MétaCan
Menu
Back to cohort
Record W7147548371 · doi:10.5281/zenodo.19349290

A Novel Approach to Turbomachinery Blading Design in Three-Dimensional Flow Using Commercial CFD Tools

2023· article· W7147548371 on OpenAlexaff
Mohammad Ali Rostami, Ali Reza Mirzaei

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Language
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsToronto General Hospital
Fundersnot available
KeywordsTurbomachineryAxial compressorAerodynamicsComputational fluid dynamicsGas compressorBlade (archaeology)Rotor (electric)Flow (mathematics)

Abstract

fetched live from OpenAlex

An aerodynamic inverse shape design of turbomachinery blading in three-dimensional viscous flow is developed and implemented into a commercial CFD program, namely ANSYS-CFX. It is applied to redesign the rotor blades of an axial compressor stage and an axial turbine stage. The design method is based on specifying one blade parameter, the stacking line (a blade line from hub to tip), and two other parameters such as the blade loading and thickness distribution or the pressure distributions on blade surfaces. This inverse design approach is fully consistent with the viscous flow assumption and is independent of the CFD approach taken. An axial compressor stage E/CO-3 and turbine stage E/TU-3 are analysed and the results thus obtained are assessed against available experimental data. These stages are then inversely designed in order to improve their aerodynamic performance.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.092
GPT teacher head0.257
Teacher spread0.165 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicTurbomachinery Performance and OptimizationFrench-language works237,207