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Single platform integration environment for turbine rotor design and analysis

2014· article· en· W2267897567 on OpenAlexaff
François Lagloire, Yannick Ouellet, Benoit Blondin, François Garnier, Hany Moustapha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsTurbineInteroperabilityFinite element methodSoftwareComponent (thermodynamics)Rotor (electric)Computer scienceDesign cycleIntegration platformEngineeringSystems engineeringMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

The present article covers briefly state of the art software interoperability technical solutions and the development of the first module of a new single platform D & A (design & analysis) tool for simulation and prediction of stress and burst behavior of turbine rotating disc a preliminary design stage. This platform singularity requires integration of multiple CAD (computer assisted design) & FEA (finite element analysis) tools processing in batch mode and driven from a SPIE (single platform integration environment). This first module is also to demonstrate, for an axial turbine disc hub axi-symmetric component, feasibility and usefulness of such a platform at preliminary design stage. Expected benefits of the D & A single platform are to improve output accuracy, reduce cycle time, improve process quality and improve resource productivity.

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: Methods · Consensus signal: none
Teacher disagreement score0.845
Threshold uncertainty score0.222

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 communication0.0000.000
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 head0.185
Teacher spread0.173 · 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
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

Citations1
Published2014
Admission routes1
Has abstractyes

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