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Record W3172302570 · doi:10.3390/aerospace8060166

Effect of a Circular Slot on Hybrid Airship Aerodynamic Characteristics

2021· article· en· W3172302570 on OpenAlexaff
S. Mano, RadhaKrishnan Ajay Sriram, G. Vinayagamurthy, S. Nadaraja Pillai, Amjad Ali Pasha, D. Siva Krishna Reddy, Mustafa Mutiur Rahman

Bibliographic record

VenueAerospace · 2021
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAerodynamicsLift coefficientStall (fluid mechanics)Lift (data mining)Angle of attackAerospace engineeringPressure coefficientBase (topology)MechanicsFlow (mathematics)Flow separationStructural engineeringPhysicsEngineeringComputer scienceMathematicsReynolds number

Abstract

fetched live from OpenAlex

This numerical study reports the aerodynamic properties of a hybrid airship. The hybrid airships were designed by combining two semi-ellipsoids with a semi-discoid as the base model. From the base model, three different geometrics were identified to study their aerodynamic characteristics. A circular slot was provided between the pressure side and the suction side of the airship. The objective of this study was to realize the flow behavior, aerodynamic characteristics, and stability properties of such slotted hybrid flying vehicles. Interestingly, the results imply that the lift coefficient increases with an increase in the angle of attack for the slotted configurations; this is because the flow separation is delayed due to the slot opening, which in turn is due to the flow of energies from the high-pressure region to the bottom through the slots. The delayed stall angle was 50 degrees, which was 10% more than that of the base model. Aerodynamic characteristics are discussed based on surface pressure, coefficient of lift, and coefficient of drag for various slotted hybrid airships.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.003
GPT teacher head0.183
Teacher spread0.180 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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