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Record W1987923124 · doi:10.1115/2000-gt-0052

Evaluation of an Uncooled Focal Plane Array Infrared Camera for Effusion Cooling Research

2000· article· en· W1987923124 on OpenAlexaff
Scott K. McGhee, A. M. Birk

Bibliographic record

VenueVolume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; Education · 2000
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsQueen's University
Fundersnot available
KeywordsOpticsInfraredImage resolutionCardinal pointCalibrationMaterials scienceThermographyDetectorInstrumentation (computer programming)Sensitivity (control systems)ThermocoupleTemperature measurementRemote sensingPhysicsComputer scienceGeologyEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

This study assessed a low-cost, uncooled ferroelectric detector infrared camera for effusion cooling research. Advances in uncooled IR technology have led to applications previously limited to research-grade cameras. The imager operated in the 7–14μm waveband and sampled up to 30 frames per second. Thermal images were made of a matte-black flat plate, downstream of two cylindrical jets with injection angles of α = 30° and 90°, and L/D = 6. Thermocouple calibration was specific to each image. Statistical analysis and image analysis yielded detailed temperature maps with uncertainty as small as 0.9°C, a spatial resolution of 0.4mm, and a sensitivity of 0.1 °C. The system compared favorably with established infrared systems. Advantages include minimal instrumentation, on-line results, and a high degree of accuracy and resolution, at significantly reduced cost.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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 designBench or experimental
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
Published2000
Admission routes1
Has abstractyes

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Same venueVolume 4: Manufacturing Materials and Metallurgy; Ceramics; Structures and Dynamics; Controls, Diagnostics and Instrumentation; EducationSame topicHeat Transfer MechanismsFrench-language works237,207