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Record W2033477717 · doi:10.1002/fld.658

Pressure weighted upwinding for flow induced force predictions: application to iced surfaces

2004· article· en· W2033477717 on OpenAlexaff
G.F. Naterer

Bibliographic record

VenueInternational Journal for Numerical Methods in Fluids · 2004
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsUpwind schemeMechanicsLift (data mining)DragContext (archaeology)Flow (mathematics)GeologyMathematicsPhysicsComputer scienceMathematical analysis

Abstract

fetched live from OpenAlex

Abstract This paper addresses two main topics, namely the development of a pressure‐weighted upwinding method and its application to flow induced forces on iced cylinders. Although the near‐wall convective upwinding exhibits special applicability to iced surfaces, its capabilities extend more generally to other applications. By fully linking pressure and velocity at a sub‐element level near the wall, a higher order accuracy can be obtained. Also, a non‐physical de‐coupling between pressure and velocity can be prevented. The method is developed under the context of a control‐volume‐based finite element method for 2‐D, incompressible flows. Drag and lift coefficients are predicted, based on the pressure weighted upwinding near the wall. The numerical predictions are successfully compared against experimental data, including flow induced forces on iced cables. Copyright © 2004 John Wiley & Sons, Ltd.

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)0.002
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.382
Teacher spread0.352 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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