Numerical analysis of blood flow through stenosed microvessels using a multi-phase model

Blood flow in arterioles have attracted considerable research attention due to their clinical implications. However, the fluid structure interaction between red blood cells and plasma in the blood poses formidable difficulty to the computational efforts. In this contribution, we seek to represent the red blood cells in the blood as a continuous non-Newtonian phase and construct a multi-phase model for the blood flow in microvessels. The methods are presented and validated using a channel with sudden expansion. And the resulting blood flow inside a stenosed microvessel is investigated at different inlet velocity amplitudes and hematocrits. It is show that the increase of both inlet velocity amplitude and inlet hematocrit leads to longer and thicker cell-rich layer downstream the stenosis. Besides, it is found that the maximum values of wall shear stress scales up with inlet velocity amplitudes and hematocrits. These results show the validity of the proposed computational model and provide helpful insights into blood flow behaviors inside stenosed vessels.

Analysis of sandwich graphene origami composite plate sandwiched by piezoelectric/piezomagnetic layers: A higher-order electro-magneto-elastic analysis

This work applies a higher order thickness-stretched model for the electro-elastic analysis of the composite graphene origami reinforced square plate sandwiched by the piezoelectric/piezomagnetic layers subjected to the thermal, electric, magnetic and mechanical loads. The plate is manufactured of a copper matrix reinforced with graphene origami where the effective material properties are calculated based on the micromechanical models as a function of volume fraction and folding degree of graphene origami, material properties of matrix, reinforcement, and local temperature. The governing equations are derived using the virtual work principle in terms of the bending, shear and stretching functions, in-plane displacements, electric, and magnetic potentials. The numerical results including various displacement components, maximum electric, and magnetic potentials are presented with changes of volume fraction, folding degree of reinforcement, electrical, magnetic, and thermal loading. A verification investigation is presented for approve of the methodology, and the solution procedure. The main novelty of this work is simultaneous effect of the thickness stretching and the multi-field loading on the electromagnetic bending results of the sandwich plate. Another novelty of this work is usage of graphene origami nano-reinforcement as a controllable material in a sandwich structure subjected to multi-field loadings. The results show an increase in bending, shear, and stretching deflections with an increase in electromagnetic loads, and folding degree as well as a decrease in volume fraction of reinforcement.

Numerical simulation of effect of hybrid nanofluid on heat transfer and flow of the Newtonian pulsatile blood through 3D occluded artery: Silver and gold nanoparticles

The study of blood flow in obstructed arteries is a significant focus in computational fluid dynamics, particularly in the field of biomedicine. The primary objective of this research is to investigate the impact of pulsating blood velocity on heat transfer within biological systems, with a specific focus on blood flow in obstructed arteries. To achieve this goal, a comprehensive 3D model representing a straight, constricted blood vessel has been developed. This model incorporates periodic, unsteady, Newtonian blood flow along with the presence of gold and silver nanoparticles. Leveraging the Finite Element Method (FEM), the Navier-Stokes and energy equations have been rigorously solved. Through the investigation, it is aim to shed light on how alterations in the pulsation rate and the volume fraction of nanoparticles influence both temperature distribution and velocity profiles within the system. The present study findings unequivocally highlight that the behavior of pulsatile nanofluid flow significantly impacts the velocity field and heat transfer performance. However, it is imperative to note that the extent of this influence varies depending on the specific volume fractions involved. Specifically, higher volume fractions of nanofluids correlate with elevated velocities at the center of the vessel and decreased velocities near the vessel walls. This pattern also extends to the temperature distribution and heat flux within the vessel, further underscoring the paramount importance of pulsatile flow dynamics in biomedicine and computational fluid dynamics research. Besides, results revealed that the presence of occlusion significantly affects the heat transfer and fluid flow.

Minimal number of sampling directions for robust measures of the spherical mean diffusion weighted signal: Effects of sampling directions, b-value, signal-to-noise ratio, hardware, and fitting strategy

Several recent multi-compartment diffusion MRI investigations and modeling strategies have utilized the orientationally-averaged, or spherical mean, diffusion-weighted signal to study tissue microstructure of the central nervous system. Most experimental designs sample a large number of diffusion weighted directions in order to calculate the spherical mean signal, however, sampling a subset of these directions may increase scanning efficiency and enable either a decrease in scan time or the ability to sample more diffusion weightings. Here, we aim to determine the minimum number of gradient directions needed for a robust measurement of the spherical mean signal. We used computer simulations to characterize the variation of the measured spherical mean signal as a function of the number of gradient directions, while also investigating the effects of diffusion weighting (b-value), signal-to-noise ratio (SNR), available hardware, and spherical mean fitting strategy. We then utilize empirically acquired data in the brain and spinal cord to validate simulations, showing experimental results are in good agreement with simulations. We summarize these results by providing an intuitive lookup table to facilitate the determination of the minimal number of sampling directions needed for robust spherical mean measurements, and give recommendations based on SNR and experimental conditions.

Application of radioactive particle tracking and an artificial neural network to calculating the flow rate in a two-phase (oil-water) stratified flow regime

A multiphase flow is defined as the transport of two or more fluids with different properties flowing together inside a pipeline. After offshore oil production, it is necessary to control the amount of transported fluids based on flow rate measurements. Therefore, in this study, we developed a simulation method for predicting the volume fraction and calculating the superficial velocity for a two-phase flow based on radioactive particle tracking, which involves using a sealed radiation source inside the pipeline in order to obtain volume fraction measurements. The test section for the multiphase flow comprised oil and saltwater under a stratified flow regime, with a polyvinyl chloride pipe, four NaI(Tl) detectors, and a¹³⁷Cs radioactive particle that emitted gamma-rays at 662 keV. Simulations were conducted using the MCNP6 code, which is a mathematical code based on the Monte Carlo method. Volume fraction predictions were obtained using a multilayer perceptron neural network with a backpropagation algorithm. The novel feature of this method is the combination of radioactive particle tracking with an artificial neural network in order to predict volume fractions in multiphase flows. The results showed that 91.65% of the predicted patterns were within 5% of the relative error. In addition, the time delay was determined using the cross-correlation function to obtain the superficial velocity in three different volume fractions, which allowed each phase flow rate to be calculated in these cases.

Effect of SWCNT volume fraction on the viscosity of water-based nanofluids

Nanofluids have received a great deal of interest in recent years because of their various unique features. According to the findings, the addition of nanotubes to the base materials can drastically alter their properties. In the present work, the viscosity of a typical water-based nanofluid containing single-walled carbon nanotubes is estimated using the molecular dynamics simulation for different volume fractions ranging between 0.557 and 3% at two temperatures (298 K and 313 K). The temperature of the systems is controlled using a Nose-Hoover thermostat. For calculating viscosity, the Green-Kubo equilibrium method is used. The enthalpy, potential, kinetic, and total energies are calculated to determine the system's stability. In addition, the influence of molecular mass on these energies is studied. The nanotube under investigation is an armchair(6,6)-type single-walled carbon nanotube. The results highlight the promise of the molecular dynamics simulation technique as a powerful tool in the prediction of nanofluid properties besides the experimental results. The value of viscosity will decrease as the temperature rises, much like the base fluid. Furthermore, it is shown that the viscosity is proportional to the volume fraction of water-SWCNT nanofluid. According to the results, a new viscosity relationship for volume fractions in the range of ϕ ≤ 3% is proposed. The viscosity, temperature, and volume fraction are all linked together in this equation.

Optimization of a flow regime identification system and prediction of volume fractions in three-phase systems using gamma-rays and artificial neural network

This study presents a method based on gamma-ray densitometry using only one multilayer perceptron artificial neural network (ANN) to identify flow regime and predict volume fraction of gas, water, and oil in multiphase flow, simultaneously, making the prediction independent of the flow regime. Two NaI(Tl) detectors to record the transmission and scattering beams and a source with two gamma-ray energies comprise the detection geometry. The spectra of gamma-ray recorded by both detectors were chosen as ANN input data. Stratified, homogeneous, and annular flow regimes with (5 to 95%) various volume fractions were simulated by the MCNP6 code, in order to obtain an adequate data set for training and assessing the generalization capacity of ANN. All three regimes were correctly distinguished for 98% of the investigated patterns and the volume fraction in multiphase systems was predicted with a relative error of less than 5% for the gas and water phases.

Exercising Muscle, and Na+/K+ Pumping Rates

Accurate quantification of cellular contributions to rates of substrate utilization in resting, activated, and diseased brain is essential for interpretation of data from studies using [¹⁸F]fluorodeoxyglucose-positron-emission tomography (FDG-PET) and [¹³C]glucose/magnetic resonance spectroscopy (MRS). A generally-accepted dogma is that neurons have the highest energy demands of all brain cells, and calculated neuronal rates of glucose oxidation in awake, resting brain accounts for 70-80%, with astrocytes 20-30%. However, these proportions do not take cell type volume fractions into account. To evaluate the conclusion that neuron-astrocyte glucose oxidation rates are similar when adjusted for astrocytic volume fraction (Hertz, Magn Reson Imaging 2011; 29, 1319), the present study analyzed data from 31 studies. On average, astrocytes occupy 6.1, 9.6, and 15% of tissue volume in hippocampus, cerebral cortex, and cerebellum, respectively, and regional astrocytic metabolic rates are adjusted for volume fraction by multiplying by 17.6, 11.4, and 6.8, respectively. After adjustment, astrocytic glucose oxidation rates in resting awake rat brain are 4-10 fold higher than neuronal oxidation rates. Volume-fraction adjustment also increases brain glycogen concentrations and utilization rates to be similar to or exceed exercising muscle. Ion flux calculations to evaluate sodium/potassium homeostasis during neurotransmission are not correct if astrocyte-neuron volume fractions are assumed to be equal. High rates of glucose and glycogen utilization after adjustment for volume fraction indicate that astrocytic energy demands are much greater than recognized, with most of the ATP being used for functions other than glutamate processing in the glutamate-glutamine cycle, challenging the notion that astrocytes 'feed hungry neurons'.

The comparison of different multilayer perceptron and General Regression Neural Networks for volume fraction prediction using MCNPX code

This research presents a methodology for volume fraction predictions in water-gas-oil multiphase systems based on gamma-ray densitometry and artificial neural networks. The simulated geometry uses a dual-energy gamma-ray source and dual-modality (transmitted and scattered beams). The Am-241 and Cs-137 sources and two NaI(Tl) detectors have been used in this methodology. Different data from the pulse height distribution were used to train the artificial neural network to evaluate the volume fraction prediction. The MCNPX code has been used to develop the theoretical model for stratified regime and to provide data for the artificial neural network. 5-layers feed-forward multilayer perceptron using backpropagation training algorithm and General Regression Neural Networks has been used with different designs. The artificial neural network design that presented the best results of volume fraction prediction has a mean relative error below 2.0%.

Flow regime and volume fraction identification using nuclear techniques, artificial neural networks and computational fluid dynamics

Knowledge of the flow regime and the volume fraction in multiphase flow is of fundamental importance in predicting the performance of many systems and processes. This study is based on gamma-ray pulse height distribution pattern recognition by means of an artificial neural network. The detection system uses appropriate one narrow beam geometry, comprising a gamma-ray source and a NaI(Tl) detector. The models for annular and stratified flow regimes were developed using MCNPX code, in order to obtain adequate data sets for training and testing of the artificial neural network. Several experiments were carried out in the stratified flow regime to validate the simulated results. Finally, Ansys-CFX was used as computational fluid dynamics software to simulate two different volume fractions, which were modeled and transformed in voxels and transferred to MCNPX code. The use of computational fluid dynamics is of great importance, because it brings the studies closer to the reality. All flow regimes were correctly recognized and the volume fractions were appropriately predicted with relative errors less than 1.1%.

Microstructure-based numerical computational method for the insertion torque of dental implant

The bone quality has a significant effect on the insertion torque of dental implant. In most clinical studies, bone density is used as a gold standard in predicting insertion torque. By contrast, trabecular microstructure is ignored. In this study, a microstructure-based numerical computational method with high accuracy and efficiency for the insertion torque of dental implant was proposed by introducing two microscopic variables, namely, volume fraction and fabric tensor. First, two kinds of 3D microstructural solid models with same volume fraction and fabric tensor were established on the basis of the microstructural topology of six reference specimens. Second, a new numerical simulation method based on homogenous theory was used to explore the material models of these 3D microstructural solid models at the microscopic scale. Then, the anisotropic material models of specimens were developed on the basis of the mixture rule. Thereafter, a numerical simulation based on the anisotropic finite element (FE) model was carried out to acquire the insertion torque. To demonstrate the efficiency and accuracy of the simulation based on the anisotropic FE model, numerical simulations based on isotropic FE model and micro-computer tomography (micro-CT) FE models were also implemented as comparisons. Comparison of the simulated peak insertion torques of the anisotropic, isotropic, and micro-CT FE models with insertion experiments demonstrated the feasibility and potential of the proposed method. The anisotropic FE model reduced the time consumption by 91.85% and enhanced the accuracy by 11.82% compared with the micro-CT and isotropic FE models, respectively.

Reduction of Dimensionality in Monte Carlo Simulation of Diffusion in Extracellular Space Surrounding Cubic Cells

The real-time iontophoretic method has measured volume fraction and tortuosity of the interstitial component of extracellular space in many regions and under different conditions. To interpret these data computer models of the interstitial space (ISS) of the brain are constructed by representing cells as Basic Cellular Structures (BCS) surrounded by a layer of ISS and replicating this combination to make a 3D ensemble that approximates brain tissue with a specified volume fraction. Tortuosity in such models is measured by releasing molecules of zero size into the ISS and allowing them to execute random walks in the ISS of the ensemble using a Monte Carlo algorithm. The required computational resources for such simulations may be high and here we show that in many situations the 3D problem may be reduced to a quasi-1D problem with consequent reduction in resources. We take the simplest BCS in the form of cubes and use MCell software to perform the Monte Carlo simulations but the analysis described here may be extended in principle to more complex BCS and an ISS that has a defined viscosity and an extracellular matrix that interacts with diffusing molecules. In the course of this study we found that the original analytical description of the relation between volume fraction and tortuosity for an ensemble of cubes may require a small correction.

Drag reduction and shear-induced cells migration behavior of microalgae slurry in tube flow

To optimize the designing of microalgae slurry pumping system and enhance the efficiency of microalgae products production, the flow characteristics of microalgae slurries (Chlorella pyrenoidosa) in tube flow were for the first time investigated combining experiments and numerical simulation. The drag reduction behavior of microalgae slurry in the fully developed laminar flow regime was studied. In addition, the transition Reynolds number of microalgae slurries from laminar flow to turbulent flow was about 1000-1300, which was similar to the expression of two-phase flow. To provide a further understanding of flow feature of microalgae slurries in tube, a two-phase mixture model was proposed by considering the heterogeneity of concentration due to the shear-induced microalgae cells migration behavior. Simulation results revealed that the heterogeneous distribution of concentration was affected by average velocity and volume fraction of microalgae slurries, significantly affecting the flow resistance and flow stability of microalgae slurry in the tube flow.

The capability of radial basis function to forecast the volume fractions of the annular three-phase flow of gas-oil-water

The problem of how to precisely measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the main challenges in the petroleum industry. This paper reports the capability of Radial Basis Function (RBF) in forecasting the volume fractions in a gas-oil-water multiphase system. Indeed, in the present research, the volume fractions in the annular three-phase flow are measured based on a dual energy metering system including the ¹⁵²Eu and ¹³⁷Cs and one NaI detector, and then modeled by a RBF model. Since the summation of volume fractions are constant (equal to 100%), therefore it is enough for the RBF model to forecast only two volume fractions. In this investigation, three RBF models are employed. The first model is used to forecast the oil and water volume fractions. The next one is utilized to forecast the water and gas volume fractions, and the last one to forecast the gas and oil volume fractions. In the next stage, the numerical data obtained from MCNP-X code must be introduced to the RBF models. Then, the average errors of these three models are calculated and compared. The model which has the least error is picked up as the best predictive model. Based on the results, the best RBF model, forecasts the oil and water volume fractions with the mean relative error of less than 0.5%, which indicates that the RBF model introduced in this study ensures an effective enough mechanism to forecast the results.

Application and evaluation of NODDI in the cervical spinal cord of multiple sclerosis patients

INTRODUCTION

There is a need to develop imaging methods sensitive to axonal injury in multiple sclerosis (MS), given the prominent impact of axonal pathology on disability and outcome. Advanced multi-compartmental diffusion models offer novel indices sensitive to white matter microstructure. One such model, neurite orientation dispersion and density imaging (NODDI), is sensitive to neurite morphology, providing indices of apparent volume fractions of axons (vin), isotropic water (viso) and the dispersion of fibers about a central axis (orientation dispersion index, ODI). NODDI has yet to be studied for its sensitivity to spinal cord pathology. Here, we investigate the feasibility and utility of NODDI in the cervical spinal cord of MS patients.

METHODS

NODDI was applied in the cervical spinal cord in a cohort of 8 controls and 6 MS patients. Statistical analyses were performed to test the sensitivity of NODDI-derived indices to pathology in MS (both lesion and normal appearing white matter NAWM). Diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) analysis were also performed to compare with NODDI.

RESULTS

A decrease in NODDI-derived vin was observed at the site of the lesion (p < 0.01), whereas a global increase in ODI was seen throughout white matter (p < 0.001). DKI-derived mean kurtosis (MK) and radial kurtosis (RK) and DTI-derived fractional anisotropy (FA) and radial diffusivity (RD) were all significantly different in MS patients (p < 0.02), however NODDI provided higher contrast between NAWM and lesion in all MS patients.

CONCLUSION

NODDI provides unique contrast that is not available with DKI or DTI, enabling improved characterization of the spinal cord in MS.

Mechanical and structural characterization of discontinuous fiber-reinforced dental resin composite

OBJECTIVES

This study evaluated several fiber- and matrix related factors and investigated different mechanical properties of discontinuous i.e. short fiber-reinforced composite (SFRC) (everX Posterior, eXP). These were compared with three conventional composites, microfilled G-ænial Anterior (GA), nanofilled Supreme XTE (SXTE) and bulk-fill Filtek Bulk-Fill (FBF).

METHODS

Fracture toughness (KIC), flexural strength (FS), flexural modulus (FM), compressive strength (CS), diametral tensile strength (DTS), apparent horizontal shear strength (AHSS) and fracture work (Wf) were determined for each composite (n=8) stored dry or in water. SEM analysis of the fiber diameter (df) (n=6) and orientation (n=6) were performed. The theoretical critical fiber length (lfc) and the aspect ratio (l/d) of SFRC were calculated and the volume fraction of discontinuous fibers (Vf%) and the fiber length (lf) of SFRC were evaluated. The results were statistically analyzed with two-way ANOVA (α=0.05).

RESULTS

The mechanical properties of SFRC (eXP) were generally superior (p<0.05) compared with conventional composites. GA had the highest FM (p>0.05), whereas FBF had the highest AHSS (p<0.05). The fiber related properties Vf%, l/d, lf, lfc and df of eXP were 7.2%, 18-112, 0.3-1.9mm, 0.85-1.09mm and 17μm respectively. SEM results suggested an explanation to several toughening mechanisms provided by the discontinuous fibers, which were shown to arrest crack propagation and enable a ductile fracture. Water exposure weakened the mechanical properties regardless of material type. Wf was unaffected by the water storage.

CONCLUSION

The properties of this high aspect ratio SFRC were dependent on the fiber geometry (length and orientation) and matrix ductility.

CLINICAL SIGNIFICANCE

The simultaneous actions of the toughening mechanisms provided by the short fibers accounted for the enhanced toughness of this SFRC, which toughness value matched the toughness of dentin. Hence, it could yield an inherently uniform distribution of stresses to the hard biological tissues.

MRI contrast agent concentration and tumor interstitial fluid pressure

The present work describes the relationship between tumor interstitial fluid pressure (TIFP) and the concentration of contrast agent for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We predict the spatial distribution of TIFP based on that of contrast agent concentration. We also discuss the cases for estimating tumor interstitial volume fraction (void fraction or porosity of porous medium), ve, and contrast volume transfer constant, K(trans), by measuring the ratio of contrast agent concentration in tissue to that in plasma. A linear fluid velocity distribution may reflect a quadratic function of TIFP distribution and lead to a practical method for TIFP estimation. To calculate TIFP, the parameters or variables should preferably be measured along the direction of the linear fluid velocity (this is in the same direction as the gray value distribution of the image, which is also linear). This method may simplify the calculation for estimating TIFP.

Two-Phase Acto-Cytosolic Fluid Flow in a Moving Keratocyte: A 2D Continuum Model

The F-actin network and cytosol in the lamellipodia of crawling cells flow in a centripetal pattern and spout-like form, respectively. We have numerically studied this two-phase flow in the realistic geometry of a moving keratocyte. Cytosol has been treated as a low viscosity Newtonian fluid flowing through the high viscosity porous medium of F-actin network. Other involved phenomena including myosin activity, adhesion friction, and interphase interaction are also discussed to provide an overall view of this problem. Adopting a two-phase coupled model by myosin concentration, we have found new accurate perspectives of acto-cytosolic flow and pressure fields, myosin distribution, as well as the distribution of effective forces across the lamellipodia of a keratocyte with stationary shape. The order of magnitude method is also used to determine the contribution of forces in the internal dynamics of lamellipodia.

Mapping mean axon diameter and axonal volume fraction by MRI using temporal diffusion spectroscopy

Mapping mean axon diameter and intra-axonal volume fraction may have significant clinical potential because nerve conduction velocity is directly dependent on axon diameter, and several neurodegenerative diseases affect axons of specific sizes and alter axon counts. Diffusion-weighted MRI methods based on the pulsed gradient spin echo (PGSE) sequence have been reported to be able to assess axon diameter and volume fraction non-invasively. However, due to the relatively long diffusion times used, e.g. >20ms, the sensitivity to small axons (diameter<2μm) is low, and the derived mean axon diameter has been reported to be overestimated. In the current study, oscillating gradient spin echo (OGSE) diffusion sequences with variable frequency gradients were used to assess rat spinal white matter tracts with relatively short effective diffusion times (1-5ms). In contrast to previous PGSE-based methods, the extra-axonal diffusion cannot be modeled as hindered (Gaussian) diffusion when short diffusion times are used. Appropriate frequency-dependent rates are therefore incorporated into our analysis and validated by histology-based computer simulation of water diffusion. OGSE data were analyzed to derive mean axon diameters and intra-axonal volume fractions of rat spinal white matter tracts (mean axon diameter of ~1.27-5.54μm). The estimated values were in good agreement with histology, including the small axon diameters (<2.5μm). This study establishes a framework for the quantification of nerve morphology using the OGSE method with high sensitivity to small axons.

Distribution of orientation of smooth muscle bundles does not change along human great and small varicose veins

Wall remodeling in varicose veins is associated with hypertrophy of subendothelial tissue, increase in inner diameter, wrinkling and invagination of the endothelial layer. Due to structural alterations of the wall, the smooth muscle cells (SMCs) change their original circular and longitudinal orientations. Our aim was to quantify the volume fraction of circularly, longitudinally and obliquely oriented SMCs within both the inner and outer half of the wall of 11 great saphenous varicose veins and five small saphenous varicose veins. Using stereological methods applied on cross-sections of the vessels regularly gained each 5 cm along the vessel we determined the wall thickness (846 ± 319 μm, mean ± standard deviation), the volume fraction of circular SMCs in the inner (0.19 ± 0.13) and outer (0.06 ± 0.06) layers, the volume fraction of longitudinal SMCs in the inner (0.06 ± 0.05) and outer (0.05 ± 0.04) layers, the volume fraction of oblique SMCs in the inner (0.15 ± 0.08) and outer (0.09 ± 0.08) layers, and the total volume fraction of SMCs in the inner (0.4 ± 0.1) and outer (0.21 ± 0.09) layers. The volume fraction of SMCs with circular and oblique but not with longitudinal orientation was greater in the inner layer compared to the outer layer. The SMC orientation distribution was uniform along the varicose saphenous veins. With increasing wall thickness, the volume fraction of longitudinal and oblique SMC bundles increased in both layers at the expansion of circular SMC bundles. The main differences in the orientation of the SMCs in the inner and outer wall layers should be taken into account when computational modeling of varicose saphenous veins is attempted.