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Record W3198014527 · doi:10.18280/mmep.080418

Chemically Reactive MHD Eyring-Powell Nanofluid Flow past a Stretching Surface with Convergence Test

2021· article· en· W3198014527 on OpenAlexvenueno aff
Abdullah Al‐Mamun, Sk. Reza‐E‐Rabbi, S. M. Arifuzzaman, Umme Sara Alam, Md. Shohel Parvez, Md. Shakhaoath Khan

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2021
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsnot available
Fundersnot available
KeywordsStreamlines, streaklines, and pathlinesNusselt numberNanofluidMechanicsSherwood numberFluid dynamicsHeat transferWork (physics)Flow (mathematics)Momentum (technical analysis)Mass transferThermodynamicsThermophoresisThermal radiationMaterials sciencePhysicsReynolds numberTurbulence

Abstract

fetched live from OpenAlex

The motive of this work is to examine the transfer of heat as well as mass phenomena of Eyring-Powell fluid flow on a stretching type of porous medium. The impact of heat absorption and thermal radiation are also considered here to describe the characteristics of the fluid flow. Firstly, a mathematical model of this fluid flow is established which includes time dependent continuity, energy, momentum and concentration formula. Then the fundamental equations are formed to dimensionless format. After that an explicit type finite difference technique is imposed to solve the model by taking the help of FORTRAN. The stability as well as convergence analysis (SCA) is used to develop and check the precession of the overall system. Moreover, the fields of temperature, velocity, concentration, skin friction, Nusselt number and Sherwood number got affected significantly with the influences of various pertinent parameters which are presented in different color diagrams. However, the updated visualization of the fluid flows is also presented by both isotherms and streamlines for the impact of radiation parameter. Moreover, for Eyring-Powell fluid, an interesting observation has been made that the thermophoretic parameter is influencing the temperature field significantly rather than the Brownian parameter. Finally, for the validation of the ongoing investigation, a suitable comparison is also depicted with some published papers and a proper agreement is noticed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.168
Teacher spread0.159 · 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.

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
Published2021
Admission routes1
Has abstractyes

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