带五杂化纳米粒子的磁化杰弗里血流动态预测的智能模型方法。

IF 1.5 4区 生物学 Q3 BIOLOGY
Puja Paul, Sanatan Das, Poly Karmakar, Asgar Ali, Tilak Kumar Pal
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引用次数: 0

摘要

本研究提出了一种人工智能(AI)框架,用于预测导管化电动脉环中磁化五纳米粒子增强的杰弗里血流动力学。这项工作解决了多物理场相互作用的非牛顿血液流变学建模的关键空白。采用杰弗里的流体模型来封装混合了纳米颗粒的血液的非牛顿流变性能。该分析综合了影响热源、焦耳加热、界面纳米层和多孔介质阻力的多种因素。通过润滑理论和debye - h ckel线性化对流动系统进行了流线化处理,并用同伦摄动法进行了求解。使用Mathematica和Matlab工具对必不可少的流量指标进行可视化。计算结果表明,电渗透力显著改变了五杂化纳米颗粒注入血液在导管动脉几何结构中的流动模式。随着纳米层厚度的增加,导管内温度降低,轴向血压梯度升高,电渗透因子升高,壁剪应力(WSS)减弱。热传递系数(HTC)随着纳米层厚度的增加而提高。人工智能驱动的人工神经网络(ANN)模型对WSS和HTC的预测准确率达到97-100%。研究结果强调了针对患者的治疗策略的潜在改进,并通过提高非侵入性治疗的疗效和精度,为生物医学工程的更广泛领域做出了贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An intelligent model approach for dynamic prediction of magnetized Jeffrey blood flow carrying penta-hybrid nanoparticles in a catheterized electrified arterial annulus.

This research paper presents an artificial intelligence (AI) framework to predict magnetized penta-nanoparticle-enhanced Jeffrey blood flow dynamics in a catheterized electrified arterial annulus. The work addresses critical gaps in modeling non-Newtonian blood rheology with multi-physics interactions. Employing Jeffrey's fluid model to encapsulate the non-Newtonian rheological properties of blood mixed with nanoparticles. This analysis combines diverse factors influencing heat sources, Joule heating, interfacial nanolayers, and porous media drag. The flow system is streamlined via lubrication theory and Debye-Hückel linearization and then solved using homotopy perturbation method (HPM). Visualization of indispensable flow metrics is conducted using tools in Mathematica and Matlab. Computational results indicate electro-osmotic forces significantly alter the streaming patterns of penta-hybrid nanoparticle-infused blood in catheterized arterial geometry. Blood temperature lowers in the catheterized regions for the expanded thickness of nanolayer, and the axial blood pressure gradient elevates with an upsurge in the electro-osmotic factor while wall shear stress (WSS) abates. Heat transfer coefficient (HTC) improves with thicker nanolayers. AI-driven artificial neural network (ANN) model achieves 97-100% accuracy in predicting WSS and HTC. The research findings highlight potential improvements in patient-specific treatment strategies and contribute to the broader field of biomedical engineering by enhancing the efficacy and precision of non-invasive therapies.

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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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