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Record W4212815735 · doi:10.1115/1.4053861

Application of the B-Spline Method to Solve Nonlinear Problem of Heat Conduction With Radiation Boundary Conditions Using Kirchhoff Transformation

2022· article· en· W4212815735 on OpenAlexaff
Z. Annasabi, Fouad Erchiqui, M. Souli

Bibliographic record

VenueJournal of Heat Transfer · 2022
Typearticle
Languageen
FieldEngineering
TopicNumerical methods in engineering
Canadian institutionsUniversité du Québec en Abitibi-Témiscamingue
Fundersnot available
KeywordsThermal conductionFinite element methodNonlinear systemBoundary value problemMathematical analysisThermalTransformation (genetics)MechanicsSpline (mechanical)MathematicsPhysicsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

Abstract This paper concerns the joint application of the B-spline method and the Kirchhoff transformation to solve the nonlinear problem of thermal conduction with radiation type boundary conditions. The proposed method requires few iterations, sometimes none, for solids subjected to prescribed temperature boundary conditions. This method can be deployed by other numerical approaches (boundary element method, finite element method, finite element method, etc.) for the resolution of the heat conduction equation (linear or nonlinear), in terms of the Kirchhoff transformation θ. For numerical implementation, the steady-state finite element method is considered. The numerical validation was performed for a hollow aluminum cylinder whose outer surface is subjected to radiation. Three types of thermal conductivities are considered: (i) constant, (ii) linear, and (iii) nonlinear. As an application, we studied the thermal response of an aluminum reactor, in the form of an annular disk with cooling tubes, exposed to thermal radiation.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.281
Teacher spread0.266 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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