Evidence map›Paper›PMID 42804028›Full record

ArticleDiscover nano2026

Neural network-based study of thermal behaviour in radiative magnetized Casson micropolar nanofluid flow between two disks.

Saima Noor, Laila A Al-Essa, Humaira Yasmin, Izharul Haq

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Article in Discover nano, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Saima NoorDepartment of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al Ahsa, 31982, Saudi Arabia.
Laila A Al-EssaDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
Humaira YasminDepartment of Mathematics and Statistics, College of Science, King Faisal University, P.O. Box 400, Al Ahsa, 31982, Saudi Arabia. hhassain@kfu.edu.sa.ORCID https://orcid.org/0000-0003-0199-6850
Izharul HaqCollege of Sciences and Human Studies (CSHS), Prince Mohammad Bin Fahd University, Al Khobar, Dammam, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work examines thermally radiative Casson nanofluid flow through the variable porous space between two circular disks. The flow is subjected to the micro-rotation theory with effects of magnetohydrodynamics in the normal direction of the flow system. The governing equations are first solved using the bvp4c numerical solver to generate accurate reference solutions. These solutions subsequently serve as the foundational training dataset for the artificial neural network (ANN), providing a reliable basis for model training, prediction, and performance validation. This work shows that error histograms and regression plots demonstrate excellent agreement between predicted and target values, confirming high accuracy, strong generalization capability, and the absence of overfitting. The performance analysis achieves optimal validation errors of 1.521 × 10⁻⁸, 2.184 × 10⁻⁸, and 1.3366 × 10⁻⁸ for the three cases, confirming rapid convergence and outstanding predictive performance. Axial velocity and micro-rotational velocity decline with growth in magnetic parameter and Casson parameter, while augmented with a surge in Reynolds number. Thermal profiles augment with growth in Reynolds number, radiation parameter, magnetic factor, and heat source parameter. Current work is validated through comparative analysis of current results with an established dataset. This study signifies the advanced thermal management strategies for high-speed rotating machinery, including turbines and electric motors, by harnessing the combined effects of magnetic fields and nanoparticles to precisely regulate fluid flow and heat transfer. The proposed approach contributes to enhanced cooling performance, optimized lubrication systems, improved energy efficiency, and the design of high-performance biomedical devices, thereby offering significant potential for next-generation thermal engineering and industrial applications.

Indexed as

Circular disksMagnetohydrodynamicsMicropolar fluidNanofluidThermal radiationVariable porous surface

Identifiers

PMID42804028
PMCPMC13620054

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Registered trials

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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.