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Record W2335837810 · doi:10.1103/physreve.93.043118

Marginal turbulent state of viscoelastic fluids: A polymer drag reduction perspective

2016· article· en· W2335837810 on OpenAlexafffund
Li Xi, Xue‐Song Bai

Bibliographic record

VenuePhysical review. E · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsTurbulenceDragLaminar flowPhysicsMechanicsViscoelasticityShear flowClassical mechanicsThermodynamics

Abstract

fetched live from OpenAlex

The laminar-turbulent (LT) transition of dilute polymer solutions is of great interest not only for the complex transition dynamics itself, but also for its potential link to the maximum drag reduction (MDR) phenomenon. We present an in-depth investigation of the edge state (ES), an asymptotic solution on the LT boundary, in viscoelastic channel flow. For given Re and simulation domain size, mean flow statistics of the ES do not vary with the introduction of polymers, proving that there is a region of turbulent states not susceptible to polymer drag reduction effects. The dynamics of the ES features low-frequency fluctuations and in the longer domains we studied it is nearly periodic with regular bursts of turbulent activities separated by extended quiescent periods. Its flow field is dominated by elongated vortices and streaks, with very weak extensional and rotational flow motions. Polymer stretching is almost exclusively contributed by the mean shear and polymer-turbulence interaction is minimal. Flow structures and the kinematics of the ES match hibernating turbulence, an MDR-like phase intermittently occurring in turbulent dynamics. Its observation now seems to result from recurrent visits to certain parts of the ES. The ES offers explanations for the existence and universality of MDR, the quantitative magnitude of which, however, still remains unsolved.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.403

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 head0.008
GPT teacher head0.278
Teacher spread0.270 · 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 designBench or experimental
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

Citations18
Published2016
Admission routes2
Has abstractyes

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