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Record W4313140051 · doi:10.1115/fedsm2022-88624

Immersed Boundary Method Implemented in LES for Flow Past a Sphere at Subcritical Reynolds Numbers

2022· article· en· W4313140051 on OpenAlexaff
H. Ali Marefat, Jahrul Alam, Kevin Pope

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsReynolds numberWakeMechanicsBluffFlow (mathematics)Hele-Shaw flowLarge eddy simulationPhysicsImmersed boundary methodTurbulenceClassical mechanicsStatistical physicsBoundary (topology)MathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Flow within the sub-critical range of Reynolds number around spherical bluff bodies has a complex dynamic behaviour due to transient fluid flow happening in the wake of the sphere. Large-eddy simulation and penalization methods have been used successfully for approximating these flows for the last two decades. In this work, a combination of two approaches is presented for flow past a spherical bluff body at sub-critical Reynolds numbers. Results at Re = 1000 presented in comparison with direct numerical simulation results and experimental data available in the literature. They show a very good agreement with data in the literature despite using a comparably large computational mesh. In addition, the essential characteristics of flow dynamics and the coherent vortical structures in the wake of the sphere are investigated and discussed in detail for Reynolds numbers less than 10,000.

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)0.001
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.268
Teacher spread0.258 · 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
Published2022
Admission routes1
Has abstractyes

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