scholarly journals Numerical Prediction of Microbubble Attachment in Biological Flows

2016 ◽  
Vol 13 (1) ◽  
Author(s):  
Joshua Gosney ◽  
Jeffrey Heys

Biofilm infections pose a major threat to human health and are difficult to detect. Microbubbles provide an effective and inexpensive method of detection for biofilm-based infections and other diseases such as cancer. The approach studied here examines the potential of targeted microbubbles, with specific antibodies covalently linked to their surfaces for use as ultrasound contrast agents and drug delivery vehicle. This work presents a novel numerical model for estimating the forces on microbubble conjugates in the vascular system. A full computational fluid dynamics simulation of biological fluid flow and the resulting forces on attached microbubbles is presented as well as comparisons with simplified analytical models. Both the computational and analytical predictions are compared with experimental measurements from Takalkar et al. and Schmidt et al., and these comparisons indicate stable microbubble attachment can be anticipated when the total hydrodynamic force on the microbubble is less than 100 pN. Through the examination of typical biological flows, microbubble attachment can be expected up to an average fluid velocity of 0.025 cm/s near the microbubble (i.e., a particle Reynolds number on the order of .001). The Stokes drag law was shown to predict the drag force (the dominant force) on the microbubble within an order of magnitude of the force predicted by the numerical model. Finally, it was found that the lift force on a microbubble was small relative to the drag force, and that the Saffman equation prediction differed from the numerical model by more than an order of magnitude for the biological flows examined. KEYWORDS: Microbubble Attachment; Ultrasound Contrast Agent; Hydrodynamic Force; Computational Fluid Dynamics

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Rim Farjallah ◽  
Monia Chaabane ◽  
Hatem Mhiri ◽  
Philippe Bournot ◽  
Hatem Dhaouadi

In this paper, we propose a numerical study of a tubular solar collector with a U-tube. A three-dimensional numerical model is developed. It was first used in order to study the efficiency of the solar collector and to evaluate the validity of the developed computational fluid dynamics (CFD) model by comparison with experimental results from the literature. For the numerical simulations, the turbulence and the radiation were, respectively, modeled using the standard k–ε model and the discrete ordinates (DO) model. This numerical model was then used to carry out a parametrical study and to discuss the effect of selected operating parameters such as the fluid mass flow rate, the absorber selectivity, and the material properties. Numerical results show that with the increase of the working fluid flow rate from 0.001 kg/s to 0.003 kg/s, the efficiency of the solar collector is improved (from 30% to 35%). Numerical results also show that the filled-type evacuated tube with graphite presents a best result in comparison with those found using the copper fin tube (η increases from 54% to 64%). Finally, we noted that the use of a high selective absorber surface adds to better performance in comparison with the black absorber tube. This is mainly due to the radiation losses reduction.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
David R. Rutkowski ◽  
Alejandro Roldán-Alzate ◽  
Kevin M. Johnson

AbstractBlood flow metrics obtained with four-dimensional (4D) flow phase contrast (PC) magnetic resonance imaging (MRI) can be of great value in clinical and experimental cerebrovascular analysis. However, limitations in both quantitative and qualitative analyses can result from errors inherent to PC MRI. One method that excels in creating low-error, physics-based, velocity fields is computational fluid dynamics (CFD). Augmentation of cerebral 4D flow MRI data with CFD-informed neural networks may provide a method to produce highly accurate physiological flow fields. In this preliminary study, the potential utility of such a method was demonstrated by using high resolution patient-specific CFD data to train a convolutional neural network, and then using the trained network to enhance MRI-derived velocity fields in cerebral blood vessel data sets. Through testing on simulated images, phantom data, and cerebrovascular 4D flow data from 20 patients, the trained network successfully de-noised flow images, decreased velocity error, and enhanced near-vessel-wall velocity quantification and visualization. Such image enhancement can improve experimental and clinical qualitative and quantitative cerebrovascular PC MRI analysis.


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