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Record W4224996134 · doi:10.18280/mmep.090227

Numerical Investigation of Conjugate Heat Transfer Between Spherical Solid Body and Fluid

2022· article· en· W4224996134 on OpenAlexvenueno aff
Muhammad Asmail Eleiwi, Farhan Lafta Rashid, Abbas Fadhil Khalaf, Sohaib Abdulrahman Tuama

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2022
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsnot available
FundersUniversity of KerbalaTikrit University
KeywordsMechanicsHeat transferThermal conductionHeat fluxConvective heat transferThermodynamicsFilm temperatureFluid dynamicsMaterials scienceFlow (mathematics)ConvectionFinite volume methodPhysicsNusselt numberTurbulenceReynolds number

Abstract

fetched live from OpenAlex

Conjugate heat transfer (CHT) happens often in engineering environments, involving convection as well as conduction in a fluid flow and a rigid body in contact with each other. Although the analytical solutions for the problems of the individual convection and conduction are surprisingly simple, solving the combined conjugate heat transfer problem is much more difficult. The CHT of a fluid (air) flowing past an unbounded sphere is the subject of this research. ANSYS Fluent V.16.0 is employed to solve the governing equations using a finite volume system, assuming axisymmetric, no normal convection, and steady physical properties. Heat is generated at a consistent and uniform rate by the sphere. The results demonstrated that as the air temperature rises, so does the temperature distribution. The temperature distribution will be reduced as the rate of air flow is raised. Also, the distribution of temperature will rise as the sphere heat flux increases. The flow rate distribution will increase as the air flow rate rises. The distribution of pressure will rise as the rate of air flow raises.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.198
Teacher spread0.179 · 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
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

Citations20
Published2022
Admission routes1
Has abstractyes

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