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Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equations (Neural SDEs) are an attractive modeling technique for…

Machine Learning · Computer Science 2025-02-05 Xi Zhang , Yuan Pu , Yuki Kawamura , Andrew Loza , Yoshua Bengio , Dennis L. Shung , Alexander Tong

We demonstrate that discontinuous shear thickening (DST) can occur even in moderately dense, inertial suspensions of hydrodynamically interacting, frictionless soft particles. Using the Lubrication-Friction Discrete Element Method, our…

Soft Condensed Matter · Physics 2025-05-28 Satoshi Takada , Kazuhiro Hara , Hisao Hayakawa

Oscillatory shear tests are widely used in rheology to characterize the linear and non-linear mechanical response of complex fluids, including the yielding transition. There is an increasing urge to acquire detailed knowledge of the…

Soft Condensed Matter · Physics 2021-09-07 Paolo Edera , Matteo Brizioli , Giuliano Zanchetta , George Petekidis , Fabio Giavazzi , Roberto Cerbino

Thermophoresis is an effective method to drive the motion of nanoparticles in fluids. The transport of nanoparticles in polymer networks has significant fundamental and applied importance in biology and medicine, and can be described as…

Soft Condensed Matter · Physics 2024-03-01 Yu Lu , Guo-Hui Hu

We present a local detection method for dissipative particle dynamics (DPD) involving arbitrarily shaped geometric three-dimensional domains. By introducing an indicator variable of boundary volume fraction (BVF) for each fluid particle,…

Computational Physics · Physics 2020-05-12 Zhen Li , Xin Bian , Yu-Hang Tang , George Em Karniadakis

In this work we present a simple qualitative model to describe shear rheological behavior of the twist-bend nematic liquid crystals. We find that at relatively low shear rate the effective viscosity decreases with the shear rate manifesting…

Soft Condensed Matter · Physics 2022-10-26 E. I. Kats

In this paper, a stochastic Hamiltonian formulation (SHF) is proposed and applied to dissipative particle dynamics (DPD) simulations. As an extension of Hamiltonian dynamics to stochastic dissipative systems, the SHF provides necessary…

Numerical Analysis · Mathematics 2022-04-26 Linyu Peng , Noriyoshi Arai , Kenji Yasuoka

Transit-time damping (TTD) is a process in which the magnetic mirror force -- induced by the parallel gradient of magnetic field strength -- interacts with resonant plasma particles in a time-varying magnetic field, leading to the…

Plasma Physics · Physics 2024-11-20 Rui Huang , Gregory G. Howes , Andrew J. McCubbin

We present simulations of coherent structures in compressible flows near the transition to turbulence using the Dissipative Particle Dynamics (DPD) method. The structures we find are remarkably consistent with experimental observations and…

Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic method which allows to select the level of resolution at which a fluid is simulated. In this work, we study the consistency of the resulting thermodynamic properties as a…

Statistical Mechanics · Physics 2016-10-19 G. Faure , J. Roussel , J. -B. Maillet , G. Stoltz

Accurately measuring liquid dynamic viscosity across a wide range of shear rates, from the linear-response to shear-thinning regimes, presents significant experimental challenges due to limitations in resolving high shear rates and…

Materials Science · Physics 2025-03-26 Hongyu Gao , Minghe Zhu , Jia Ma , Marc Honecker , Kexian Li

Smoothed Dissipative Particle Dynamics (SDPD) is a mesoscopic particle method which allows to select the level of resolution at which a fluid is simulated. The numerical integration of its equations of motion still suffers from the lack of…

Statistical Mechanics · Physics 2017-10-25 Gérôme Faure , Gabriel Stoltz

From paints to food products, solvent evaporation is ubiquitous and critically impacts product rheological properties. It affects Newtonian fluids by concentrating any non-volatile components and viscoelastic materials, which hardens up. In…

Soft Condensed Matter · Physics 2021-10-19 Pierre Lehéricey , Audrey Delots , Niels Holten-Andersen , Thibaut Divoux

The non-Newtonian behavior of a monodisperse concentrated dispersion of spherical particles was investigated using a direct numerical simulation method, that takes into account hydrodynamic interactions and thermal fluctuations accurately.…

Soft Condensed Matter · Physics 2012-11-22 Takuya Iwashita , Ryoichi Yamamoto

We identify the sequence of microstructural changes that characterize the evolution of an attractive particulate gel under flow and discuss their implications on macroscopic rheology. Dissipative Particle Dynamics (DPD) is used to monitor…

Soft Condensed Matter · Physics 2017-02-07 Safa Jamali , Gareth H. McKinley , Robert C. Armstrong

The current theories on the mechanism for polymer drag-reduction (DR) are generally applicable for long-chain flexible polymers that form viscoelastic solutions. Rigid polymer solutions that generate DR seemingly lack prevalent viscoelastic…

Fluid Dynamics · Physics 2021-11-03 Lucas Warwaruk , Sina Ghaemi

The rheological characterisation of complex fluids is mostly performed under simple shear flow in rotational rheometers. Their modern commercial versions are extremely sensitive instruments which are able to provide very accurate…

Fluid Dynamics · Physics 2021-03-05 Ahmad Fakhari , Francisco J. Galindo-Rosales

This paper evaluates the behavior of a single rigid ellipsoidal particle suspended in homogenous viscous flow with a power-law Generalized Newtonian Fluid (GNF) rheology using a custom-built finite element analysis (FEA) simulation. The…

Fluid Dynamics · Physics 2025-01-07 Aigbe Awenlimobor , Douglas E. Smith

This paper studies the dynamics of relaxation phenomena in the standard dissipative particle dynamics (DPD) model [Groot and Warren, JCP, 107:4423 (1997)]. Using fluctuating hydrodynamics as the framework of the investigation, we focus on…

Statistical Mechanics · Physics 2018-03-29 J. S. Hansen , Michael L. Greenfield , Jeppe C. Dyre

Diffuse correlation spectroscopy (DCS) is an emerging noninvasive technique that measures the tissue blood flow, by using near-infrared coherent point-source illumination to detect spectral changes. While machine learning has demonstrated…

Machine Learning · Computer Science 2024-02-02 Xi Chen , Xingda Li