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Record W4360611031 · doi:10.37285/bsp.sasat2023.22

Roll Damping Analysis of Crew Escape Vehicle with Grid Fins at Subsonic and Transonic Mach Numbers

2023· article· en· W4360611031 on OpenAlexfundno aff
M. Jathaveda, Kunal Garg, P. Balasubramanian, G. Vidya, Fmtd Adsg

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
FundersHeart and Stroke Foundation of Canada
KeywordsTransonicMach numberSubsonic and transonic wind tunnelCrewAerospace engineeringAerodynamicsGridPhysicsComputer scienceMechanicsAcousticsEngineeringAeronauticsMathematicsGeometry

Abstract

fetched live from OpenAlex

Crew Escape Vehicle (CEV) is a man rated vehicle, which is used to eject the crew module from main rocket in an emergency situation so that the crew can be saved.Grid fins are employed in this vehicle for aerodynamic stabilization.Aerodynamic characterization is quite crucial for mission simulation and studies.Static and dynamic aerodynamic coefficients govern aerodynamic behavior among which roll damping derivative represents aerodynamic damping due to roll motion.Roll damping derivatives are obtained from wind tunnel tests for range of Mach numbers but sparse data is available due to cost overhead hence CFD has been employed for same.Forced oscillation technique using CFD++ software has been validated for Basic Finner configuration and same has been applied for CEV in current studies.Roll damping derivative are obtained for CEV and compared with wind tunnel results.Current studies have been carried out for subsonic and transonic Mach number but can be extended to supersonic Mach number.

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: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.007
GPT teacher head0.213
Teacher spread0.207 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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