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Record W2333618208 · doi:10.2514/6.2007-6573

Development and Assessment of CFD Methods for Integrated Simulation of Air Vehicle Stability and Control

2007· article· en· W2333618208 on OpenAlexaff
James Chung, Bradford Green, Mohagna J. Pandya, Neal T. Frink, J. R. Chambers

Bibliographic record

VenueAIAA Atmospheric Flight Mechanics Conference and Exhibit · 2007
Typearticle
Languageen
FieldEngineering
TopicComputational Fluid Dynamics and Aerodynamics
Canadian institutionsGeneral Dynamics (Canada)
FundersU.S. Department of Defense
KeywordsAirframeSystems engineeringSoftwareAir combatComputational fluid dynamicsEngineeringComputer scienceSuiteScalabilityAeronauticsAerospace engineeringOperating system

Abstract

fetched live from OpenAlex

The Naval Air Systems Command (NAVAIR), NASA Langley Research Center, AS&M, and GDIT inc. have joined together in a cooperative effort to develop a computational modeling and simulation suite capable of handling air vehicle stability. The funding and computer resources were provided by the DoD High Performance Computing Modernization Office (HPCMO) under the Common High Performance Computing Software Support Initiative (CHSSI). This project is under Collaborative Simulation and Testing (CST) portfolio to provide scalable software for military application to reduce risk in weapons system development and to provide information to senior decision makers throughout the life cycle of the system. This paper summarizes the three year effort to develop a methodology to utilize CFD analysis for an improved test and evaluation process on fixed-wing air vehicle stability and control problems and to demonstrate an integrated analysis capability of airframe-inlet-compressor interaction for a high-performance military jet.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.017
GPT teacher head0.287
Teacher spread0.271 · 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

Citations1
Published2007
Admission routes1
Has abstractyes

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