Size Distributions of Fluid Membrane Vesicles Far From Equilibrium

1996 ◽  
Vol 463 ◽  
Author(s):  
Leonardo Golubović ◽  
Mirjana Golubović
Keyword(s):  
Transport Equation ◽  
Size Distribution ◽  
Time Evolution ◽  
Boltzmann Transport Equation ◽  
Membrane Vesicles ◽  
Size Distributions ◽  
Boltzmann Transport ◽  
Fluid Membrane ◽  
Equilibration Process ◽  
Far From Equilibrium

ABSTRACTWe investigate nonequilibrium behavior of a polydisperse ensemble of fluid membrane vesicles by means of a diffusive Boltzmann transport equation which, incorporates vesicle diffusion and the reactions between vesicles. This approach is used to study the time evolution of the size distribution of an initially monodisperse vesicle ensemble and its interesting properties such as the internal aqueous, encapsulated volume. We investigate various nonequilibrium paths such ensembles may follow during the equilibration process.

Numerical Analysis of Electro-Thermal Phenomena in Metal by Boltzmann Transport Equation for Electron

2005 ◽  
Author(s):  
Kohei Ito ◽  
Ryohei Muramoto ◽  
Isamu Shiozawa ◽  
Yasushi Kakimoto ◽  
Takashi Masuoka
Keyword(s):  
Transport Equation ◽  
Equilibrium Distribution ◽  
Boltzmann Transport Equation ◽  
Quasi Equilibrium ◽  
Boltzmann Transport ◽  
Simulation Based ◽  
Far From Equilibrium ◽  
New Formulation ◽  
Thermal Phenomena ◽  
Non Equilibrium

By the development of micro-fabrication technology, much smaller-size electronic devices will be soon available. In such a smaller device, a non-equilibrium state might appear in metal and/or semiconductor. In this case, it is difficult to estimate the device performance by the macroscopic transport equations that assume quasi-equilibrium distribution. We are developing a numerical simulation based on Boltzmann transport equation (BTE), which can analyze thermal and electric phenomena even when the state is far from equilibrium. In this study, we show a new formulation of BTE for free electron in metal and its calculation result: the thermoelectric power obtained agreed with that of experimental value: the heat flux derived by the non-equilibrium distribution was two-orders small than that estimated by thermal conductivity.

Solving Nongray Boltzmann Transport Equation in Gallium Nitride

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Ajit K. Vallabhaneni ◽  
Liang Chen ◽  
Man P. Gupta ◽  
Satish Kumar
Keyword(s):  
Gallium Nitride ◽  
Transport Equation ◽  
Thermal Transport ◽  
Hot Spot ◽  
Boltzmann Transport Equation ◽  
Significant Loss ◽  
Computational Time ◽  
Boltzmann Transport ◽  
Phonon Modes ◽  
Fourier Model

Several studies have validated that diffusive Fourier model is inadequate to model thermal transport at submicron length scales. Hence, Boltzmann transport equation (BTE) is being utilized to improve thermal predictions in electronic devices, where ballistic effects dominate. In this work, we investigated the steady-state thermal transport in a gallium nitride (GaN) film using the BTE. The phonon properties of GaN for BTE simulations are calculated from first principles—density functional theory (DFT). Despite parallelization, solving the BTE is quite expensive and requires significant computational resources. Here, we propose two methods to accelerate the process of solving the BTE without significant loss of accuracy in temperature prediction. The first one is to use the Fourier model away from the hot-spot in the device where ballistic effects can be neglected and then couple it with a BTE model for the region close to hot-spot. The second method is to accelerate the BTE model itself by using an adaptive model which is faster to solve as BTE for phonon modes with low Knudsen number is replaced with a Fourier like equation. Both these methods involve choosing a cutoff parameter based on the phonon mean free path (mfp). For a GaN-based device considered in the present work, the first method decreases the computational time by about 70%, whereas the adaptive method reduces it by 60% compared to the case where full BTE is solved across the entire domain. Using both the methods together reduces the overall computational time by more than 85%. The methods proposed here are general and can be used for any material. These approaches are quite valuable for multiscale thermal modeling in solving device level problems at a faster pace without a significant loss of accuracy.

ShengBTE: A solver of the Boltzmann transport equation for phonons

2014 ◽  
Vol 185 (6) ◽  
pp. 1747-1758 ◽  
Author(s):  
Wu Li ◽  
Jesús Carrete ◽  
Nebil A. Katcho ◽  
Natalio Mingo
Keyword(s):  
Transport Equation ◽  
Boltzmann Transport Equation ◽  
Boltzmann Transport

Chebyshev spectral hexahedral wavelets on the sphere for angular discretisations of the Boltzmann transport equation

2008 ◽  
Vol 35 (6) ◽  
pp. 1098-1108 ◽  
Author(s):  
A.G. Buchan ◽  
C.C. Pain ◽  
M.D. Eaton ◽  
R.P. Smedley-Stevenson ◽  
A.J.H. Goddard
Keyword(s):  
Transport Equation ◽  
Boltzmann Transport Equation ◽  
Boltzmann Transport

Nonequilibrium size distributions of fluid membrane vesicles

1997 ◽  
Vol 56 (3) ◽  
pp. 3219-3230 ◽  
Author(s):  
Leonardo Golubović ◽  
Mirjana Golubović
Keyword(s):  
Membrane Vesicles ◽  
Size Distributions ◽  
Fluid Membrane

Study of an Iterative Solution for Boltzmann Transport Equation and Calculation of Thermal Conductivity

2018 ◽  
Vol 777 ◽  
pp. 421-425 ◽  
Author(s):  
Chhengrot Sion ◽  
Chung Hao Hsu
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Numerical Calculation ◽  
Iterative Method ◽  
Transport Equation ◽  
Linear Systems ◽  
Heat Transport ◽  
Boltzmann Transport Equation ◽  
Iterative Solution ◽  
Boltzmann Transport

Many methods have been developed to predict the thermal conductivity of the material. Heat transport is complex and it contains many unknown variables, which makes the thermal conductivity hard to define. The iterative solution of Boltzmann transport equation (BTE) can make the numerical calculation and the nanoscale study of heat transfer possible. Here, we review how to apply the iterative method to solve BTE and many linear systems. This method can compute a sequence of progressively accurate iteration to approximate the solution of BTE.

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