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Record W2218807426 · doi:10.1115/imece2014-37364

Thermal Spreading Resistance in Flux Channel With Arbitrary Heat Convection in the Sink Plane

2014· article· en· W2218807426 on OpenAlexaff
M. Razavi, Yuri S. Muzychka, Serpil Kocabiyik

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHeat sinkThermal resistanceHeat fluxMechanicsBoundary value problemHeat transfer coefficientHeat transferFinite element methodMaterials scienceConvective heat transferConvectionThermalMechanical engineeringThermodynamicsEngineeringPhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

Thermal spreading resistance is one of the key factors for designing the thermal management systems in microelectronic devices. This type of thermal resistance occurs in most of the microelectronic devices and causes some difficulties for thermal engineers to model the system. One of the common geometries in these devices is the flux channel. Different boundary conditions can be applied on the flux channel based on the designing criteria of the system including the arbitrary distribution of heat sinks over the sink plane. This boundary condition is usually simplified as a constant heat transfer coefficient to facilitate the modeling of the system. In this paper, a flux channel with an arbitrary distributed heat transfer coefficient over the sink plane is studied without simplification of the sink boundary condition. Both adiabatic and convective cooling over the edges of the flux channel are considered. Due to the complexity of the sink boundary condition, the conventional analytical solutions are not applicable and the method of least squares is used. By employing this approach, the effect of a non-uniform heat transfer coefficient on thermal spreading resistance is investigated. The solution is presented in form of a Fourier series expansion which can be used to obtain the temperature all over the channel. Results are validated with Finite Element Models, FEM. This approach is useful for thermal engineers who have some difficulty for modeling complex boundary conditions and presents an effective solution for thermal resistance in the flux channels.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.204

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 head0.006
GPT teacher head0.177
Teacher spread0.171 · 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 designSimulation or modeling
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

Citations3
Published2014
Admission routes1
Has abstractyes

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