scholarly journals A hybrid semi-Lagrangian cut cell method for advection-diffusion problems with Robin boundary conditions in moving domains

2021 ◽  
pp. 110805
Author(s):  
Aaron Barrett ◽  
Aaron L. Fogelson ◽  
Boyce E. Griffith
2016 ◽  
Vol 22 (2) ◽  
Author(s):  
Karl K. Sabelfeld

AbstractWe suggest random walk on semi-infinite cylinders methods for solving interior and exterior diffusion problems with different types of boundary conditions which include mixed Dirichlet, Neumann, and Robin boundary conditions on different parts of the boundary. Based on probabilistic interpretation of the diffusion process, stochastic simulation algorithms take into account specific features of each boundary condition to optimally adjust the Markov chain distribution on the relevant boundary parts. In contrast to the conventional direct trajectory tracking method, the new method avoids to simulate the diffusion trajectories. Instead, it exploits exact probabilities of different events like the first passage, splitting, and survival probabilities inside the semi-infinite cylinders, depending on the domain and its boundary structure. Applications to diffusion imaging methods like the cathodoluminescence (CL) and electron beam induced current (EBIC) semiconductor analysis techniques performed in scanning electron and transmission microscopes, are discussed.


2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Li-Bin Liu ◽  
Ying Liang ◽  
Xiaobing Bao ◽  
Honglin Fang

AbstractA system of singularly perturbed convection-diffusion equations with Robin boundary conditions is considered on the interval $[0,1]$ [ 0 , 1 ] . It is shown that any solution of such a problem can be expressed to a system of first-order singularly perturbed initial value problem, which is discretized by the backward Euler formula on an arbitrary nonuniform mesh. An a posteriori error estimation in maximum norm is derived to design an adaptive grid generation algorithm. Besides, in order to establish the initial values of the original problems, we construct a nonlinear optimization problem, which is solved by the Nelder–Mead simplex method. Numerical results are given to demonstrate the performance of the presented method.


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