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Record W4400426897 · doi:10.1080/10407782.2024.2375322

A comparative study of heat absorption and chemical reaction on MHD flow with fractional derivatives

2024· article· en· W4400426897 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNumerical Heat Transfer Part A Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMagnetohydrodynamicsChemical reactionMechanicsFlow (mathematics)ThermodynamicsChemistryMaterials sciencePhysicsOrganic chemistryPlasmaNuclear physics

Abstract

fetched live from OpenAlex

In this article, Caputo and Prabhakar fractional derivatives are used to analyze the influence of heat flux on fractionalized second grade flow. The fluid model is generalized by Fick’s and Fourier’s laws. Moreover, radiation and slip effects are also taken into account additionally. Fractional governing models are solved semi-analytically by using Caputo and Prabhakar fractional derivatives. The method of Laplace method is applied to solve the dimensional model for temperature, velocity, and concentration profiles. The results are contrasted visually. A variety of graphs are used to illustrate the impacts of several parameters, including the heat absorption Q, fractional parameter, magnetic parameter M, and chemical reaction R. It is evident from the figure that the velocity distribution is affected less by chemical and magnetic field, while the fluid velocity is affected more by diffusion-thermodynamics and mass Grashoff number. Furthermore, comparisons among classical and fractional fluid models are made to check the validity of the result. It is noted that the classical approach is less convenient as compared to the fractional approach.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.020
GPT teacher head0.261
Teacher spread0.241 · 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