具有Lotka-Volterra型竞争动力学的两种趋化- navier - stokes系统的全局可解性和最终光滑性

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED
Guoqiang Ren, Bin Liu, Jianshe Yu
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引用次数: 0

摘要

本文研究了具有Lotka-Volterra型竞争动力学的两种趋化- navier - stokes系统:nt + u·∇n = Δn−χ1∇·(n∇w) + n(λ1−μ1nθ−1−a1v);vt + u·∇v = Δv−χ2∇·(v∇w) + v(λ2−μ2v−a2n);wt + u·∇w = Δw−w + n + v;ut + κ(u·∇)u = Δu + ΔP + (n + v)∇φ;\(\nabla \cdot u=0\), \(x\in \Omega \), \(t>0\)在无通量/Dirichlet边界条件的有界光滑域\(\Omega \subset {\mathbb {R}}^2\)中,其中\(\chi _1, \chi _2\)为正常数。我们给出了两种趋化- navier - stokes系统的广义解的整体存在性,并且在退化弱得多的系统\((\theta >1)\)中已经出现了最终的平滑性,同样是在\(\lambda _1, \lambda _2\)上的小条件下。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Global Solvability and Eventual Smoothness in a Two-Species Chemotaxis–Navier–Stokes System with Lotka–Volterra Type Competitive Kinetics

In this paper, we study a two-species chemotaxis–Navier–Stokes system with Lotka–Volterra type competitive kinetics: nt + u · ∇n = Δn − χ1∇ · (nw) + n(λ1 − μ1nθ−1 − a1v); vt + u · ∇v = Δv − χ2∇ · (vw) + v(λ2 − μ2v − a2n); wt + u · ∇w = Δw − w + n + v; ut + κ(u · ∇)u = Δu + ΔP + (n + v)∇ϕ; \(\nabla \cdot u=0\), \(x\in \Omega \), \(t>0\) in a bounded and smooth domain \(\Omega \subset {\mathbb {R}}^2\) with no-flux/Dirichlet boundary conditions, where \(\chi _1, \chi _2\) are positive constants. We present the global existence of generalized solution to a two-species chemotaxis–Navier–Stokes system and the eventual smoothness already occurs in systems with much weaker degradation \((\theta >1)\), again under a smallness condition on \(\lambda _1, \lambda _2\).

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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