MétaCan
Menu
Back to cohort
Record W1029083295 · doi:10.1115/imece2014-39286

Experimental, Numerical and Analytical Investigation of Thermal Resistance in High Brightness LED Arrays

2014· article· en· W1029083295 on OpenAlexaff
Mahmood R. S. Shirazy, Andréane D’Arcy-Lepage, Michel Gilbert, Samuel Richard, Luc G. Fréchette

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsThermal resistanceHeat sinkMaterials scienceThermocoupleThermalThermographyJunction temperatureThermal management of high-power LEDsLED lampFinite element methodMechanical engineeringLight-emitting diodeSpreading resistance profilingTemperature measurementInfraredOptoelectronicsOpticsComposite materialEngineeringElectrical engineeringStructural engineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Thermal performance of a commercial LED array module has been studied by experimental, numerical and analytical approaches to find the dominant thermal resistance in the thermal circuit. The light quality, lifetime and reliability of the LED modules depend strongly on the junction temperature which can be obtained and modified by a suitable thermal resistance model. Analytical models for the first level packaging (Die) and second level packaging (PCB and heat sink) have been developed with special attention to the thermal spreading resistance. Numerical modeling has been performed using commercial finite element software (COMSOL) and the results are in good agreement with the analytical models. An LED array module has also been studied experimentally by measuring the temperature field in the PCB and heat sink using thermocouples and infrared thermography. The results of the experimental part are used to validate the numerical and analytical models. It is shown that more than 50% of the total thermal resistance is caused by the heat sink while the PCB and LED package each share 25% of the total thermal resistance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.135
Threshold uncertainty score0.503

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)0.0000.000

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 head0.013
GPT teacher head0.245
Teacher spread0.232 · 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.

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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicGaN-based semiconductor devices and materialsFrench-language works237,207