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Temperature and pressure dependent gas diffusion coefficient in coal: Numerical modeling and experiments
Published in Heping Xie, Jian Zhao, Pathegama Gamage Ranjith, Deep Rock Mechanics: From Research to Engineering, 2018
S.Y. Liu, C.H. Wei, W.C. Zhu, Y.J. Yu
The factors influencing gas diffusion coefficient in coal mainly include pore structure of coal, moisture content, gas pressure and gas temperature. And a large number of experiments had been carried out (Busch et al., 2004; Clarkson et al., 1999; Cui et al., 2004; Kumar et al., 2007; Mavor et al., 1990; Nandi et al., 1970; Saghafi et al., 2007; Shi et al., 2003; Siemons et al., 2007; Smith et al., 1984). Experiments carried out by Xu et al. (2015) show that the diffusion coefficient exhibit a trend of first dropping and then rising (“U” shape) with an increase in the degree of metamorphism. Experiments carried out by Pan et al. (2010) and Wang et al. (2014) show that the diffusion coefficient decreases with increasing moisture content. The study carried out by Mallikarjun Pillalamarry et al. (2011) indicates that there is a negative correlation between diffusion coefficient and gas pressure for pressures below 3.5 MPa. The study carried out by Tang et al. (2015) shows that the equivalent diffusion coefficient increases with increasing temperature. For the diffusion process under constant temperature, the diffusion coefficient is dependent on temperature and independent of adsorption equilibrium pressure.
Diffusion
Published in Gregory N. Haidemenopoulos, Physical Metallurgy, 2018
According to the above discussion, the temperature dependence of diffusion is integrated in the diffusion coefficient. There is a direct relation between the diffusion coefficient and the atomic mobility in the crystal lattice. As a consequence the diffusion coefficient depends on the nature and strength of atomic bonding in the material. At the same time the diffusion coefficient depends on atomic packing, the way atoms arrange in space, i.e., crystal structure. In a close-packed structure with less free space the mobility is limited and the diffusion coefficient is lower than in a more “open” structure. We will try to quantify the above observations. Consider a metal where self-diffusion takes place by the jump of atoms in vacancy sites. The activation energy ΔHD* can be written as ΔHD*=ΔHv+ΔHm
Introduction
Published in Nayef Ghasem, Modeling and Simulation of Chemical Process Systems, 2018
Diffusion from the arbitrary molecular motion is known as molecular diffusion; diffusion from turbulent eddies is referred to as turbulent diffusion or eddy diffusion. The diffusion coefficient is the coefficient in Fick’s first law, where J is the diffusion flux (amount of substance per unit area per unit time). Mass flux is the measurement of the amount of mass passing in or out of the control volume. The governing equation for calculating mass flux is the continuity equation. The mass flux is defined simply as mass flow per area.
A difunctional Pluronic®127-based in situ formed injectable thermogels as prolonged and controlled curcumin depot, fabrication, in vitro characterization and in vivo safety evaluation
Published in Journal of Biomaterials Science, Polymer Edition, 2021
Samiullah Khan, Naveed Akhtar, Muhammad Usman Minhas, Hassan Shah, Kifayat Ullah Khan, Raghu Raj Singh Thakur
Diffusion coefficient (D) represents the amount of substance passes through a unit area in unit time across the concentration gradient. For the determination of diffusion coefficient of the hydrogels, dried discs were soaked in basic medium (pH = 7.4) due to their higher swelling. Diffusion coefficient of the hydrated gels was calculated by gradually drying the swollen gels at room temperature till constant weight. Diffusion coefficients of the chemically grafted in situ depot gels were calculated by the following equation [16]: where D refers to the diffusion coefficient of the hydrogels, Qeq refers to the equilibrium swelling degree of the in situ depot gel, θ is the slope of the linear part of the swelling curves, and ℎ refers to the initial thickness of gel disc in dry state.
Molecular dynamics simulation on influence of temperature effect on electro-coalescence behavior of nano-droplets
Published in Journal of Dispersion Science and Technology, 2018
Qicheng Chen, Jie Ma, Yingjin Zhang, Chunlei Wu
Figure 5 shows the self-diffusion coefficient. In the same electric field, the frequency of the motion and collision of molecules are enhanced as the temperature increasing. This results in violent deviating of molecules from the initial position, and weakens the electrostatic restriction among molecules. Thus, the self-diffusion coefficient increases with the temperature increasing. However, because the thermal motion is weaker at the lower temperature, e.g., 298 K, 303 K, the difference of the self-diffusion coefficient is almost inconspicuous, as shown in Figure 5. As the temperature increases further, the diffusion effect of water molecules is so violent that the electrostatic attraction dominates the coalescence process during stage II. Thus, the efficiency of the coalescence is enhanced due to the diffusion effect, which is agreed with the conclusions of Ref.[29] It should be noticed that higher temperature makes water clusters move toward to the arbitrarily direction. Therefore, the higher temperature is harmful for the electro-coalescence.
Simulation-assisted investigation on the formation of layer bands and the microstructural evolution in directed energy deposition of Ti6Al4V blocks
Published in Virtual and Physical Prototyping, 2021
Xufei Lu, Guohao Zhang, Junjie Li, Miguel Cervera, Michele Chiumenti, Jing Chen, Xin Lin, Weidong Huang
Until now, several classic theories like MLSW model have been developed to quantify the growth (coarsening) of the second phase during the isothermal processes (Semiatin et al. 2019; Zhang et al. 2021c). However, coarsening observed in AM parts is obtained under repeated heating and cooling cycles, which is different from the constant temperature assumption in MLSW theory. So far, no theoretical model can directly quantify Ostwald ripening or transformation coarsening mechanism at variable temperature. However, Ostwald ripening theory has illustrated that the particle coarsening depends on two factors in terms of the coarsening time and the diffusion coefficient (Vengrenovitch 1982). Noticeably, the diffusion coefficient is positively related to temperature. The higher the temperature or the longer the time, the greater the diffusion coefficient. Therefore, the coarsening level can be determined by time and temperature involved. Based on this, the integral area (IA), i.e. the area under the evolving curve of temperature versus time, is proposed to assess the coarsening level, as it considers the combined effect of both time and temperature. It should be noted that the larger is the IA, the longer is the acting time and/or the higher is the sustained temperature, favouring the coarsening of α plates. As known, diffusion-controlled Oswald ripening can happen in a wide range of temperatures. Thereby, starting when the peak temperature is smaller than Tβ, the integral area (IAOsw) below the thermal curve, coloured in yellow in Figure 14(b), is calculated and plotted in Figure 15(a). Observe that the IAOsw values at the same build-height are close for four cases, whereas the microstructural characteristics in Figure 8 are markedly different. This illustrates that Ostwald ripening hardly explains the coarsening of the α lamellar observed in these cases.