arXiv subjects

Gianmarco Guglielmo

Publications and source records attributed to Gianmarco Guglielmo.

3 recordsLinked to original sources

arXiv2026

Accurate wetting dynamics via a conservative Allen-Cahn based lattice Boltzmann approach for multiphase flows

In this work we propose a local geometric wetting boundary condition for a conservative Allen--Cahn-based lattice Boltzmann framework. The prescribed contact angle is imposed through ghost phase-field values constructed from a locally reconstructed wall normal and a donor-fluid extrapolation. The ghost-node wetting update is local, geometrically consistent, and compatible with thread-safe large-scale implementations, while phase-field mass is controlled through a separate global volume correction. Validation includes static contact-angle tests, short-time droplet spreading, impact on a hydrophobic surface, and gravity-driven motion through a sharp-edged orifice. The simulations recover the imposed equilibrium angles, reproduce contact-angle-dependent spreading exponents between approximately 1/2 and 1/4, and follow the classical W e1/4 maximum-deformation scaling. The model also captures the transition between capture, release, and release with breakup. The proposed approach provides an accurate and scalable framework for wetting-controlled flows in complex geometries.

physics.flu-dyn↗

arXiv2025

Physics-Informed Neural Networks for microflows: Rarefied Gas Dynamics in Cylinder Arrays

Accurate prediction of rarefied gas dynamics is crucial for optimizing flows through microelectromechanical systems, air filtration devices, and shale gas extraction. Traditional methods, such as discrete velocity and direct simulation Monte Carlo (DSMC), demand intensive memory and computation, especially for microflows in non-convex domains. Recently, physics-informed neural networks emerged as a meshless and adaptable alternative for solving non-linear partial differential equations. We trained a PINN using a limited number of DSMC-generated rarefied gas microflows in the transition regime with Knudsen number from 0.1 to 3, incorporating continuity and Cauchy momentum exchange equations in the loss function. The PINN achieved under 2 percent error on these residuals and effectively filtered DSMC intrinsic statistical noise. Predictions remained strong for a tested flow field with Kn equal to 0.7, and showed limited extrapolation performance on a flow field with Kn equal to 5 with a local overshoot of about 20 percent, while maintaining physical consistency. Notably, each DSMC field required about 20 hours on 4 graphics processing units (GPU), while the PINN training took less than 2 hours on one GPU, with evaluations under 2 seconds.

physics.comp-ph↗

arXiv2024

Can physical information aid the generalization ability of Neural Networks for hydraulic modeling?

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. In this work, we propose to mitigate such problem by introducing physical information into the training phase. The idea is borrowed from Physics-Informed Neural Networks which have been recently proposed in other contexts. Physics-Informed Neural Networks embed physical information in the form of the residual of the Partial Differential Equations (PDEs) governing the phenomenon and, as such, are conceived as neural solvers, i.e. an alternative to traditional numerical solvers. Such approach is seldom suitable for environmental hydraulics, where epistemic uncertainties are large, and computing residuals of PDEs exhibits difficulties similar to those faced by classical numerical methods. Instead, we envisaged the employment of Neural Networks as neural operators, featuring physical constraints formulated without resorting to PDEs. The proposed novel methodology shares similarities with data augmentation and regularization. We show that incorporating such soft physical information can improve predictive capabilities.

cs.LG↗