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Record W2903165997 · doi:10.1063/1.5055705

Quantitative lock-in thermography imaging of thermal-wave spatial profiles and thermophysical property measurements in solids with inner corner geometries using thermal-wave field theory

2018· article· en· W2903165997 on OpenAlexaff
Mingfeng Wang, Andreas Mandelis, Alexander Melnikov, Chinhua Wang

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsThermographyThermal diffusivityThermalClassification of discontinuitiesOpticsMaterials scienceField (mathematics)LaserInfraredPhysicsThermodynamics

Abstract

fetched live from OpenAlex

In this study, we established a theoretical photothermal model and its experimental validation for an infinitely long solid with an inner corner of arbitrary opening angle, with the solid being irradiated photothermally by a modulated laser beam of arbitrary spatial intensity distribution directed to the corner. The thermal-wave field distribution on the flat surfaces of the solid centered at the corner was obtained using the Green function method. Experimental results based on quantitative thermographic imaging were obtained and used to validate the theoretical model in which thermal diffusivity of an inner cornered stainless steel was measured. The thermal-wave theory based lock-in thermography imaging technique provides a quantitative tool for thermal property measurement and/or non-destructive evaluation of non-flat structures. It also generates valuable physical insights into the spatial distribution of the thermal-wave field in the neighborhood of geometric discontinuities such as inner corners in solids.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.234
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

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

Citations5
Published2018
Admission routes1
Has abstractyes

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