论文标题
通过相互浓度依赖的扩散率将两个物种解散
Demixing of two species via reciprocally concentration-dependent diffusivity
论文作者
论文摘要
我们通过假设粒子的密度依赖性有效扩散系数来解散两个物种的模型。两种类型的Microswimmers分散为活跃的倾向强度的活跃的布朗颗粒，这些粒子具有噪声强度，这取决于感应半径$ r_s $中各个其他物种的周围密度。较高的第一（第二种）浓度将扩大扩散，因此，第二种（第一（第一））所经历的噪声强度。宏观方程的稳态的数值和分析研究证明，由于这种相互浓度依赖性扩散率，颗粒的分解。纯本地模型的数值集成方案的模棱两可（$ r_s \ to 0 $）可以通过在具有$ r_s> 0 $的非局部模型中考虑非变化感应半径来解决。
We propose a model for demixing of two species by assuming a density-dependent effective diffusion coefficient of the particles. Both sorts of microswimmers diffuse as active overdamped Brownian particles with a noise intensity that is determined by the surrounding density of the respective other species within a sensing radius $r_s$. A higher concentration of the first (second) sort will enlarge the diffusion and, in consequence, the intensity of the noise experienced by the second (first) sort. Numerical and analytical investigations of steady states of the macroscopic equations prove the demixing of particles due to this reciprocally concentration-dependent diffusivity. An ambiguity of the numerical integration scheme for the purely local model ($r_s\to 0$) is resolved by considering nonvanishing sensing radii in a nonlocal model with $r_s > 0$.
