Zobrazeno 1 - 10
of 179
pro vyhledávání: '"Ellero, Marco"'
Autor:
Simavilla, David Nieto, Bonfanti, Andrea, de Beristain, Imanol García, Español, Pep, Ellero, Marco
We present a versatile framework that employs Physics-Informed Neural Networks (PINNs) to discover the entropic contribution that leads to the constitutive equation for the extra-stress in rheological models of polymer solutions. In this framework th
Externí odkaz:
http://arxiv.org/abs/2409.07545
Discontinuous Shear Thickening (DST) fluids exhibit unique instability properties in a wide range of flow conditions. We present numerical simulations of a scalar model for DST fluids in a planar simple shear using the Smoothed Particle Hydrodynamics
Externí odkaz:
http://arxiv.org/abs/2408.08285
To capture specific characteristics of non-Newtonian fluids, during the past years fractional constitutive models have become increasingly popular. These models are able to capture in a simple and compact way the complex behaviour of viscoelastic mat
Externí odkaz:
http://arxiv.org/abs/2311.13173
Despite the recent interest in the discontinuous shear-thickening (DST) behaviour, few computational works tackle the rich hydrodynamics of these fluids. In this work, we present the first implementation of a microstructural DST model in Smoothed Par
Externí odkaz:
http://arxiv.org/abs/2311.13759
A dense suspension of the cornstarch flowing on a very inclined wall finally forms some ridge-like patterns of the free surface. The onset of pattern formation is the primary target to elucidate the mechanism. In this work, based on the continuity of
Externí odkaz:
http://arxiv.org/abs/2311.11201
Publikováno v:
Scientific Reports, 13, 16374(2023)
Coffee extraction involves many complex physical and transport processes extremely difficult to model. Among the many factors that will affect the final quality of coffee, the microstructure of the coffee matrix is one of the most critical ones. In t
Externí odkaz:
http://arxiv.org/abs/2305.03911
Physics-Informed Neural Networks (PINNs) are Neural Network architectures trained to emulate solutions of differential equations without the necessity of solution data. They are currently ubiquitous in the scientific literature due to their flexible
Externí odkaz:
http://arxiv.org/abs/2302.07557
Functionalized nanoparticles (NPs) are complex objects present in a variety of systems ranging from synthetic grafted nanoparticles to viruses. The morphology and number of the decorating groups can vary widely between systems. Thus, the modelling of
Externí odkaz:
http://arxiv.org/abs/2211.16181
Autor:
Moreno, Nicolas, Ellero, Marco
We introduce a full-Lagrangian heterogeneous multiscale method (LHMM) to model complex fluids with microscopic features that can extend over large spatio-temporal scales, such as polymeric solutions and multiphasic systems. The proposed approach disc
Externí odkaz:
http://arxiv.org/abs/2211.05080
Publikováno v:
In Journal of Computational Physics 1 August 2024 510
