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Record W2143876459 · doi:10.1109/tadvp.2006.873136

A General Method for the Connection of a Component Thermal Model to a Board

2006· article· en· W2143876459 on OpenAlexaff
Xian Guo, D. Celo, D.J. Walkey, T. Smy

Bibliographic record

VenueIEEE Transactions on Advanced Packaging · 2006
Typearticle
Languageen
FieldEngineering
Topic3D IC and TSV technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsConnection (principal bundle)Component (thermodynamics)ThermalElectronic componentPrinted circuit boardFlow (mathematics)Electronic packagingThermal conductivityBoundary value problemJunction temperatureSimple (philosophy)Heat flowComputer scienceMechanical engineeringMechanicsElectronic engineeringEngineeringMathematicsThermodynamicsMathematical analysisPhysics

Abstract

fetched live from OpenAlex

In this paper, a generalized method for the connection of a thermal component model in board- and system-level thermal simulations is presented. The method allows for the definition of uniform heat flow connections as well as the standard uniform temperature interface regions. The use of uniform heat flow ports will be shown to better handle cases where large temperature gradients are present in the base model. The two methods of connecting the component model will be evaluated using two different models. First, a simple example will be presented to illustrate the nonphysical behavior introduced by the use of uniform temperature connections. Second, a model of an electronic package will be used to evaluate the relative merits of the two connection methods with respect to board thermal conductivity and boundary conditions present on the board and the package. It will be shown that the results from use of uniform heat flow connections are generally better than from use of uniform temperature regions with respect to predicting junction and board temperatures.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0110.004

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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2006
Admission routes1
Has abstractyes

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