Zobrazeno 1 - 10
of 216
pro vyhledávání: '"Montessori, Andrea"'
Autor:
Durve, Mihir, Tucny, Jean-Michel, Orsini, Sibilla, Tiribocchi, Adriano, Montessori, Andrea, Lauricella, Marco, Camposeo, Andrea, Pisignano, Dario, Succi, Sauro
We introduce a two-step, fully reversible process designed to project the outer shape of a generic droplet onto a lower-dimensional space. The initial step involves representing the droplet's shape as a Fourier series. Subsequently, the Fourier coeff
Externí odkaz:
http://arxiv.org/abs/2407.04863
Autor:
Durve, Mihir, Tucny, Jean-Michel, Bhamre, Deepesh, Tiribocchi, Adriano, Lauricella, Marco, Montessori, Andrea, Succi, Sauro
The shape of liquid droplets in air plays an important role in aerodynamic behavior and combustion dynamics of miniaturized propulsion systems such as microsatellites and small drones. Their precise manipulation can yield optimal efficiency in such s
Externí odkaz:
http://arxiv.org/abs/2403.15797
In this work an optimized multicomponent lattice Boltzmann (LB) model is deployed to simulate axisymmetric turbulent jets of a fluid evolving in a quiescent, immiscible environment over a wide range of dynamic regimes. The implementation of the multi
Externí odkaz:
http://arxiv.org/abs/2403.15773
Autor:
Succi, Sauro, Montessori, Andrea
We present a mathematical and computational framework to couple the Keldysh non equilibrium quantum transport formalism with a nanoscale lattice Boltzmann method for the computational design of quantum-engineered nanofluidic devices.
Comment: To
Comment: To
Externí odkaz:
http://arxiv.org/abs/2403.15768
Autor:
Guglielmo, Gianmarco, Montessori, Andrea, Tucny, Jean-Michel, La Rocca, Michele, Prestininzi, Pietro
Application of Neural Networks to river hydraulics is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many purely data-driven Neural Networks proved to lack predictive capabilities
Externí odkaz:
http://arxiv.org/abs/2403.08589
Autor:
Montessori, Andrea, La Rocca, Michele, Amati, Giorgio, Lauricella, Marco, Tiribocchi, Adriano, Succi, Sauro
We present a highly-optimized thread-safe lattice Boltzmann model in which the non-equilibrium part of the distribution function is locally reconstructed via recursivity of Hermite polynomials. Such a procedure allows the explicit incorporation of no
Externí odkaz:
http://arxiv.org/abs/2401.17074
Autor:
Durve, Mihir, Orsini, Sibilla, Tiribocchi, Adriano, Montessori, Andrea, Tucny, Jean-Michel, Lauricella, Marco, Camposeo, Andrea, Pisignano, Dario, Succi, Sauro
Publikováno v:
Physics of Fluids 36, 022105 (2024)
In microfluidic systems, droplets undergo intricate deformations as they traverse flow-focusing junctions, posing a challenging task for accurate measurement, especially during short transit times. This study investigates the physical behavior of dro
Externí odkaz:
http://arxiv.org/abs/2310.19374
Autor:
Fei, Linlin, Du, Jingyu, Luo, Kai H., Succi, Sauro, Lauricella, Marco, Montessori, Andrea, Wang, Qian
Publikováno v:
Physics of Fluids 31, 042105 (2019)
In this paper, we develop a three-dimensional multiple-relaxation-time lattice Boltzmann method (MRT-LBM) based on a set of non-orthogonal basis vectors. Compared with the classical MRT-LBM based on a set of orthogonal basis vectors, the present non-
Externí odkaz:
http://arxiv.org/abs/2306.16274
The prediction non-equilibrium transport phenomena in disordered media is a difficult problem for conventional numerical methods. An example of a challenging problem is the prediction of gas flow fields through porous media in the rarefied regime, wh
Externí odkaz:
http://arxiv.org/abs/2305.06222