Evidence map›Paper›PMID 39732767›Full record

ArticleScientific reports2024

Numerical treatment for Darcy-Forchheimer flow of propylene glycol with carbon nanotubes under the impacts of MHD and activation energy.

Hafiz Muhammad Shahbaz, Iftikhar Ahmad

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Hafiz Muhammad ShahbazDepartment of Mathematics, University of Gujrat, Gujrat, 50700, Pakistan. 21016109-015@uog.edu.pk.
Iftikhar AhmadDepartment of Mathematics, University of Gujrat, Gujrat, 50700, Pakistan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study is the application of a recurrent neural networks with Bayesian regularization optimizer (RNNs-BRO) to analyze the effect of various physical parameters on fluid velocity, temperature, and mass concentration profiles in the Darcy-Forchheimer flow of propylene glycol mixed with carbon nanotubes model across a stretched cylinder. This model has significant applications in thermal systems such as in heat exchangers, chemical processing, and medical cooling devices. The data-set of the proposed model has been generated with variation of various parameters such as, curvature parameter, inertia coefficient, Hartmann number, porosity parameter, Eckert number, Prandtl number, radiation parameter, activation energy variable, Schmidt number and reaction rate parameter for different scenarios. The refinement of each data-set is processed through RNNs-BRO for attestation of the proposed scheme. The outcomes are provided through graphical interpretation. The increment of curvature parameter results in the acceleration of the velocity profile, while an opposite behavior is noticed for higher values of inertia coefficient, Hartmann number, porosity parameter for single wall carbon nanotubes (SWCNTs) as well as multi wall carbon nanotubes (MWCNTs). The temperature of fluid increases for both SWCNTs and MWCNTs as the curvature parameter, radiation parameter, Eckert number, and Hartmann number are increased. However, an opposite trend is noticed for Prandtl number. The concentration profile is enhanced for higher values of activation energy variable and curvature parameter for both SWCNTs and MWCNTs, whereas opposite trend is observed for reaction rate parameter, and Schmidt number. The effectiveness of scheme is endorsed through various statistical measures like regression index, error histograms, correlation analysis and convergence analysis showing a minimum level of mean square error (E-12 to E-04) for the comprehensive simulation of the proposed model.

Indexed as

Activation energyBayesian regularizationCarbon nanotubesPropylene glycolRecurrent neural networks

Identifiers

PMID39732767
PMCPMC11682255

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.