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Aerodynamic Modelling of Unmanned Aerial System through Nonlinear Vortex Lattice Method, Computational Fluid Dynamics and Experimental Validation - Application to the UAS-S45 Bàlaam: Part 2.

2020· article· en· W3032926781 on OpenAlexafffund
Maxime Kuitche, Ruxandra Mihaela Botez, Arthur GUILLEMIN

Bibliographic record

VenueINCAS BULLETIN · 2020
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAerodynamicsComputationAirfoilNonlinear systemGridAerospace engineeringTurbulenceComputational fluid dynamicsConvergence (economics)VortexComputer scienceUnstructured gridPhysicsControl theory (sociology)Applied mathematicsClassical mechanicsMechanicsMathematicsEngineeringAlgorithmGeometryArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a comparison of a new non-linear formulation of the classical Vortex Lattice Method and a Computation Fluid Dynamics analysis in predicting the aerodynamic behaviour of an Unmanned Aerial System. The Computation Fluid Dynamics analysis used structured grid, for the airfoil, study and unstructured grid obtained from a grid convergence study, for the entire Unmanned Aerial System, that are needed to predict the aerodynamic coefficients. The Spalart-Allmaras and the k-ω models were used as turbulence models. The results have shown a close agreement between the methods presents and have indicated that the new formulation is adequate for aerodynamic model estimation.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.240
Teacher spread0.225 · 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
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

Citations7
Published2020
Admission routes2
Has abstractyes

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