MétaCan
Menu
Back to cohort
Record W2023551288 · doi:10.1115/gt2014-26167

A Preliminary Design System for Turbine Discs

2014· article· en· W2023551288 on OpenAlexafffund
Yannick Ouellet, François Garnier, F. Roy, Hany Moustapha

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsÉcole de Technologie Supérieure
FundersPratt and Whitney CanadaNational Aeronautics and Space Administration
KeywordsTurbineFinite element methodCADComputer scienceRotor (electric)SoftwareComputer Aided DesignReduction (mathematics)Parameterized complexityDesign toolMechanical engineeringAutomotive engineeringEngineeringEngineering drawingStructural engineering

Abstract

fetched live from OpenAlex

In order to improve product development cycle, design engineers use multi-disciplinary analysis tools which allow better productivity. This paper covers the development of new tools to improve the preliminary design phase of turbine disc, being a critical part of aircraft engines. First, a new single platform D&A (Design & Analysis) tool integrating commercial CAD (Computer Aided Design) and FEA (Finite Element Analysis) software processing in batch mode is presented. This integrated architecture leads to a real improvement enabling a cohesive single integrated simulation environment that offers significant time reduction on user manipulation and execution. An optimization of disc geometry is then performed by using different optimization algorithms and configurations for a given disc parameterized model. The results show potential improvement over the current preliminary rotor discs for life and burst limited design. Finally, optimal curves obtained by developing HPT (High Pressure Turbine) disc reference charts, indicate how to get the minimum weight for given mechanical performance without running any structural analysis. These new tools supporting disc design have allowed improvement of disc life and durability leading to reduction of preliminary design phase duration.

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 score0.974
Threshold uncertainty score0.249

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 head0.011
GPT teacher head0.193
Teacher spread0.181 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

Explore more

Same topicTribology and Lubrication EngineeringFrench-language works237,207