Model Of Blood Flow Research Articles

Blood flow through a stenosed left anterior descending coronary artery: Evaluation of loss coefficients in one-dimensional fluid–structure interaction model

Coronary arterial flow is affected by conditions such as atherosclerosis and stenosis resulting in coronary artery disease. Quantifying the flow fields across arteries is a key aspect in the functional assessment of occlusive arterial disease. An essential aspect of blood flow modeling is the mechanical interaction between the fluid flow and the arterial vessel wall. The present study focuses on the modeling of blood flow within the left anterior descending artery affected with stenosis. A one-dimensional (1D) model was developed to study the transient blood flow characteristics in the artery. The 1D model is coupled with the material tube law to account for the flexibility of the arterial wall. The loss coefficients that account for the local viscous and turbulent losses across the stenosis region are estimated accurately in terms of the varying local cross-sectional area, instead of empirical constants used in the literature. It was observed that the magnitude of viscous losses decreases with an increase in the severity of stenosis. For lower degree of stenosis (<30%), the local turbulent losses are insignificant compared to the viscous losses. The maximum deformation of the vessel wall is ∼0.12mm at t=0.45s for s=70%. During the cardiac cycle (T=0.9s), the artery is observed to be experiencing dilation (Δr>0) in the upstream region, whereas contraction (Δr<0) in the downstream region for all the values of severity (s). A fractional flow reserve of 58.53% was noticed in a stenosed artery of 70% severity.

Junction conditions for one-dimensional network hemodynamic model for total cavopulmonary connection using physically informed deep learning technique

Abstract This paper presents a novel methodology utilizing physics-informed neural network (PINN) as a junction condition for a 1D network model of blood flow in total cavopulmonary connection generated by the Fontan procedure. The technique integrates a 3D mesh generation process based on the parameterization of the junction geometry, along with a sophisticated physically regularized neural network architecture. Synthetic datasets are produced using 3D steady Stokes simulations within fixed boundaries. We use a physically informed feedforward neural network that utilizes a physically regularized loss function, which incorporates the principle of mass conservation. Our PINN achieves a tolerance of 6% on the test set. We develop a 1D-PINN multiscale model based on a previously developed method for multiscale 1D–3D simulations. Comparison with 1D–3D Stokes based model and 3D Navier–Stokes based model verifies the 1D-PINN model. In the first and second comparison, the maximum deviations of the averaged pressures and flows do not exceed 1.48% and 12.26%, respectively.

Shape factor and thermal radiation effects on convective MHD flow of Casson blood ternary hybrid nano‐fluid through a stretched rotating rigid disk

AbstractThe mathematical modeling of blood flow with the incorporation of ternary hybrid nanoparticles (i.e., titania, silica, and alumina nanoparticles) is the focus of this work. The flow of blood is simulated using the Casson fluid model. The impacts of ternary hybrid nanoparticles, shape factor, and Geometry of solid nanoparticles are visualized and investigated. The momentum and thermal characteristics of a flowing liquid are determined by considering magnetization, porosity, and nonlinearized radiating thermal flux. The basic partial differential equations resulting from mathematical modeling are turned into non‐linear ordinary differential equations using suitable velocity transforms. The derived nonlinear equations are then numerically calculated using the 4th‐5th order Runge‐Kutta‐Fehlberg method with the shooting technique and analytically solved by the Adomian decomposition method (ADM). The effects of the factors involved on the dimensionless profiles produced are fully addressed. It is found that the Skin friction factor and heat transfer rate in the radial direction upsurge with the augment of both rotation parameter and nanoparticles volume fraction; however, the Skin friction factor drops in the azimuthal direction. Also, results obtained reveal an enhancement in the local Nusselt number with the upsurge in the magnitude of radiation parameter, Rd, solid nanoparticles concentration, , shape factor value, s, and disk temperature, . For validation, the outcomes of this inquiry were compared to the outcomes of the HAM‐based Mathematica software. In addition, the acquired analytical DRA data are compared to numerical RKF45 values and those given in the literature.

Computational analysis of heat transport dynamics in viscous dissipative blood flow within a cylindrical shape artery through influence of autocatalysis and magnetic field orentation

Nanofluids consisting of tetra nanoparticles are crucial in bio medical sciences due to their improved thermal transport characteristics. The advanced tailored properties of tera nanoparticles make them useful in several medical interventions, such as hyperthermia treatment, where the targeted tissue can be heated more efficiently, leading to better treatment outcomes. The current study investigates the heat transfer enhancement in a hemodynamic system using tetra nanoparticles. The physical configuration of the blood flow is assumed with in a permeable cylindrical shape stenosed artery. The model incorporates the Carreau model with inclusion of diverse factors such as, exponential space-based heat source, viscous dissipation, infinite shear rate and permeability of surface. Additionally, impact of chemical reaction (autocatalysis) and magnetohydrodynamic (MHD) consequences is also integrated into the system. The framed partial differential equations (PDEs) generated by physical problem are converted into new dimensionless form of an ordinary differential system (ODEs). Bvp4c MATLAB procedure is fetched for numerical investigation. It is observed that, velocity profile of the fluid is reduced due to intensification in inclined magnetic effect, whereas autocatalysis effect promotes the concentration of nanoparticles in blood flow mixture, which increases the temperature field of fluid. Furthermore, augmentation in the values of Wassenberg number increased the elasticity in blood which enables it to deform and stretch more readily in reaction to alterations in flow conditions and hence reduction is seen in overall blood flow rate. The results revealed the significance of these integrated factors for accurate modelling of blood flow passing through a stenosed artery, which is crucial in medical interventions.

Fractional Model for Blood Flow in a Stenosed Artery Under MHD Effect Through a Porous Medium

This paper presents a fractional approach as a substitute to the integer-order model, describing the magnetohydrodynamics (MHD) effect of blood flow in a stenosed artery. We not only obtain an analytical solution for the fractional governing equation but also present an expression for the wall-shear stress. After the successful verification of our model with established models, we carry out a two-step validation with the experimental data existing in literature. Then, we analyze the obtained plots by using the analytic expression. We exhibit how fractional model (FM) is helpful for controlling the blood flow through a stenosed artery. This study also indicates that magnetic field intensity significantly controls the flow velocity and wall-shear stress to a great extent. We also show that the external magnetic field, along with the fractional order of the governing equation, plays a pivotal role in the solution of the problem involving the treatment of stenosis. Finally, we conduct some experimental data approximation by the solution to establish the credibility of the proposed fractional-order model.

Two-phase magnetohydrodynamic blood flow through curved porous artery

Blood arteries are important part of our cardiovascular system. A comprehensive study of shape and anatomy of blood arteries allows to elucidate the dynamics of blood flow in these arteries. Typically, the arteries are a curved-tube like structure, with arterial walls exhibiting a composition of various porous layers. The current study embarks on a theoretical exploration of a two-fluid model of blood flow and heat transfer through the curved artery under an influence of a magnetic field. The artery walls are composed of Brinkman and Darcy layers. The blood flows through a curved artery exerts centrifugal forces on the arterial walls that leads to change the blood flow patterns. The significant effects of curvature along with the intensity of an applied magnetic field on the blood flow patterns, heat transfer, and resistance impedance in curved artery have been investigated in the present work. The mathematical model of the proposed work is tackled by the homotopy analysis method using physically relevant boundary and interface conditions. The significant outcome of the present work is that the heat transfer rate increases with the increase in the curvature parameter, and it reduces on raising the couple stress parameter and Hartmann number. The novelty of this work lies in the consideration blood flow and heat transfer in inner endothelial layers of curved porous artery. The result presented in this work is vital to assess the condition of atherosclerosis, aneurysms, vasculties, blood clot, etc.; beyond this, the present model can be extended for medical diagnostics, treatment planning, medical device design, drug delivery optimization, and biomedical engineering research. This study can ultimately contribute for improved patient care and outcomes in cardiovascular medicine.

The effect of flow-derived mechanical cues on the growth and morphology of platelet aggregates under low, medium, and high shear rates

Platelet aggregation is a dynamic process that can obstruct blood flow, leading to cardiovascular diseases. While many studies have demonstrated clear connections between shear rate and platelet aggregation, the impact of flow-derived mechanical signals on this process is not fully understood. The objective of this work is to investigate the role of flow conditions on platelet aggregation dynamics, including effects on growth, shape, density composition, and their potential correlation with binding processes that are characterised by longer (e.g., via αIIbβ3 integrin) and shorter (e.g., via VWF) initial binding times. In vitro blood perfusion experiments were conducted at wall shear rates of 800, 1600 and 4000 s-1. Detailed analysis of two modalities of experimental images was performed to offer insights into the morphology of platelet aggregates. A consistent structural pattern was observed across all samples: a high-density core enveloped by a low-density outer shell. An image-based 3D computational blood flow model was subsequently employed to study the local flow conditions, including binding availability time and flow-derived mechanical signals via shear rate and rate of elongation. The results show substantial dependence of the aggregation dynamics on these flow parameters. We found that the different binding mechanisms that prefer different flow regimes do not have a monotonic cross-over in efficiency as the flow increases. There is a significant dip in the cumulative aggregation potential in-between the preferred regimes. The results suggest that treatments targeting the biomechanical pathways could benefit from creating conditions that exploit these low-efficiency zones of aggregation.

Particle-based modeling and GPU-accelerated simulation of cellular blood flow

Computational modeling and simulation of cellular blood flow is highly desired for understanding blood microcirculation and blood-related diseases such as thrombosis and tumor, but it remains a challenging task primarily because blood in microvessels should be described as a dense suspension of different types of deformable cells. The focus of the present work is on the development of a particle-based and GPU-accelerated numerical method that is able to quickly simulate the various behaviors of deformable cells in three-dimensional arbitrarily complex geometries. We employ a two-fluid model to describe blood flow, incorporating the deformation and aggregation of cells. A smoothed dissipative particle dynamics is used to solve the two-fluid model, and a discrete microstructure model is applied for the cell deformation, as well as a Morse potential model for the cell aggregation. The heterogeneous CPU-GPU environment is established, where each GPU thread is dedicated to a particle, and the CPU is mainly responsible for loading and exporting data. Five test cases are conducted against analytical theory, experimental data, and previous numerical results, for pure fluid, cell deformation, cell aggregation, cell suspension and the cellular flow in a complex network, respectively. It is shown that the methodology can accurately predict various behaviors of cells, and the GPU is well suited for particle-based modeling. Especially for cellular blood flow, where calculating cellular forces is a compute-intensive and time-consuming task, the GPU offers exceptional parallel capabilities, significantly enhancing the simulation efficiency. The speedup is about 3.5 times faster than the CPU parallelization with 96 cores for the pure fluid, and this acceleration nearly reaches 20 times when cells are included in the simulations. Particularly, the calculations for deformation and aggregation forces demonstrate a substantial speedup, achieving the improvements of up to 120 and 640 times, respectively, compared to their serial counterparts. The present methodology can effectively integrate various behaviors of cells, and has the potential in simulating very large microvascular networks at organ levels.

Numerical simulating the blood flow model via nonhomogeneous Riemann solver scheme

This paper examines a one-dimensional (1D) model that appears in arterial blood flow. The mathematical model for blood flow via arteries is similar to that of unstable incompressible flows in thin-walled collapsible tubes. We present the Riemann invariants of the suggested model, which is one of the fundamental components of this work. For numerical modeling of blood flow model, we present a nonhomogeneous Riemann solver (NHRS) technique. Next, we demonstrate the simulation of how pressure, velocity, and cross section area waveforms propagate through arteries. Specifically, we present numerical test cases with various initial data sets. In addition, we compare the NHRS scheme to the classic Rusanov, Lax–Friedrichs, and Roe schemes. Theoretical models for thin-walled collapsible tubes are applicable to a wide range of physiological events and may be used to build clinical devices for actual biomedical science. The NHRS method’s accuracy and efficiency are demonstrated by the numerical tests.

Interactions of stationary wave with rarefaction wave and shock wave for a blood flow model in arteries

Interactions of stationary wave with rarefaction wave and shock wave for a blood flow model in arteries

A comprehensive MRI-based computational model of blood flow in compliant aorta using radial basis function interpolation

BackgroundProperly understanding the origin and progression of the thoracic aortic aneurysm (TAA) can help prevent its growth and rupture. For a better understanding of this pathogenesis, the aortic blood flow has to be studied and interpreted in great detail. We can obtain detailed aortic blood flow information using magnetic resonance imaging (MRI) based computational fluid dynamics (CFD) with a prescribed motion of the aortic wall.MethodsWe performed two different types of simulations—static (rigid wall) and dynamic (moving wall) for healthy control and a patient with a TAA. For the latter, we have developed a novel morphing approach based on the radial basis function (RBF) interpolation of the segmented 4D-flow MRI geometries at different time instants. Additionally, we have applied reconstructed 4D-flow MRI velocity profiles at the inlet with an automatic registration protocol.ResultsThe simulated RBF-based movement of the aorta matched well with the original 4D-flow MRI geometries. The wall movement was most dominant in the ascending aorta, accompanied by the highest variation of the blood flow patterns. The resulting data indicated significant differences between the dynamic and static simulations, with a relative difference for the patient of 7.47±14.18% in time-averaged wall shear stress and 15.97±43.32% in the oscillatory shear index (for the whole domain).ConclusionsIn conclusion, the RBF-based morphing approach proved to be numerically accurate and computationally efficient in capturing complex kinematics of the aorta, as validated by 4D-flow MRI. We recommend this approach for future use in MRI-based CFD simulations in broad population studies. Performing these would bring a better understanding of the onset and growth of TAA.

Machine learning based analysis of entropy production and wall shear stress in blood flow through stenosed artery

The building up of cholesterol and various substances on the inner boundary of arterial walls forms a plaque (stenosis), which results in the narrowing of walls of arteries, and this exerts additional stress on the walls. Shear stress at the wall of arteries may cause the formation of plaque, plaque growth, and expansive arterial remodeling, and it may also result in an increased risk of plaque rupture. The purpose of this research work is to use machine learning approaches to analyze the wall shear stress and entropy generation through irreversible processes of blood flow in a porous stenosed artery within the context of magnetohydrodynamic consideration. As a way of illustrating blood’s non-Newtonian behavior, the Casson fluid model is used. A blood flow model in the context of mild stenosis has been developed, and it has subsequently been computationally addressed using an explicit finite difference method. The velocity, temperature, entropy generation, Bejan number, and wall shear stress are visualized for different values of pertinent variables. The result reflects the increased velocity in the stenosed region. A hybrid machine learning method using artificial neural network and particle swarm optimization is implemented to optimize the entropy generation and wall shear stress. The network is trained using Stochastic gradient–descent training procedure as well as Levenberg–Marquardt training procedure, and the outcomes of both training procedures are compared. Using this machine learning process, the minimum entropy production has been found for specific values of the Hartmann number, dimensionless temperature difference, and pulsatile component of pressure gradient. This machine learning-based optimization approach enhances the ability to forecast quantitative changes in affecting parameters, which will contribute to minimized damage to the arterial wall, improved blood flow efficiency and the delivery of necessary components to tissues.

Flow Analysis of Central Venous Outflow Tract: A New Approach to Understanding Pulse-Synchronous Tinnitus.

Pulse-synchronous tinnitus (PST) has been linked to multiple anatomical variants of the central venous outflow tract (CVOT) including sigmoid sinus (SS) dehiscence and diverticulum. This study investigates flow turbulence, pressure, and wall shear stress along the CVOT and proposes a mechanism that results in SS dehiscence and PST. Case series. Tertiary Academic Center. Venous models were reconstructed from computed tomography scans of 3 patients with unilateral PST. Two models for each patient are obtained: a symptomatic and contralateral asymptomatic side. A turbulent model-enabled commercial flow solver was used to simulate the pulsatile blood flow over the cardiac cycle through the models. Fluid flow through the transverse and SS junction was analyzed to observe the velocity, pressure, turbulent kinetic energy (TKE), and shear stress over a simulated cardiac cycle. Fluid flow on the symptomatic side showed increased vorticity in the presence of an SS diverticulum. Higher TKE with periodicity following the cardiac cycle was observed on the symptomatic side, and a sharp increase was observed if SS diverticulum was present. Shear stress was highest near the narrowest segments of the vessel. Pressure was observed to be lower on the symptomatic side at the transverse-SS junction for all 3 patients. Computational fluid dynamics modeling of blood flow through the CVOT in PST suggests that low pressure may be the cause of dehiscence, and tinnitus may result from periodic increases in TKE.

Identifying hemodynamic factors associated with the rupture of anterior communicating artery aneurysms based on global modeling of blood flow in the cerebral artery network

Anterior communicating artery (ACoA) aneurysms are more prone to rupture compared to aneurysms present in other cerebral arteries. We hypothesize that systemic blood flow in the cerebral artery network plays an important role in shaping intra-aneurysmal hemodynamic environment thereby affecting the rupture risk of ACoA aneurysms. The majority of existing numerical studies in this field employed local modeling methods where the physical boundaries of a model are confined to the aneurysm region, which, though having the benefit of reducing computational cost, may compromise the physiological fidelity of numerical results due to insufficient account of systemic cerebral arterial hemodynamics. In the present study, we firstly carried out numerical experiments to address the difference between the outcomes of local and global modeling methods, demonstrating that local modeling confined to the aneurysm region results in inaccurate predictions of hemodynamic parameters compared with global modeling of the ACoA aneurysm as part of the cerebral artery network. Motivated by this finding, we built global hemodynamic models for 40 ACoA aneurysms (including 20 ruptured and 20 unruptured ones) based on medical image data. Statistical analysis of the computed hemodynamic data revealed that maximum wall shear stress (WSS), minimum WSS divergence, and maximum WSS gradient differed significantly between the ruptured and unruptured ACoA aneurysms. Optimal threshold values of high/low WSS metrics were determined through a series of statistical tests. In the meantime, some morphological parameters of aneurysms, such as large nonsphericity index, aspect ratio, and bottleneck factor, were found to be associated closely with aneurysm rupture. Furthermore, multivariate logistic regression analyses were performed to derive models combining hemodynamic and morphological parameters for discriminating the rupture status of aneurysms. The capability of the models in rupture status discrimination was high, with the area under the receiver operating characteristic curve reaching up to 0.9. The findings of the study suggest that global modeling of the cerebral artery network is essential for reliable quantification of hemodynamics in ACoA aneurysms, disturbed WSS and irregular aneurysm morphology are associated closely with aneurysm rupture, and multivariate models integrating hemodynamic and morphological parameters have high potential for assessing the rupture risk of ACoA aneurysms.

A Mathematical Analysis and Simulation of the F-L Effect in Two-Layered Blood Flow through the Capillaries Remote from the Heart and Proximate to Human Tissue

In this paper, we have provided a mathematical analysis of an empirical result, namely, the Fahraeus–Lindqvist effect, a phenomenon that occurs in capillary tubes with a diameter lower than 0.3 mm. According to this effect, in capillary tubes under 0.3 mm in diameter, the apparent viscosity of blood decreases as the diameter of the tube decreases, making flow possible in these vessels. A two-phase blood flow mathematical model for human capillaries has been presented here. According to Haynes’ theory, blood is separated into two layers when it flows from the capillary. It is assumed that the first layer is plasma, and the second layer is the core layer. The plasma layer flows near the wall of the capillary, and the core layer flows along the axis of the capillary. Further, the core layer is assumed to be a mixture of two phases: one is the plasma, and the other is that of RBCs. For mathematical modeling purposes, a curvilinear coordinate system has been adopted, with physical quantities used in tensorial form. Derived equations are solved to find the effective viscosity, which depends upon the radius of the capillary; that is, it reduces viscosity to make blood flow possible. A comparative study was conducted with the experimental result of this effect, and it was observed that the proposed two-phase blood flow model is much closer to the experimental data than the single-phase blood flow model, and both have the same trends. After validation of the model with the experimental result, this model was applied to human capillaries (diameter lower than 10 μm) to show the F-L effect, and the impact of various physiological quantities that are relevant to the flow of blood into human capillaries are also discussed here. The impact of hematocrit on various parameters has been demonstrated explicitly.

A hemodynamic study of the relationship between the left and right liver volumes and the blood flow distribution in portal vein branches.

Cirrhosis patients often exhibit clinical symptoms such as right liver atrophy, portal hypertension, spleen enlargement and increased blood supply, which exhibit considerable variation between the left and right liver sections. These differences are hypothesized to stem from disparities in blood flow within the left and right portal vein (PV) branches. However, rigorous quantitative evidence remains scarce. We mainly aim at quantitatively revealing the relationship between the blood flow rates of two PV branches and liver volumes, and providing quantitative evidence and theoretical support for the diagnosis and treatment of cirrhosis from the perspective of hemodynamics. Five cirrhotic patients and two healthy volunteers from Beijing Friendship Hospital are investigated. Their PV blood flow models are established based on computed tomography (CT) images and finite volume simulations. The volume of the left and right liver lobes are measured in 3-matic. The distributions of blood source in the PV branches are tracked by streamline analysis. The blood flow rates are quantitatively counted by integrating the blood source velocities. Linear analysis is performed to build the relationship between liver volumes and PV blood flow distributions. Streamline analysis reveals significant differences in blood distribution between the left and right PV branches. The majority of blood from the superior mesenteric vein (SMV) flowed into the right portal vein (RPV), while most blood from the splenic vein (SV) entered the left portal vein (LPV). The main PV pressure drop linearly increases with the SV blood velocity for all PV structures of patients and healthy volunteers. The flow rate ratio QRPV/QLPV demonstrates an increase in tandem with the volume ratio VR/VL, exhibiting a linear correlation with the Pearson correlation coefficient being 0.93. The differences in the blood distributions are consistent with the clinicians' knowledge and validate our simulations. We demonstrated a linear increase in PV pressure with elevated SV blood velocity. Additionally, the volumes of the left and right hepatic lobes exhibited a positive correlation with blood flow rates in the corresponding PV branches.

Simulation studies on the hemodynamic models for blood flow

Abstract One of the most important fluid flows is blood flow seen in hemodynamics, which is a vital process and carries many ingredients from one place to another in the interior of the body. Blood is a special suspension; it is a non-Newtonian fluid as the blood flow cannot be compressed due to the imbalance in strain force and velocity. The blood flow is modelled by various equations which are based on fundamental equations such as the Korteweg-De Vries (KdV) equation and the nonlinear Schrödinger type equations. In this study, some new solitary solutions of the blood flow models are obtained in explicit form via Bernoulli method which is one of the ansatz-based methods. Moreover, 3D and 2D simulations under the suitable values of the parameters of the solutions obtained are plotted.

From cine-MRI to computational models of blood flow within the heart

From cine-MRI to computational models of blood flow within the heart

Multifaceted simulation: Finite volume and finite element modeling of blood flow in multiple stenosed arteries

Cardiovascular illnesses are a primary global health concern because they are frequently brought on by arterial stenosis. The complicated hemodynamics of blood flow via elliptically shaped arteries with numerous stenotic lesions along their top and bottom walls are examined in this paper. Carreau fluid model is used with Navier–Stokes equations in this study. The complete comparative study is done by using the Finite Element and Finite Volume Methods. This study uses commercial software to examine blood flow velocity, pressure and temperature distributions under various physiological situations at Reynolds number 30. Our results illuminate the interaction between flow dynamics, stenosis characteristics, and arterial geometry. The novelty of the work is to investigate how stenosis size, shape, and location affect pressure gradients, and flow disturbances. These observations provide helpful direction for understanding disease progression, designing treatments, and possibly new stent designs. The future direction of this research may involve further exploration of the interplay between hemodynamics and arterial stenosis by incorporating advanced computational models. Additionally, studies focusing on in vivo validation and clinical applications could enhance the translational impact of the findings. Collaborations between researchers, clinicians, and engineers may pave the way for personalized treatment strategies and innovations in cardiovascular care based on a deeper understanding of the intricate dynamics within diseased arteries.

Numerical study of the parameters of a fractional derivative blood flow model in the context of superdiffusive heat transfer

AbstractThis paper explores the impact of temperature on the fractionalization of magnetic nanoparticles in blood, coupled with vibratory motion influenced by rotation. The distribution systems exhibit heightened diffusivity, explored numerically through the finite difference method and the algorithm. The temperature distribution robustly responds to elevated fractional parameters, indicating a critical threshold. The study achieves a comprehensive understanding of temperature and velocity evolution in different tube zones. In comparison, single‐walled carbon nanotubes surpass multiple‐walled carbon nanotubes in distributions, while CuO nanoparticles demonstrate larger distributions at an average fractional‐order parameter of . In the observed growth region at and exhibit noteworthy temperature distributions, highlighting the fractional derivative's impact in highly diffusive models with nanoparticles. It is also noted that in this region, the temperature distribution tends to decrease for all the parameters and values examined, particularly at a low Reynolds number (). However, the introduction of nanoparticles accelerates the processes and distributions across the various observed zones. Furthermore, the accelerated behavior of each nanoparticle can be moderated based on its sphericity. By encompassing all facets of fractional order for controlled rotations, the study sheds light on the role of magnetized nanoparticles in blood dynamics, emphasizing the significance of a critical zone where certain physicochemical properties are disrupted, potentially leading to cellular disorders, and the fluidodynamic effects of vortex flow. This perspective is pivotal for addressing tissue lesions induced by vibrations, as seen in the case of coagulations, and for targeting carcinogenic areas using nanoelements.