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Micro/Nano Heat Transfer
Published in Sadık Kakaç, Hongtan Liu, Anchasa Pramuanjaroenkij, Heat Exchangers, 2020
Sadık Kakaç, Hongtan Liu, Anchasa Pramuanjaroenkij
Brownian motion is the random motion of particles suspended in a fluid. When nanofluids are considered, this random motion transports energy directly by nanoparticles. In addition, a microconvection effect, which is due to the fluid mixing around nanoparticles, is also proposed to be important. Brownian dynamics simulation is used to determine the effective thermal conductivity of nanofluids90 by considering the Brownian motion of the nanoparticles.
Nanomedicine: Could It Be a Boon for Pulmonary Fungal Infections?
Published in Sarwar Beg, Mahfoozur Rahman, Md. Abul Barkat, Farhan J. Ahmad, Nanomedicine for the Treatment of Disease, 2019
Biswajit Mukherjee, Ashique Al Hoque, Shreyasi Chakraborty, Leena Kumari, Somdatta Roy, Paramita Paul
Apart from impaction and sedimentation, Brownian motion plays a major role in the deeper alveolar areas of the lungs. Brownian motion is the random microscopic motion of small particles due to the numerous random collisions by gas molecules. In the small airways where the distance is short and residence time is long, diffusion is an important mechanism for the deposition of small particles (<0.5 μm). Macroscopically, we see the overall movement of particles from a higher concentration region (i.e., the center of air stream) to a lower concentration region (i.e., the airway wall). Since it is caused by gas molecule collisions, the effectiveness of this mechanism increases as particle size decreases. The Brownian motion of the surrounding molecules of the aqueous lung surfactant causes a random movement of the particles. Upon contact with the lung surfactant, the dissolution of the drug in alveolar fluid is essential for diffusion. Additionally, the concentration gradient also influences the diffusion process. Particles smaller than 1 to 0.5 μm are deposited in the alveolar region, while most of the particles, owing to smaller sizes, are exhaled.
Particle Transport and Entrainment during Reactor Accidents
Published in Robert E. Masterson, Nuclear Reactor Thermal Hydraulics, 2019
Brownian motion was first discovered by Englishman Robert Brown (see Figure 28.3) in 1827 when he observed the motion of grains of pollen under a microscope in what appeared to be a drop of stagnant water. He could not explain why the grains of pollen moved when the water was standing still. However, later Einstein and others provided an elegant mathematical explanation of why this occurred. This molecular view of diffusion is very popular among physicists and mathematicians because classical mechanics can be used to describe the motion of the particles and the rate at which they diffuse. Figure 28.4 illustrates this process using a concentration gradient. Suppose that, we have a glass of stagnant water and decide to add some particles to it. If some of the particles are dissolved in the water, the particles will initially reside in only one part of the glass. Then over a period of time, the particles will randomly move around, and they will diffuse in different directions. Eventually, over a longer period of time called the diffusion time, they will eventually become distributed randomly and uniformly throughout the glass of water. The rate at which this diffusion occurs depends on the temperature of the water in the glass, and it can be expressed mathematically by what is called the diffusion coefficient.
Activation energy and entropy generation in viscous nanofluid with higher order chemically reacting species
Published in International Journal of Ambient Energy, 2022
Mlamuli Dhlamini, Hiranmoy Mondal, Precious Sibanda, Sandile Motsa
Figure 3 shows the impact of Brownian motion on the velocity, temperature and concentration profiles. Brownian motion is the random ‘indecisive’ movement of particles suspended in a fluid resulting from the collision with the fast-moving molecules of the fluid. An increase in the Brownian motion causes the momentum boundary layer to thin as shown in Figure 3(a), a result consistent with a result obtained by Shehzad et al. (2014) and Ardahaie et al. (2018). The fluid around a particle is dragged in the direction of the particle. At the same time, the motion of the particle is resisted by viscous forces in the fluid (Uma et al. 2011). The overall effect is a reduction in the velocity of the fluid. Temperature is shown to increase with increasing values of the Brownian motion as reported by Mabood, Ibrahim, and Khan (2016). Increasing the Brownian motion parameter was shown to lead to a decrease in the solute boundary layer. An increase in the Brownian motion parameter boost the movement of particles. This cause the warming of boundary layer which effectively cause nanoparicle to move away of the surfaces inside the inactive fluid. This increases the deposition of the solute particles away from the surface, leading to the reduction of the concentration (Goyal and Bhargava 2017). The results are shown in Figure 3(c). Similar results were obtained by Dhlamini et al. (2019, 2018) and Mabood, Ibrahim, and Khan (2016).
Transport and deposition of ultrafine particles in the upper tracheobronchial tree: a comparative study between approximate and realistic respiratory tract models
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Jingliang Dong, Jiang Li, Lin Tian, Jiyuan Tu
Because the airway models and inhalation exposure parameters used in present study are not consistent with the reference data, a diffusion parameter was adopted to minimise the effects due to these inter-subject variability (Cheng et al. 1995). Here, denotes the particle diffusion coefficient in cm2/s, and Q stands for the volumetric flow rate in Litre/min. The particle diffusion coefficient is a parameter to quantify the molecular diffusion effect produced by Brownian motion, = kBTCs/3µdp, where kB stands for the Boltzmann’s constant, µ denotes the air viscosity (1.85 × 10−5 kg/m·s), dp denotes the particle size, T denotes the absolute temperature (310 K in present study), Csstands for the Cunningham slip correction factor. Therefore, overall model predictions using the two four-generation tracheobronchial airway models (exclude extension tubes) and the reference data were converted regarding this diffusion parameter. All data series were plotted in Figure 6.
Homotopy analysis approach to Ferro-hydrodynamic bio-nanofluid flow over a co-axial rotating discs with Stefan blowing and magnetic dipole
Published in Numerical Heat Transfer, Part B: Fundamentals, 2023
E. Ragupathi, D. Prakash, M. Muthtamilselvan, Kyubok Ahn
Figure 7 illustrates the effect of FHD parameter on the temperature field. The FHD parameter can have a significant effect on the temperature of a bio-nanofluid system, as it influences the way that heat is transferred within the system. When the FHD parameter (B) is high, the magnetic forces between particles are strong, which can lead to increase frictional heating and enhanced thermal conductivity. This can cause the temperature of the bio-nanofluid to enhance. The impact of Hartmann number on the temperature field is demonstrated in Figure 8. The temperature profile is enhanced by increasing the values of M. Basically, the resistive power offered by Lorentz force, creates a friction between surface and nanofluid. Continuation of this frictional hindrance originates frictional heat at molecular level. Thus, temperature boosts. Figure 9 highlights the impact of Stefan blowing on the temperature field. When the Stefan blowing effect increases, the temperature field diminishes. Generally, when the Stefan blowing parameter is increased, the heat transfer coefficient between the surface and the fluid flow also increases. This can lead to a decrease in the surface temperature, as more heat is transferred away from the surface. Also, the opposite behavior is noted in the case of Figure 10 displays the effect of Nb on the temperature field. The random motion of particles due to Brownian motion leads to an increase in the kinetic energy of the particles, which in turn increases the temperature of the fluid flow system. The temperature of a nanofluid is a measure of the average kinetic energy of its molecules. The Brownian motion of particles causes an increase in the kinetic energy of the surrounding molecules, which in turn increases the temperature of the system. On the other hand, impact of Nt on the temperature field is plotted in Figure 11. Physically, a higher thermophoresis parameter indicates a stronger thermophoretic effect and can lead to significant changes in the temperature distribution of the fluid. Thus, enhancing the fluid’s temperature. Figure 12 portrays the contribution of Pr on the temperature profile. It is observed that, increases in the values of Pr cause a decrease in temperature. In terms of physics, thermal diffusivity affects the Pr. A weaker thermal diffusivity is associated with higher values of the Pr. Such a decreased thermal diffusivity causes a decrease in the temperature profile and the accompanying thermal layer thickness. On the other hand, the temperature profile is affected significantly due to enhancing the values of hall parameter. Which is seen in Figure 13.