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Record W2983927208 · doi:10.1115/gt2019-90747

Inlet and Outlet Characteristics Boundary Conditions for Large Eddy Simulations of Turbomachinery

2019· preprint· en· W2983927208 on OpenAlexaff
Nicolas Odier, Thierry Poinsot, Florent Duchaine, Laurent Gicquel, Stéphane Moreau

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsUniversité de Sherbrooke
FundersGrand Équipement National De Calcul Intensif
KeywordsTurbomachineryInletMechanicsComputational fluid dynamicsBoundary value problemContext (archaeology)Flow (mathematics)Boundary (topology)Large eddy simulationComputer scienceMechanical engineeringPhysicsEngineeringTurbulenceGeologyMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Abstract Inlet an outlet boundary conditions are essential elements of any CFD predictions and this is even more so for turbomachinery Large Eddy Simulations, either applied to academic or industrial configurations. For compressible solvers, non-reflecting, characteristic inlet boundary condition imposing total pressure, total temperature and flow direction is usually needed, while an outlet relaxation methodology that automatically adapts the outlet static pressure as a function of the desired mass-flow rate rate is used for turbomachinery flow predictions. Establishing such a framework is clearly desirable especially for industrial use of LES. Development and validations remain necessary in such a fully unsteady context as detailled hereafter.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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
GenreMethods

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

Citations4
Published2019
Admission routes1
Has abstractyes

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