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Record W2569786082 · doi:10.2514/6.2017-1530

Modernization of the real time control and data acquisition systems at the National Research Council of Canada’s 1.5m Tri-Sonic Wind Tunnel

2017· article· en· W2569786082 on OpenAlexaffabout
Greg Burns, Mazen Sabbagh, Stuart Rutherford, Yves Cronier

Bibliographic record

Venue55th AIAA Aerospace Sciences Meeting · 2017
Typearticle
Languageen
FieldEngineering
TopicIcing and De-icing Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsModernization theoryResearch councilWind tunnelData acquisitionComputer scienceControl (management)MeteorologyRemote sensingTelecommunicationsEnvironmental scienceReal-time computingEngineeringGeologyGeographyAerospace engineeringArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

The legacy systems that are used for real-time control and data acquisition at the NRC’s 1.5m Tri-Sonic wind tunnel have reached the end of their life cycle, and a project has been initiated to systematically replace them. This paper will describe the approach and design considerations that were used to replace the existing hardware and software systems, some dating back to 1984, with modern off-the-shelf components combined with custom software developed in LabVIEW Real-Time. It will also describe the performance improvements that were achieved as well as some of the new features that were incorporated into the systems.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.163
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.109
GPT teacher head0.304
Teacher spread0.195 · 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.

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
Published2017
Admission routes2
Has abstractyes

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