MétaCan
Menu
Back to cohort
Record W2570981856 · doi:10.2514/6.2017-0807

New Methodology for Longitudinal Flight Dynamics Modelling of the UAS-S4 Ehecatl towards its Aerodynamics Estimation Modelling

2017· article· en· W2570981856 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA Modeling and Simulation Technologies Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicAerospace and Aviation Technology
Canadian institutionsUniversité du Québec
FundersDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsAerodynamicsFlight dynamicsAerospace engineeringComputer sciencePropulsionSoftwareVehicle dynamicsSimulationEngineering

Abstract

fetched live from OpenAlex

Unmanned Aerial Vehicle modelling has found diverse utilities in both civil and military applications. In order to develop an accurate model of their flight dynamics, it is important to properly estimate their aerodynamics coefficients. For this purpose, several methods are usually applied. This paper presents a methodology to obtain the flight dynamics of an Unmanned Aerial Vehicle, for which its aerodynamics coefficients were found based on its geometrical properties. This methodology was applied to the UAS-S4, designed and manufactured by Hydra Technologies, using DATCOM and TORNADO codes. The aerodynamic model thus found was compared with another model obtained by use of ANSYS Fluent software. The model was completed with a propulsion system developed by use of Javaprop. Results have shown that the obtained model is capable of estimating with accuracy the aerodynamic behaviour of the UAS-S4.

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.

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 score0.658
Threshold uncertainty score0.823

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 science0.0010.000
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 head0.148
GPT teacher head0.310
Teacher spread0.162 · 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