Unsupervised machine learning solutions for electroosmotically driven Casson hybrid nanofluid flow using sigmoid and Fibonacci neural networks: a biomedical approach.

IF 1.5 4区 生物学 Q3 BIOLOGY
Arshad Riaz, Humaira Yasmin, Muhammad Naeem Aslam, Safia Akram, Sami Ullah Khan, Emad E Mahmoud
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引用次数: 0

Abstract

This work investigates the electroosmotic peristaltic transport of a Casson (blood)-based hybrid nanofluid Fe2O3-Cu via an asymmetric channel embedded inside a porous medium. The model takes into consideration electric and magnetic field effects, Ohmic heating, as well as velocity and thermal slip conditions. The governing equations are simplified and solved by employing unsupervised sigmoid-based neural networks (SNNs), Fibonacci-based neural networks (FNNs), and their hybrid model (FSNNs) under the assumptions of low Reynolds number and long wavelength. Furthermore, a comparative analysis is conducted among SNNs, FNNs, and FSNNs to evaluate their performance. The results reveal that the FSNNs demonstrate superior accuracy and stability compared to the other models. The results show that the temperature rises with larger values of the Grashof number, Brinkman number, and heat source/sink parameter, while lowers with higher values of Casson parameter, porosity factor, and velocity slip parameter. The pressure gradient grows with increasing Gr, ϱ, and Uhs, but decreases as Hartmann number increases. This study sheds light on the design of efficient microfluidic, biomedical, and thermal management systems, emphasizing the role of electromagnetic modulation and hybrid nanofluids in improving performance and control.

使用s型和斐波那契神经网络的电渗透驱动卡森混合纳米流体流动的无监督机器学习解决方案:生物医学方法。
本研究研究了基于卡森(血液)的混合纳米流体Fe2O3-Cu通过嵌入在多孔介质中的不对称通道的电渗透蠕动运输。该模型考虑了电场和磁场效应、欧姆加热以及速度和热滑移条件。采用基于无监督s型神经网络(snn)、基于斐波那契神经网络(fnn)及其混合模型(fsnn)在低雷诺数和长波长假设下对控制方程进行了简化和求解。此外,对snn、fnn和fsnn进行了比较分析,以评估它们的性能。结果表明,与其他模型相比,fsnn具有更高的精度和稳定性。结果表明:Grashof数、Brinkman数和热源/热源参数值越大,温度越高;Casson参数、孔隙度因子和速度滑移参数值越大,温度越低;压力梯度随Gr、ϱ和Uhs的增大而增大,随Hartmann数的增大而减小。本研究阐明了高效微流体、生物医学和热管理系统的设计,强调了电磁调制和混合纳米流体在改善性能和控制方面的作用。
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来源期刊
CiteScore
3.60
自引率
11.80%
发文量
33
审稿时长
>12 weeks
期刊介绍: Aims & Scope: Electromagnetic Biology and Medicine, publishes peer-reviewed research articles on the biological effects and medical applications of non-ionizing electromagnetic fields (from extremely-low frequency to radiofrequency). Topic examples include in vitro and in vivo studies, epidemiological investigation, mechanism and mode of interaction between non-ionizing electromagnetic fields and biological systems. In addition to publishing original articles, the journal also publishes meeting summaries and reports, and reviews on selected topics.
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