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Record W3116913125 · doi:10.18280/mmep.070412

Scaling and Integral Solutions to Mixed Convection Over an Exponential Stretching Sheet

2020· article· en· W3116913125 on OpenAlexvenueno aff
Rudra Murthy B. Veerabhadrappa, Vashist Ademane, Veershetty Gumtapure, Vijay Kumar Hindasageri

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2020
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsScalingSimilarity (geometry)Prandtl numberMathematicsSimilarity solutionIntegral equationExponential functionFlow (mathematics)Mathematical analysisWork (physics)Applied mathematicsConvectionPhysicsMechanicsComputer scienceThermodynamicsGeometryBoundary layer

Abstract

fetched live from OpenAlex

The reported studies on mixed convection flow problems have been solved purely by method of similarity studies. Scaling analysis is an alternate method that can give better engineering insight of the problem being investigated. Integral solutions are mathematically simpler to handle as the engineering requirement is that of accurate solutions only close to the wall. In the present work, scaling and integral solutions are discussed for a typical mixed convection flow problem already discussed in literature by similarity technique. Scaling method has been demonstrated and is found in good agreement with the results obtained from similarity method. The integral solution is obtained by deriving the integral form of governing equation and solution is discussed for specific case of Prandtl number = 1. The solution obtained by Integral formulations is in good agreement with that of similarity method.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.032
GPT teacher head0.202
Teacher spread0.170 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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