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Record W3010600802 · doi:10.1063/1.5144387

Targeted turbulent structure control in wall-bounded flows via localized heating

2020· article· en· W3010600802 on OpenAlexafffund
Jean-Pierre Hickey, Khaled Younes, Matthew Yao, Duosi Fan, Joseph Mouallem

Bibliographic record

VenuePhysics of Fluids · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsDragTurbulenceMechanicsPhysicsViscosityParasitic dragDrag divergence Mach numberDrag coefficientDirect numerical simulationClassical mechanicsThermodynamicsReynolds number

Abstract

fetched live from OpenAlex

A targeted turbulent flow control strategy, based on selective heating of streamwise-aligned heat strips, is assessed for drag reduction using direct numerical simulations of variable viscosity and compressible turbulent channel flows. As increasing the temperature of a gas increases its viscosity, heating is generally an unfavorable drag mitigation approach. However, through a selective spatial arrangement of the heating array, the slight increase in viscosity and decrease in density can serve to modify the organization of the streamwise-aligned structures and the likelihood of the ejection and sweep events near the wall. This can, under specific conditions, lead to a very modest drag reduction. The optimal spatial arrangement is identified using a bidimensional empirical mode decomposition and targets the near-wall, large-scale turbulent motion. The drag coefficient, at constant mass flow rate, remains unchanged with heating despite up to an 11% increase in the local viscosity above the heating strips. When accounting for the viscosity variation in the drag reduction calculation, an effective drag reduction of 6% is observed.

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 categoriesMeta-epidemiology (narrow)
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 score0.282
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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.

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

Citations18
Published2020
Admission routes2
Has abstractyes

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