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Record W4409603784 · doi:10.61091/jcmcc127b-209

Numerical simulation of heat transfer characteristics of high temperature devices based on nonlinear partial differential equations

2025· article· en· W4409603784 on OpenAlexvenueno aff

Bibliographic record

VenueJournal of Combinatorial Mathematics and Combinatorial Computing · 2025
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsNonlinear systemPartial differential equationHeat transferMechanicsMaterials scienceComputer sciencePhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

This study takes the physical properties of high temperature devices as a starting point and the experimental apparatus used to obtain the study samples.The heat transfer process can be categorized into heat conduction, heat convection and heat radiation depending on the mode of contact.Under the theoretical support of the irst law of thermodynamics, the nonlinear partial differential equations of the heat transfer characteristics of the high temperature devices are determined, and the above equations are analyzed by numerical simulation with the help of ANSYS software.When the thickness of the device is 1um, 8um and 15um, the heat transfer temperature and the power of the heat source show a monotonically increasing trend, in addition, when the thickness of the device is a ixed value, the spacing of the heat source and the heat transfer temperature show a nonlinear monotonically decreasing, and the present study has an important practical signi icance for improving the heat transfer performance of high temperature devices.

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 score0.331
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.237
Teacher spread0.229 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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