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arXiv 1901.07099math.AP
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具有外部势的群集流体力学

Flocking hydrodynamics with external potentials

Ruiwen Shu, Eitan Tadmor

摘要:

我们研究了流体动力学模型的大时间行为,该模型描述了连续介质的集体行为,由两两对齐相互作用和附加的外部势强迫驱动。外力倾向于与对齐竞争,这使得大时间行为与原始Cucker-Smale(CS)对齐模型非常不同,并且更有趣。在这里,我们专注于一致凸势。在特定的情况下,\emph{quadratic}潜力,我们能够治疗一个大类的容许相互作用内核,$ϕ(r) \gtrsim (1+r^2)^{-β}$与“薄”尾巴$β\leq 1$ --薄比通常的“肥尾”的内核中遇到CS植绒$β\leq\frac{1}{2}$:我们发现无条件植绒速度指数收敛\emph{and}的位置对狄拉克质量作为谐振子旅行。对于一般的凸势,我们施加了一个稳定性条件,需要足够大的对齐内核,以避免人群散射。然后,我们证明,hypoverrity参数,这两个速度\emph{and}位置光滑的解决方案必须群集。我们还证明了一个和两个空间维度的整体光滑解的存在性,在初始配置空间的临界阈值。有趣的是,可以观察到,可以保证亚临界初始数据的全局光滑性,而不依赖于大时间群集行为的先验知识。

英文摘要:

We study the large-time behavior of hydrodynamic model which describes the collective behavior of continuum of agents, driven by pairwise alignment interactions with additional external potential forcing. The external force tends to compete with alignment which makes the large time behavior very different from the original Cucker-Smale (CS) alignment model, and far more interesting. Here we focus on uniformly convex potentials. In the particular case of \emph{quadratic} potentials, we are able to treat a large class of admissible interaction kernels, $ϕ(r) \gtrsim (1+r^2)^{-β}$ with `thin' tails $β\leq 1$ --- thinner than the usual `fat-tail' kernels encountered in CS flocking $β\leq\frac{1}{2}$: we discover unconditional flocking with exponential convergence of velocities \emph{and} positions towards a Dirac mass traveling as harmonic oscillator. For general convex potentials, we impose a stability condition, requiring large enough alignment kernel to avoid crowd scattering. We then prove, by hypocoercivity arguments, that both the velocities \emph{and} positions of smooth solution must flock. We also prove the existence of global smooth solutions for one and two space dimensions, subject to critical thresholds in initial configuration space. It is interesting to observe that global smoothness can be guaranteed for sub-critical initial data, independently of the apriori knowledge of large time flocking behavior.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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