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The fate of small particles in turbulent flows depends strongly on the surrounding fluid's velocity gradient properties such as rotation and strain-rates. For non-inertial (fluid) particles, the Restricted Euler model provides a simple,…

Fluid Dynamics · Physics 2017-04-05 Perry L. Johnson , Charles Meneveau

The Recent Fluid Deformation Closure (RFDC) model of lagrangian turbulence is recast in path-integral language within the framework of the Martin-Siggia-Rose functional formalism. In order to derive analytical expressions for the…

Fluid Dynamics · Physics 2015-06-18 L. Moriconi , R. M. Pereira , L. S. Grigorio

The congestion formation on a urban road network is one of the key issue for the development of a sustainable mobility in the future smart cities. In this work we propose a reductionist approach studying the stationary states of a simple…

Physics and Society · Physics 2024-05-28 Lorenzo Di Meco , Mirko Degli Esposti , Federico Bellisardi , Armando Bazzani

Predicting particle transport in complex flows is traditionally achieved by solving the Navier-Stokes equations. While various numerical and experimental methods exist, they typically require deep physical insights and incur high…

Fluid Dynamics · Physics 2025-11-03 Jingdi Wan , Hongping Wang , Bo Liu , Xiaolei Yang , Xiaodong Hu , Shengze Cai , Guowei He , Yang Liu

Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as obtained from particle tracking experiments. Recent work in this context highlights the application of graph-based approaches,…

Dynamical Systems · Mathematics 2019-07-08 Ralf Banisch , Péter Koltai , Kathrin Padberg-Gehle

Reduced models describing the Lagrangian dynamics of the Velocity Gradient Tensor (VGT) in Homogeneous Isotropic Turbulence (HIT) are developed under the Physics-Informed Machine Learning (PIML) framework. We consider VGT at both Kolmogorov…

Fluid Dynamics · Physics 2021-08-17 Yifeng Tian , Daniel Livescu , Michael Chertkov

Understanding how star formation is regulated requires studying the energy balance between turbulence, magnetic fields, stellar feedback, and gravity within molecular clouds. However, identifying the transition region where the gravity…

Astrophysics of Galaxies · Physics 2020-07-22 Yue Hu , Alex Lazarian , Ka Ho Yuen

Random geometric graphs (RGG) can be formalized as hidden-variables models where the hidden variables are the coordinates of the nodes. Here we develop a general approach to extract the typical configurations of a generic hidden-variables…

Disordered Systems and Neural Networks · Physics 2015-04-28 Massimo Ostilli , Ginestra Bianconi

The equation for the fluid velocity gradient along a Lagrangian trajectory immediately follows from the Navier-Stokes equation. However, such an equation involves two terms that cannot be determined from the velocity gradient along the…

Fluid Dynamics · Physics 2023-06-21 Xiaolong Zhang , Maurizio Carbone , Andrew D. Bragg

Because the Navier-Stokes equations are dissipative, the long-time dynamics of a flow in state space are expected to collapse onto a manifold whose dimension may be much lower than the dimension required for a resolved simulation. On this…

Fluid Dynamics · Physics 2023-01-12 Alec J. Linot , Michael D. Graham

Distributions of the resilience of transport networks are studied numerically, in particular the large-deviation tails. Thus, not only typical quantities like average or variance but the distributions over the (almost) full support can be…

Physics and Society · Physics 2015-06-18 Alexander K. Hartmann

We analyze data from direct numerical simulations of homogeneous and isotropic turbulence (at Re_\lambda \approx 280) and study the statistics of curvature and torsion of Lagrangian trajectories in order to extract informations on the…

Chaotic Dynamics · Physics 2009-09-11 Andrea Scagliarini

The dynamics of Lagrangian particles in turbulence play a crucial role in mixing, transport, and dispersion in complex flows. Their trajectories exhibit highly non-trivial statistical behavior, motivating the development of surrogate models…

The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (potential energy). These combinations yield different…

Machine Learning · Computer Science 2024-07-04 Kirill Neklyudov , Rob Brekelmans , Alexander Tong , Lazar Atanackovic , Qiang Liu , Alireza Makhzani

Turbulent flows consist of a wide range of interacting scales. Since the scale range increases as some power of the flow Reynolds number, a faithful simulation of the entire scale range is prohibitively expensive at high Reynolds numbers.…

Fluid Dynamics · Physics 2023-07-24 Dhawal Buaria , Katepalli R. Sreenivasan

We develop a variational Bayesian (VB) approach for estimating large-scale dynamic network models in the network autoregression framework. The VB approach allows for the automatic identification of the dynamic structure of such a model and…

Methodology · Statistics 2021-02-19 Wei-Ting Lai , Ray-Bing Chen , Ying Chen , Thorsten Koch

The dynamics of a passive scalar plume in a turbulent boundary layer is experimentally investigated via vertical turbulent transport time-series. Data are acquired in a rough-wall turbulent boundary layer that develops in a recirculating…

We numerically investigate jamming transitions in complex heterogeneous networks. Inspired by Internet routing protocols, we study a general model that incorporates local traffic information through a tunable parameter. The results show…

Statistical Mechanics · Physics 2007-05-23 Pablo Echenique , Jesus Gomez-Gardenes , Yamir Moreno

Turbulent fluid flows exhibit a complex small-scale structure with frequently occurring extreme velocity gradients. Particles probing such swirling and straining regions respond with an intricate shape-dependent orientational dynamics,…

Fluid Dynamics · Physics 2020-11-30 Leonhard A. Leppin , Michael Wilczek

Finding accurate reduced descriptions for large, complex, dynamically evolving networks is a crucial enabler to their simulation, analysis, and, ultimately, design. Here we propose and illustrate a systematic and powerful approach to…

Chaotic Dynamics · Physics 2017-05-02 Tom Bertalan , Yan Wu , Carlo Laing , C. William Gear , Ioannis G. Kevrekidis