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Record W4392878003 · doi:10.1016/j.dib.2024.110330

Time-series data from a hybrid finite element Large Eddy Simulation of flow over a backward-facing step

2024· article· en· W4392878003 on OpenAlexaff
Angus Creech

Bibliographic record

VenueData in Brief · 2024
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsAcadia University
FundersEngineering and Physical Sciences Research Council
KeywordsSeries (stratigraphy)Finite element methodTurbulenceReynolds stressComputer scienceLarge eddy simulationFlow (mathematics)Detached eddy simulationGalerkin methodComputational fluid dynamicsExperimental dataReynolds numberMechanicsAlgorithmApplied mathematicsMathematicsPhysicsReynolds-averaged Navier–Stokes equationsStructural engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

In previous work, a Large Eddy Simulation turbulence (LES) algorithm was developed for finite element-based computational fluid dynamics, using a hybrid continuous-discontinuous Galerkin scheme. The test case for this was a backward-facing step, which is a well-known example with published experimental results for validation. The results of this simulation were used to produce temporal statistics, such as averaged velocity and Reynolds stress profiles, and time-series data. In conjunction with previous published work by the authors, this data here will allow validation of other LES models and as well as detailed comparisons with the original model.

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: none
Teacher disagreement score0.541
Threshold uncertainty score0.731

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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