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Record W3035439309 · doi:10.1063/5.0011444

Experimental investigation of turbulent flow in a two-pass channel with different U-shaped bends

2020· article· en· W3035439309 on OpenAlexfundno aff
Runzhou Liu, Haiwang Li, Ruquan You, Zhi Tao

Bibliographic record

VenueAIP Advances · 2020
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesUniversity of Guelph
KeywordsReynolds numberTurbulenceOpen-channel flowReynolds stressMechanicsVortexParticle image velocimetryTurbulence kinetic energyFlow (mathematics)Hydraulic diameterGeometryPhysicsPipe flowMaterials scienceMathematics

Abstract

fetched live from OpenAlex

Time-resolved particle image velocimetry is used to study internal flow field characteristics in U-shaped channels of square cross section and different structures of the bend section. The Reynolds number based on the hydraulic diameter of the channel is 8888, 13 333, or 17 777. The mean flow and Reynolds stress are considered, and proper orthogonal decomposition (POD) is used to investigate the flow characteristics. A series of important conclusions are drawn from the results. For the main flow, the structure of the bend section has an obvious influence on the flow field characteristics. The size and number of vortices in the corner area are significantly reduced because the increase in the Reynolds number makes the impact of the influx stronger. It can be seen from the clear differences in the Reynolds stress for different bend sections that the fluctuations caused by the mixing of the main flow and the vortices are significantly stronger than those at the boundary. The flow in the bend section is complex, there is a relatively high proportion of turbulent kinetic energy in the low-order modes, and there is an obvious stripe-like structure in the bend section of the channel in which the bend has both inner and outer circular walls, which matches the velocity field from the POD.

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 score0.065
Threshold uncertainty score0.500

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 head0.010
GPT teacher head0.218
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

Citations11
Published2020
Admission routes1
Has abstractyes

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