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Thermal Diffusivity by The Laser Flash Technique

2012· other· en· W1954480451 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

VenueCharacterization of Materials · 2012
Typeother
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsAdvantage Forensics (Canada)Dalhousie University
Fundersnot available
KeywordsThermal diffusivityLaser flash analysisHeat transferLaserMechanicsThermalMaterials scienceThermal conductionThermodynamicsOpticsPhysics

Abstract

fetched live from OpenAlex

Abstract Thermal diffusivity is an important material thermophysical property. The most widely used method for measuring thermal diffusivity is the laser flash technique. In this technique, a sample is placed within a controlled atmosphere furnace and subjected to a finite impulse of radiant energy on its front surface, through the use of a laser. The transport of heat through the sample, as a result of the laser impulse, causes a transient temperature rise on the rear surface of the specimen. This temperature rise is measured by an IR detector placed above the rear sample surface. The net result is a “thermogram” which is a plot of the rear‐face temperature versus time. Assuming a proper set‐up and careful experimentation, the transfer of heat under these conditions approximates one‐dimensional heat flow. Comparing the experimental data with one‐dimensional heat flow theoretical predictions, allows an estimation of thermal diffusivity. There are several methods available to determine thermal diffusivity based on experimental and theoretical comparisons. The simplest method is to determine the “half‐rise time,” t 0.5, which is the time at which the experimentally measured rear‐face temperature reaches half of its maximum value. More accurate methods use sophisticated analysis algorithms to model and fit the entire experimental thermogram curve to an ideal theoretical curve by means of a nonlinear least‐squares procedure. These approaches can include corrections that account for the fact that the experimental measurements only approximate one‐dimensional heat flow conditions. Using the thermogram curve fitting techniques, the measurement of thermal diffusivity of a range of material types including solid thermal insulators and conductors is possible. It is also possible to measure the apparent diffusivity of inhomogeneous samples such as composites and porous materials. Using two‐ and three‐layer analysis methods allows the measurement of thermal diffusivity of liquids. The layer methods can be extended to a determination of the thermal contact resistance of interfaces encountered in coated or bonded materials.

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 categoriesInsufficient payload (model declined to judge)
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 score0.271
Threshold uncertainty score0.995

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.005
GPT teacher head0.187
Teacher spread0.182 · 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