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Record W2968438958 · doi:10.4050/f-0075-2019-14512

Full-Configuration CFD Analysis of the S-97 RAIDER

2019· article· en· W2968438958 on OpenAlexaff
Patrick Bowles, Claude Matalanis, Margaret Battisti, Byung-Young Min, Brian Wake, Nick Tuozzo

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsAerodynamicsComputational fluid dynamicsFlight envelopeAerospace engineeringPropulsionPropellerComputer scienceRotor (electric)Wind tunnelFull scaleCoaxialFlight testEngineeringSimulationMarine engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The S-97 RAIDERTM is a next-generation light tactical helicopter which uses a coaxial dual main rotor and aft-mounted propeller for auxiliary propulsion. This advanced configuration presents challenges for aerodynamic modeling, particularly with regard to the interactions between various components and their dependency on flight condition and trim state across a wide envelope. This paper describes continued research into the aerodynamics of the coupled aircraft components. Computational simulations of multiple combinations of components have been conducted for a series of pitch sweeps and flight conditions. These computational fluid dynamics (CFD) results are compared to available scale-model wind-tunnel test results. As is typical with development programs, changes were made between the original small-scale testing and analysis, and the current flight-test aircraft. In addition, the available computational methods and processing capabilities have improved significantly since the program started in 2010.

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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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
Published2019
Admission routes1
Has abstractyes

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