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Record W2774338810 · doi:10.48550/arxiv.1712.05777

Pseudophase-Change Effects in Turbulent Channel Flow under Transcritical Temperature Conditions

2017· preprint· en· W2774338810 on OpenAlexafffund
Kukjin Kim, Jean-Pierre Hickey, Carlo Scalo

Bibliographic record

VenuearXiv (Cornell University) · 2017
Typepreprint
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaPurdue University
KeywordsTurbulenceCompressibilityMechanicsBuoyancyDirect numerical simulationSupercritical fluidThermodynamicsIsothermal processEnstrophyPhysicsChemistryMaterials scienceVortexReynolds number

Abstract

fetched live from OpenAlex

We have performed direct numerical simulations (DNS) of compressible turbulent channel flow at supercritical pressure with top and bottom isothermal walls kept respectively at a supercritical (Ttop > Tpb) and subcritical temperature (Tbot < Tpb), where Tpb is the pseudoboiling temperature. The DNS are conducted using a high-order discretization of the fully compressible Navier-Stokes equations in conservative form closed with the Peng-Robinsion (PR) state equation. Bulk density is adjusted to obtain a bulk pressure of approximately pb = 1.1pcr where pcr is the critical pressure of the working fluid. Top-to-bottom temperature differences investigated are DT = 5 K, 10 K, and 20 K, where Ttop/bot = Tpb +- DT / 2; buoyancy effects are neglected. Varying DT modifies the average location of pseudophase change from ypb/h = -0.23 (DT = 5 K) to 0.89 (DT = 20 K), where h is the channel half-height and y = 0 the centerline position. Real-fluid effects cause visible deviations from classical scaling laws in the mean velocity profile. Enstrophy generation due stretching and tilting decreases with DT. The proximity to the pseudotransitioning layer inhibits the intensity of the velocity fluctuations, while enhancing the density and temperature fluctuations. Conditional probability analysis reveals that the sheet of fluid undergoing pseudophase change is characterized by a dramatic reduction in the kurtosis of density fluctuations and becomes thinner as DT is increased. Instantaneous visualizations show dense fluid ejections from the pseudoliquid viscous sublayer, some reaching the channel core, causing positive values of density skewness in the respective buffer-layer region (vice versa for the top wall).

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 score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.065
GPT teacher head0.208
Teacher spread0.143 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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