arXiv subjects

Adriano Tiribocchi

Publications and source records attributed to Adriano Tiribocchi.

At least 19 recordsLinked to original sources

arXiv2026

Negative quantum friction in nanoscale water flows: the Wigner picture

We explore the phenomenon of "quantum" friction based on a single-particle model patterned after the Wigner equation describing electrons flow in a solid wall confining nanoscale water flows. The numerical simulations show a clear signature of negative quantum friction, namely a net momentum transfer from the electrons in the solid wall to the flowing water molecules. Such net momentum transfer results into a sizeable reduction of the water friction, up to forty percent, depending on the strength of the coupling between classical and quantum fluctuations. Our results offer the prospect of a theoretical framework bridging classical and quantum description by using continuum kinetic theories and particle-based simulations.

cond-mat.mes-hall↗

arXiv2026

LBFAST: A Lightweight Moment-Represented Lattice Boltzmann Solver for Multi-GPU Architectures

We present LBFAST, a GPU-oriented lattice Boltzmann solver based on a lightweight moment-represented formulation, in which post-collision populations are reconstructed on the fly from a reduced set of moments rather than stored explicitly. This approach significantly lowers the memory footprint, enabling large three-dimensional simulations within the constraints of modern accelerator architectures, where VRAM capacity and bandwidth are critical resources. The method is assessed through standard single- and two-component benchmarks demonstrating good accuracy and stability. Extensive scaling experiments on multi-GPU systems show near-ideal weak scaling up to 512 GPUs and sustained performance across different velocity sets. The combination of reduced memory usage, competitive throughput, and stable energy efficiency makes the proposed formulation a practical route for large-scale lattice Boltzmann simulations on current and emerging HPC platforms.

cs.DC↗

arXiv2026

Schr\"odinger-Navier-Stokes equation for capillary fluids

We highlight some properties of the Schr\"odinger-Navier-Stokes (SNS) equation [Salasnich, Succi, and Tiribocchi (2024)] of potential relevance for microfluidics and soft matter. Specifically, we show that the SNS equation with generic parameters is formally equivalent to the Navier-Stokes-Korteweg equations for capillary fluids, with the equivalence established at the level of an action functional that decomposes naturally into a Korteweg conservative and a dissipative contribution. We derive the dispersion relation for sound modes, showing that the dispersive parameter controls capillary stiffness while the dissipative parameter controls viscous damping, and that the Bogoliubov dispersion relation is recovered in the quantum limit. We also derive an effective one-dimensional SNS equation for a fluid confined in a narrow capillary tube.

physics.flu-dyn↗

arXiv2026

Three dimensional contractile droplet under confinement

We numerically study the dynamics of a three-dimensional contractile fluid droplet in the bulk and under confinement. We show that varying activity leads to a variety of shapes and motile regimes whose motion is driven by an interplay between spontaneous flows and elasticity. In the bulk the droplet self-propels unidirectionally, acquiring either an almost spherical shape at intermediate activity or a peanut-like geometry for larger values. Under confinement, the droplet exhibits a previously unreported oscillating dynamics characterized by periodic hits against opposite walls of a microchannel while moving forward. These results could be of interest for the study of artificial microswimmers and their biological analogs, such as living cells.

cond-mat.soft↗

arXiv2026

Ghost-Mode Filtered Fluctuating Lattice Boltzmann Method

Fluctuating lattice Boltzmann solvers are widely employed to model mesoscopic fluid behavior in soft-matter systems, including colloidal suspensions and dilute polymer solutions. Despite their utility, these methods can lose accuracy and stability when non-hydrodynamic modes interfere with the dynamics, especially in single--relaxation-time schemes. Here, we introduce a ghost-mode filtered fluctuating lattice Boltzmann method (GMF-FLBM) for the D3Q27 lattice, obtained by selectively eliminating the propagation of the ghost deterministic content while preserving the necessary stochastic forcing. We show, over a broad range of relaxation times, that GMF-FLBM recovers the amplitudes of equilibrium fluctuations with a comparable accuracy as a fully regularized high-order formulation, while requiring only minor adjustments to the conventional BGK collision framework.

cond-mat.soft↗

arXiv2025

Phase Separation Kinetics in a Polar Active Field Model

A milestone of phase separation kinetics is the emergence of universal power laws $\sim t^{1/z}$ governing the domain growth evolution. We investigate a phase-separating polar active model comprising a scalar density field with an advective coupling to a polarization field. Our analysis reveals a novel $\sim t^{0.6}$ regime, which agrees well with the accelerated growth recently observed in simulations of polar active particles. We provide analytical arguments to explain how advection facilitates the creation of topological defects and compresses the domains leading to faster growth. We also show that the $\sim t^{0.6}$ regime is robust to several model generalizations.

cond-mat.soft↗

arXiv2025

Regularized Fluctuating Lattice Boltzmann Model

We introduce a regularized fluctuating lattice Boltzmann model (Reg-FLBM) for the D3Q27 lattice, which incorporates thermal fluctuations through Hermite-based projections to ensure compliance with the fluctuation-dissipation theorem. By leveraging the recursive regularization framework, the model achieves thermodynamic consistency for both hydrodynamic and ghost modes. Compared to the conventional single-relaxation-time BGK-FLBM, the Reg-FLBM provides improved stability and a more accurate description of thermal fluctuations. The implementation is optimized for large-scale parallel simulations on GPU-accelerated architectures, enabling systematic investigation of fluctuation-driven phenomena in mesoscale and nanoscale fluid systems.

physics.comp-ph↗

arXiv2025

Thread-safe multiphase lattice Boltzmann model for droplet and bubble dynamics at high density and viscosity contrasts

This study presents a high-order, thread-safe version of the lattice Boltzmann (LBM) method, incorporating an interface-capturing equation, based on the conservative Allen-Cahn equation, to simulate incompressible two-component systems with high-density and viscosity contrasts. The method utilizes a recently proposed thread-safe implementation optimized for shared memory architectures and it is employed to reproduce the dynamics of droplets and bubbles on several test cases with results in agreement with experiments and other numerical simulations from the literature. The proposed approach offers promising opportunities for high-performance computing simulations of realistic fluid systems with high-density and viscosity contrasts for advanced applications in environmental, atmospheric and meteorological flows, all the way down to microfluidic and biological systems, particularly on GPU-based architectures.

physics.flu-dyn↗

arXiv2024

Minimal droplet shape representation in experimental microfluidics using Fourier series and autoencoders

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 coefficients are reduced to lower-dimensional vectors by using autoencoder models. The exploitation of the domain knowledge of the droplet shapes allows us to map generic droplet shapes to just 2D space in contrast to previous direct methods involving autoencoders that could map it on minimum 8D space. This 6D reduction in the dimensionality of the droplet's description opens new possibilities for applications, such as automated droplet generation via reinforcement learning, the analysis of droplet shape evolution dynamics and the prediction of droplet breakup. Our findings underscore the benefits of incorporating domain knowledge into autoencoder models, highlighting the potential for increased accuracy in various other scientific disciplines.

physics.flu-dyn↗

arXiv2024

Droplet shape representation using Fourier series and autoencoders

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 systems. It is desired to have a minimal representation of droplet shapes using as few parameters to automate shape manipulation using self-learning algorithms, such as reinforcement learning. In this paper, we use a neural compression algorithm to represent, with only two parameters, elliptical and bullet-shaped droplets initially represented with 200 points (400 real numbers) at the droplet boundary. The mapping of many to two points is achieved in two stages. Initially, a Fourier series is formulated to approximate the contour of the droplet. Subsequently, the coefficients of this Fourier series are condensed to lower dimensions utilizing a neural network with a bottleneck architecture. Finally, 5000 synthetically generated droplet shapes were used to train the neural network. With a two real numbers representation, the recovered droplet shapes had excellent overlap with the original ones, with a mean square error 10^-3. Hence, this method compresses the droplet contour to merely two numerical parameters via a fully reversible process, a crucial feature for rendering learning algorithms computationally tractable.

physics.flu-dyn↗

arXiv2024

3D active nematic disclinations behave as Majorana quasiparticles

Quasiparticles are low-energy excitations with important roles in condensed matter physics. An intriguing example is provided by Majorana fermions, quasiparticles which are identical to their antiparticles. Despite being implicated in neutrino oscillations and topological superconductivity, their experimental realisations remain scarce. Here we propose a purely classical realisation of Majorana fermions, in terms of 3-dimensional disclination lines in active nematics. Activity is required to overcome the elastic cost associated with these excitations, so they can appear in steady state. We combine topology and simulations to show that active nematics under confinement spontaneously create in their interior topologically charged disclination lines and loops, akin to Majorana quasiparticles with finite momentum. Within an elongated channel, we find a phenomenology similar to that of the Kitaev chain, as local Majorana-like excitations appear near surfaces, while a non-local system-spanning helical disclination line can arise along the centre. In unconfined active turbulence, Majorana-like charged loops are instead exceedingly rare, suggesting that boundaries are crucial to generate these quasiparticles, as in quantum condensed matter. We suggest that 3-dimensional active disclinations can be used to probe the physics of Majorana spinors at a much larger scale than traditionally considered, potentially facilitating the experimental observation of their dynamics.

cond-mat.soft↗

arXiv2024

Measuring arrangement and size distributions of flowing droplets in microchannels through deep learning

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 droplets within dense emulsions in diverse microchannel geometries, specifically focusing on the impact of varying opening angles within the primary channel and injection rates of fluid components. Employing a sophisticated droplet tracking tool based on deep-learning techniques, we analyze multiple frames from flow-focusing experiments to quantitatively characterize droplet deformation in terms of ratio between maximum width and height and propensity to form liquid with hexagonal crystalline order. Our findings reveal the existence of an optimal opening angle where shape deformations are minimal and crystal-like arrangement is maximal. Variations of fluid injection rates are also found to affect size and packing fraction of the emulsion in the exit channel. This paper offers insights into deformations, size and structure of fluid emulsions relative to microchannel geometry and other flow-related parameters captured through machine learning, with potential implications for the design of microchips utilized in cellular transport and tissue engineering applications.

physics.flu-dyn↗

arXiv2024

Controlling flow patterns and topology in active emulsions

Active emulsions and liquid crystalline shells are intriguing and experimentally realisable types of topological matter. Here we numerically study the morphology and spatiotemporal dynamics of a double emulsion, where one or two passive small droplets are embedded in a larger active droplet. We find activity introduces a variety of rich and nontrivial nonequilibrium states in the system. First, a double emulsion with a single active droplet becomes self-motile, and there is a transition between translational and rotational motion: both of these regimes remain defect-free, hence topologically trivial. Second, a pair of particles nucleate one or more disclination loops, with conformational dynamics resembling a rotor or chaotic oscillator, accessed by tuning activity. In the first state a single, topologically charged, disclination loop powers the rotation. In the latter state, this disclination stretches and writhes in 3D, continuously undergoing recombination to yield an example of an active living polymer. These emulsions can be self-assembled in the lab, and provide a pathway to form flow and topology patterns in active matter in a controllable way, as opposed to bulk systems that typically yield active turbulence.

cond-mat.soft↗

arXiv2024

High-order thread-safe lattice Boltzmann model for HPC turbulent flow simulations

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 non-equilibrium moments of the distribution up to the order supported by the lattice. Thus, the proposed approach increases accuracy and stability at low viscosities without compromising performances and amenability to parallelization with respect to standard lattice Boltzmann models. The high-order thread-safe LB is tested on two types of turbulent flows, namely the turbulent channel flow at $Re_τ=180$ and the axisymmetric turbulent jet at $Re = 7000$, it delivers results in excellent agreement with reference data (both DNS, theory, and experiments) and a) achieves peak performances ($\sim 5 \; TeraFlop/s$ and an arithmetic intensity of $\sim 7\; FLOP/byte$ on single GPU) by significantly reducing the memory footprint, b) retains the algorithmic simplicity of standard lattice Boltzmann computing and c) allows to perform stable simulations at vanishingly low viscosities. Our findings open attractive prospects for high-performance simulations of realistic turbulent flows on GPU-based architectures. Such expectations are confirmed by the excellent agreement among lattice Boltzmann, experimental, and DNS reference data.

physics.flu-dyn↗

arXiv2023

Shapes and dynamic regimes of a polar active fluid droplet under confinement

Active droplets are artificial microswimmers built from a liquid dispersion by microfluidic tools and showing self-propelled motion. These systems hold particular interest for mimicking biological phenomena, such as some aspects of cell locomotion and collective behaviors of bacterial colonies, as well as for the design of droplet-based biologically inspired materials, such as engineered tissues. Growing evidence suggests that geometrical confinement crucially affects their morphology and motility, but the driving physical mechanisms are still poorly understood. Here we study the effect of activity on a droplet containing a contractile polar fluid confined within microfluidic channels of various sizes. We find a surprising wealth of shapes and dynamic regimes, whose mechanics is regulated by a subtle interplay between contractile stress, droplet elasticity and microchannel width. They range from worm-like and cell-like shaped droplets displaying an oscillating behavior within wider channels to bullet-shaped droplets exhibiting rectilinear motion in narrower slits. Our findings support the view that geometrical confinement can provide a viable strategy to control and predict the propulsion direction of active droplets. It would be of interest to look for analogues of these motility modes in biological cells or in synthetic active matter.

cond-mat.soft↗

arXiv2023

Multiscale Hybrid Modeling of Proteins in Solvent: SARS-CoV2 Spike Protein as test case for Lattice Boltzmann -- All Atom Molecular Dynamics Coupling

Physiological solvent flows surround biological structures triggering therein collective motions. Notable examples are virus/host-cell interactions and solvent-mediated allosteric regulation. The present work describes a multiscale approach joining the Lattice Boltzmann fluid dynamics (for solvent flows) with the all-atom atomistic molecular dynamics (for proteins) to model functional interactions between flows and molecules. We present, as an applicative scenario, the study of the SARS-CoV-2 virus spike glycoprotein protein interacting with the surrounding solvent, modeled as a mesoscopic fluid. The equilibrium properties of the wild-type spike and of the Alpha variant in implicit solvent are described by suitable observables. The mesoscopic solvent description is critically compared to the all-atom solvent model, to quantify the advantages and limitations of the mesoscopic fluid description.

physics.comp-ph↗

arXiv2023

Thread-safe lattice Boltzmann for high-performance computing on GPUs

We present thread-safe, highly-optimized lattice Boltzmann implementations, specifically aimed at exploiting the high memory bandwidth of GPU-based architectures. At variance with standard approaches to LB coding, the proposed strategy, based on the reconstruction of the post-collision distribution via Hermite projection, enforces data locality and avoids the onset of memory dependencies, which may arise during the propagation step, with no need to resort to more complex streaming strategies. The thread-safe lattice Boltzmann achieves peak performances, both in two and three dimensions and it allows to sensibly reduce the allocated memory ( tens of GigaBytes for order billions lattice nodes simulations) by retaining the algorithmic simplicity of standard LB computing. Our findings open attractive prospects for high-performance simulations of complex flows on GPU-based architectures.

cs.DC↗

arXiv2023

Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications

Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.

cs.CV↗