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Record W2747743674 · doi:10.1115/gt2017-64932

Measurement of Large Flow Angles With Non-Nulling Multi-Hole Pressure Probes

2017· article· en· W2747743674 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueVolume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy · 2017
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsDalhousie UniversityNational Research Council Canada
Fundersnot available
KeywordsCalibrationFlow (mathematics)Mode (computer interface)Reduction (mathematics)Computer scienceBasis (linear algebra)Electronic engineeringMechanical engineeringRemote sensingPhysicsOpticsEngineeringMechanicsGeologyMathematicsGeometry

Abstract

fetched live from OpenAlex

In complex flow fields, the steady-state, three-dimensional attributes can be measured intrusively using a multi-hole pressure probe. These probes are built to be as small as possible to minimize flow disturbance, but the small size makes them susceptible to both manufacturing tolerances and fouling. As such, each probe must be calibrated on a regular basis and care must be taken to ensure that the geometry is not disturbed. The probes are operated in either “nulling” or “non-nulling” modes, the latter being the simpler of the two in terms of experimental setup and mechanical fixturing. This has made non-nulling mode the preferred choice for many years; however, non-nulling mode necessitates complete three-dimensional calibration data. Sector-based calibration strategies have become nearly universal, although improvement efforts continue. This paper presents a new calibration and data analysis strategy that gives shorter calibration times and more robust data reduction.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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