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Record W4281264046 · doi:10.1177/09544062221081395

Thermal analysis of unsteady hybrid nanofluid magneto-hemodynamics flow via overlapped curved stenosed channel

2022· article· en· W4281264046 on OpenAlexaff
Akbar Zaman, Ambreen Afsar Khan, Fazle Mabood, A. Abbasi, Irfan Anjum Badruddin

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2022
Typearticle
Languageen
FieldEngineering
TopicNanofluid Flow and Heat Transfer
Canadian institutionsFanshawe College
FundersDeanship of Scientific Research, King Khalid University
KeywordsMechanicsNanofluidVector fieldFlow (mathematics)Finite difference methodPartial differential equationDimensionless quantityPhysicsMaterials scienceMathematicsHeat transferMathematical analysis

Abstract

fetched live from OpenAlex

In this paper, we present a detailed mathematical model of the unsteady hybrid blood through the curved overlapping stenosed vessels in the presence of the applied external magnetic field. The governing differential equations of this model are derived from the generic laws of conservation of momentum and energy. These derived differential equations are then rendered dimensionless by incorporating the normalization parameters, which are subsequently followed by the implementation of mild stenotic supposition. The parabolic equations are solved numerically by using the explicit finite difference technique. After that, these numerical calculations are used to simulate several flow characteristics such as velocity, impedance, and wall shear stress using the set of emerging parameters taken from literature. The influence of these emerging parameters on the flow characteristics reveals that the velocity of the hybrid blood flow stream decelerates due to the presence of an induced magnetic field having the inclusion of nanoparticles. Finally, the global behavior of hybrid blood flow within the curved stenosed channels is sketched and analyzed by using various streamline plots.

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)0.002
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: Empirical
Teacher disagreement score0.179
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.195
Teacher spread0.187 · 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

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