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Record W2760231875 · doi:10.1109/sege.2017.8052803

Simulation and validation of thermal stability for complex system design high power dissipation

2017· article· en· W2760231875 on OpenAlexaff
Aziz Oukaira, Shamsodin Taheri, Ahmed Lakhssassi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSilicon Carbide Semiconductor Technologies
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsForced convectionNatural convectionMicroelectronicsMechanical engineeringHeat transferConvective heat transferHeat exchangerFinite element methodComputer scienceMechanicsNuclear engineeringMaterials scienceEngineeringElectrical engineeringPhysicsStructural engineering

Abstract

fetched live from OpenAlex

Nowadays, heat is an inevitable issue affecting the performance of electronic devices, used in several power applications. In order to improve the lifetime as well as the productivity of these devices, heat should be reduced or kept at a stable level. The objective of this work is to make a thermal study of the ASIC (4.68 mm × 5.97 mm) under natural and forced air convections. The simulation results carried out with the Finite Element Method (FEM)-based software i.e., COMSOL and NISA tools. The DBC (Dirichlet Boundary conditions) method is applied around the ASIC at 25°C. Through these simulations the relationship between the powers dissipated by ASIC and the difference of temperature in both forced and natural convection is validated. Simulation results show a decrease in temperature of 22°C under forced air convection. This work offers an appropriate tool to model a variety of physical phenomena characterizing a real problem as the heat exchange by convection. The temperature profile obtained from the FEM model can ensure a uniform temperature distribution in both natural and forced convection. This work could be regarded as an important basis for the improvement of the reliability of new microelectronic devices, commonly used in power networks.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.250

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 head0.085
GPT teacher head0.291
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 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

Citations13
Published2017
Admission routes1
Has abstractyes

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