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Motion of inertial particles in Gaussian fields driven by an infinite-dimensional fractional Brownian motion

机译:无限维分数布朗运动驱动高斯场中的惯性粒子运动

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We study the motion of an inertial particle in a fractional Gaussian random field. The motion of the particle is described by Newton's second law, where the force is proportional to the difference between the background fluid velocity and the particle velocity. The fluid velocity satisfies a linear stochastic partial differential equation driven by an infinite-dimensional fractional Brownian motion with an arbitrary Hurst parameter H∈(0, 1). The usefulness of such random velocity fields in simulations is that we can create random velocity fields with desired statistical properties, thus generating artificial images of realistic turbulent flows. This model also captures the clustering phenomenon of preferential concentration, observed in real world and numerical experiments, i.e. particles cluster in regions of low-vorticity and high-strain rate. We prove almost sure existence and uniqueness of particle paths and give sufficient conditions to rewrite this system as a random dynamical system with a global random pullback attractor. Finally, we visualize the random attractor through a numerical experiment.
机译:我们研究了分数高斯随机场中惯性粒子的运动。牛顿第二定律描述了粒子的运动,该力与背景流体速度和粒子速度之差成正比。流体速度满足由具有任意Hurst参数H∈(0,1)的无穷维分数布朗运动驱动的线性随机偏微分方程。这种随机速度场在仿真中的有用之处在于,我们可以创建具有所需统计特性的随机速度场,从而生成逼真的湍流人工图像。该模型还捕获了在现实世界和数值实验中观察到的优先集中的聚类现象,即颗粒在低涡度和高应变率区域聚集。我们证明了粒子路径的存在和唯一性,并给出了充分的条件来将该系统重写为具有全局随机回拉吸引子的随机动力学系统。最后,我们通过数值实验将随机吸引子可视化。

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