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Record W1976457151 · doi:10.1088/1755-1315/22/2/022004

Evaluation of an improved mixing plane interface for OpenFOAM

2014· article· en· W1976457151 on OpenAlexaff
M. Beaudoin, Håkan Nilsson, Maryse Page, Robert Magnan, Hrvoje Jasak

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2014
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsMixing (physics)TurbomachineryInterface (matter)Computer scienceFlow (mathematics)Plane (geometry)Rotor (electric)SimulationMechanicsMechanical engineeringPhysicsEngineeringGeometryMathematics

Abstract

fetched live from OpenAlex

A mixing plane interface provides a circumferentially averaging rotor-stator\ncoupling interface, which is extremely useful in practical turbomachinery simulations. It\nallows fundamentally transient problems to be studied in steady-state, using simplified mesh\ncomponents having periodic properties, and with the help of a multiple reference frames\n(MRF) approach. An improved version of the mixing plane interface for the community-driven\nversion of OpenFOAM is presented. This new version of the mixing plane introduces a perfield,\nuser-selectable mixing option for the flow fields at the interface, including the possibility\nto use a mass-flow averaging algorithm for the velocity field. We show that the quality of the\nmass-flow transfer can be improved by a proper selection of the mixing options at the\ninterface. This paper focuses on the evaluation of the improved mixing plane interface for\nvarious steady-state simulations of incompressible flows, applied to a simple 2D validation test\ncase, and to more complex 3D turbomachinery cases.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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