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Record W3199874915 · doi:10.1115/gt2021-59238

Mathematical Model to Describe Double Circular Arc and Multiple Circular Arc Compressor Blading Profiles

2021· article· en· W3199874915 on OpenAlexaff
John Kidikian, Chelesty Badrieh, Marcelo Reggio

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTurbomachinery Performance and Optimization
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsGas compressorAirfoilMATLABComputer scienceSoftwareMechanical engineeringGeometric modelingArc (geometry)Engineering drawingGeometryEngineeringStructural engineeringMathematicsProgramming language

Abstract

fetched live from OpenAlex

Abstract For the past seven decades, a compressor aerodynamicist has developed various methodologies to design, analyze, and simulate compressor stages. In compressor design, three major subsequent steps can be identified: the one-dimensional mean-line methodology, the two-dimensional through-flow analysis, and the three dimensional computational fluid dynamics. One of the interconnecting threads, between these various x-dimensional analysis, is the compressor blade profile shape. This shape, of known and controllable geometric parameters, is usually accompanied by, or related to, loss models and known flow physics, either defined by theory or through experimental test. In this paper, a novel mathematical approach is described to define axial compressor airfoil profile shapes. These shapes, developed in a Cartesian coordinate system, can be used to create Double Circular Arc, Multiple Circular Arc, and a hybrid combination of the two types. The proposed methodology, based on the mathematics of circles, can be easily applied using generalized software such as Python or MATLAB, or be embedded in specialized engineering design software. In doing so, researchers and engineers can create compressor airfoil shapes which are consistent and flexible with respect to geometric parameter manipulation. Full details of the formulas, with respect to the camber line definition and the calculation of the profile intrados and extrados, are presented. A URL link to an equivalent MATLAB code, and a specialized engineering software, has been provided for those researchers that wish to apply the formulations and review its use.

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: Empirical · Consensus signal: none
Teacher disagreement score0.463
Threshold uncertainty score0.624

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 head0.026
GPT teacher head0.227
Teacher spread0.201 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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