reynolds and intermediate frequency. The non-dimensional parameters for the study are Reτ = 180, Wo = 15 and Auc/uc = 0.44. The periodic component of the velocity is found to deviate significantly from the laminar Stokes solution. The timeaveraged velocity collapses onto a profile that is similar to the non-pulsating case, except for it being shifted upward in the outer region. Close to the wall there is significantly less phase-lag in the streamwise Reynolds stress component compared to the wall-normal component. The Reynolds stress components deviate substantially from single harmonic motion with the highest deviation occurring towards the wall. A quadrant analysis of the turbulent shear stress highlights the variation of fluid structure motions through different phases in the pulse. It is shown that these components experience varying amounts of phase-lag.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.230
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

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Same venueProceeding of THMT-18. Turbulence Heat and Mass Transfer 9 Proceedings of the Ninth International Symposium On Turbulence Heat and Mass TransferSame topicHeat transfer and supercritical fluidsFrench-language works237,207