Artificial intelligence computing of blood based nanofluid transport mechanism under thermal radiation influence.
8 affiliations
- 1Department of Mathematics, Mohi-ud-Din Islamic University, Nerian Sharif, AJ&K, 12080, Pakistan. [email protected].
- 2Department of Mathematics, Ambo University, Ambo, Ethiopia. [email protected].
- 3Department of Electrical Engineering, Faculty of Engineering, Islamic University of Madinah, Madinah, 42351, Saudi Arabia.
- 4Faculty of Technical Education, Hourani Center for Applied Scientific Research (HCASR), Al-Ahliyya Amman University, Amman, Jordan.
- 5Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
- 6Department of Basic Sciences, Virtual University, Wah Campus, Wah, Pakistan.
- 7Department of Mathematics, University of Hafr Al Batin, Hafr Al Batin, 31991, Saudi Arabia.
- 8Department of Mechanical Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia. [email protected].
Abstract
Bionanofluids along a three-dimensional wedge play a significant role in biomedical and engineering applications, including targeted drug delivery and thermal therapy, because they have improved thermal characteristics. The wedge geometry causes sophisticated flow dynamics, enhancing mixing and efficiency in nanofluid based systems, which is important for optimizing medical and industrial process performance. An investigation of bionanofluids along a 3D wedge facilitates the understanding of boundary layer characteristics, heat transfer processes, and nanoparticle distribution, contributing to improved biofluidic device design. Hence, the problem is formulated to analyze the bionanofluid flow through 3D wedge. The basic laws, transformations and supporting bionanofluid characteristics used in the formation leads to nonlinear model. The influence of parameters is than analyzed through ANN (artificial neural network) based LMBPS (Levenberg Marquardt backpropagation scheme) using 15-neurons. The computational output for Nusselt number shown that the depreciation for [Formula: see text] and [Formula: see text]. The velocities [Formula: see text] and [Formula: see text] enhanced for [Formula: see text] and [Formula: see text]. Further, improved Nusselt number computed for [Formula: see text] and [Formula: see text] from [Formula: see text] to [Formula: see text], [Formula: see text] to [Formula: see text] and [Formula: see text] to [Formula: see text]. The results authenticity achieved through FFs (functions fitting), HGE (histogram error), and regressions and found better alignment with test, training, targets and best. The benchmark solutions for future replication of the stud also provided. Clinical trial Not applicable.
Related
- Thermal convective and flow dynamics of blood trihybrid nanofluid inside a stenosis artery for cardiovascular disease treatment using artificial intelligence approach.
- Artificial intelligent computing of hybrid nano suspension for enhanced thermal mechanism in Jeffery Hamel configuration.
- Unsteady MHD transport of a tangent-hyperbolic ternary hybrid nanofluid over a permeable stretching surface with variable thermal conductivity and nonlinear radiation
- Artificial Intelligence-Driven Neural Computing for Navigation Decision Classification in Autonomous Vehicles.
- 太极统一场论(TUFT)在人工智能领域的工程落地:基于阴阳破缺机制的新型大语言模型推理架构 Taiji Unified Field Theory (TUFT) in Artificial Intelligence: A Novel LLM Reasoning Architecture Based on Yin-Yang Symmetry Breaking Mechanism
BibTeX
@article{adnan2026artificial,
title = {{Artificial intelligence computing of blood based nanofluid transport mechanism under thermal radiation influence.}},
author = {Adnan and Dase Fayera Hule and Mohamed Arbi Khlifi and Rashed O Abu Hammour and Heba G Mohamed and Aniqa Bashir and Yasir Khan and Muhammad Nasir Bashir},
journal = {Discover nano},
year = {2026},
month = oct,
doi = {10.1186/s11671-026-05035-1},
url = {https://europepmc.org/article/MED/42905358},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial intelligence computing of blood based nanofluid transport mechanism under thermal radiation influence.
AU - Adnan
AU - Dase Fayera Hule
AU - Mohamed Arbi Khlifi
AU - Rashed O Abu Hammour
AU - Heba G Mohamed
AU - Aniqa Bashir
AU - Yasir Khan
AU - Muhammad Nasir Bashir
PY - 2026
DA - 2026/10/21
JO - Discover nano
DO - 10.1186/s11671-026-05035-1
UR - https://europepmc.org/article/MED/42905358
AB - Bionanofluids along a three-dimensional wedge play a significant role in biomedical and engineering applications, including targeted drug delivery and thermal therapy, because they have improved thermal characteristics. The wedge geometry causes sophisticated flow dynamics, enhancing mixing and efficiency in nanofluid based systems, which is important for optimizing medical and industrial process performance. An investigation of bionanofluids along a 3D wedge facilitates the understanding of boundary layer characteristics, heat transfer processes, and nanoparticle distribution, contributing to improved biofluidic device design. Hence, the problem is formulated to analyze the bionanofluid flow through 3D wedge. The basic laws, transformations and supporting bionanofluid characteristics used in the formation leads to nonlinear model. The influence of parameters is than analyzed through ANN (artificial neural network) based LMBPS (Levenberg Marquardt backpropagation scheme) using 15-neurons. The computational output for Nusselt number shown that the depreciation for [Formula: see text] and [Formula: see text]. The velocities [Formula: see text] and [Formula: see text] enhanced for [Formula: see text] and [Formula: see text]. Further, improved Nusselt number computed for [Formula: see text] and [Formula: see text] from [Formula: see text] to [Formula: see text], [Formula: see text] to [Formula: see text] and [Formula: see text] to [Formula: see text]. The results authenticity achieved through FFs (functions fitting), HGE (histogram error), and regressions and found better alignment with test, training, targets and best. The benchmark solutions for future replication of the stud also provided. Clinical trial Not applicable.
SN - 2731-9229
ER -