arXiv subjects

Sauro Succi

Publications and source records attributed to Sauro Succi.

At least 91 records · Page 5Linked to original sources

arXiv2023

Quantum Algorithm for Lattice Boltzmann (QALB) Simulation of Incompressible Fluids with a Nonlinear Collision Term

We propose a quantum algorithm for solving physical problems represented by the lattice Boltzmann formulation. Specifically, we deal with the case of a single phase, incompressible fluid obeying the Bhatnagar-Gross-Krook model. We use the framework introduced by Kowalski that links the nonlinear dynamics of a system to the evolution of bosonic modes, assigning a Carleman linearization order to the truncation in the bosonic Fock space of the bosons. The streaming and collision steps are both achieved via unitary operators. A quantized version of the nonlinear collision term has been implemented, without introducing variables of discrete densities coupled from neighbouring sites, unlike the classical Carleman technique. We use the compact mapping of the bosonic modes to qubits that uses a number of qubits which scales logarithmically with the size of truncated bosonic Fock space. The work can be readily extended to the multitude of multiphysics problems which could adapt the lattice Boltzmann formulation.

quant-ph↗

arXiv2023

Ensemble Fluid Simulations on Quantum Computers

We discuss the viability of ensemble simulations of fluid flows on quantum computers. The basic idea is to formulate a functional Liouville equation for the probability distribution of the flow field configuration and recognize that, due to its linearity, such an equation is in principle more amenable to quantum computing than the equations of fluid motion. After suitable marginalization and associated closure, the Liouville approach is shown to require several hundreds of logical qubits, hence calling for a major thrust in current noise correction and mitigation techniques.

quant-ph↗

arXiv2023

Benchmarking Noisy Intermediate Scale Quantum Error Mitigation Strategies for Ground State Preparation of the HCl Molecule

Due to numerous limitations including restrictive qubit topologies, short coherence times and prohibitively high noise floors, few quantum chemistry experiments performed on existing noisy intermediate-scale quantum hardware have achieved the high bar of chemical precision, namely energy errors to within 1.6 mHa of full configuration interaction. To have any hope of doing so, we must layer contemporary resource reduction techniques with best-in-class error mitigation methods; in particular, we combine the techniques of qubit tapering and the contextual subspace variational quantum eigensolver with several error mitigation strategies comprised of measurement-error mitigation, symmetry verification, zero-noise extrapolation and dual-state purification. We benchmark these strategies across a suite of eight 27-qubit IBM Falcon series quantum processors, taking preparation of the HCl molecule's ground state as our testbed.

quant-ph↗

arXiv2023

Universal scaling of nanoparticle deposition by colloidal droplet drying

We present a comprehensive study of nanoparticle deposition from drying of colloidal droplets. By means of lattice Boltzmann modeling and theoretical analysis, various deposition patterns, including mountain-like, uniform and coffee ring, as well as un-/symmetrical multiring/mountain-like patterns are achieved. The ratio of nanoparticles deposited at droplet peripheries and center is proposed to quantify different patterns. Its value is controlled by the competition between the capillary flow and nanoparticle diffusion, leading to a linear dependence on an effective Péclet number, across over three orders of magnitude. Remarkably, the final deposition pattern can be predicted based on material properties only.

physics.flu-dyn↗

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↗

arXiv2023

Quantum Carleman Lattice Boltzmann Simulation of Fluids

We present a pedagogical introduction to a quantum computing algorithm for the simulation of classical fluids, based on the Carleman linearization of a second-quantized version of lattice kinetic theory. Prospects and limitations for the case of fluid turbulence are discussed and commented on.

physics.flu-dyn↗

arXiv2022

A Review on Contact and Collision Methods for Multi-body Hydrodynamic problems in Complex Flows

Modeling and direct numerical simulation of particle-laden flows have a tremendous variety of applications in science and engineering across a vast spectrum of scales from pollution dispersion in the atmosphere, to fluidization in the combustion process, to aerosol deposition in spray medication, along with many others. Due to their strongly nonlinear and multiscale nature, the above complex phenomena still raise a very steep challenge to the most computational methods. In this review, we provide comprehensive coverage of multibody hydrodynamic (MBH) problems focusing on particulate suspensions in complex fluidic systems that have been simulated using hybrid Eulerian-Lagrangian particulate flow models. Among these hybrid models, the Immersed Boundary-Lattice Boltzmann Method (IB-LBM) provides mathematically simple and computationally-efficient algorithms for solid-fluid hydrodynamic interactions in MBH simulations. This paper elaborates on the mathematical framework, applicability, and limitations of various 'simple to complex' representations of close-contact interparticle interactions and collision methods, including short-range inter-particle and particle-wall steric interactions, spring and lubrication forces, normal and oblique collisions, and mesoscale molecular models for deformable particle collisions based on hard-sphere and soft-sphere models in MBH models to simulate settling or flow of nonuniform particles of different geometric shapes and sizes in diverse fluidic systems.

physics.flu-dyn↗

arXiv2022

Flow force calculation in Lattice Boltzmann Method

We revisit force evaluation methodologies on rigid solid particles suspended in a viscous fluid and simulated via lattice Boltzmann method (LBM). We point out the non-commutativity of streaming and collision operators in the force evaluation procedure and provide a theoretical explanation for this observation. Based on this analysis, we propose a discrete force calculation scheme with enhanced accuracy. The proposed scheme is essentially a lattice version of the Reynolds Transport Theorem (RTT) in the context of the lattice Boltzmann formulation. Besides maintaining satisfactory levels of reliability and accuracy, the method also handles force evaluation on complex geometries in a simple and transparent way. We run simulations for NACA0012 airfoil for a range of Reynolds numbers ranging from 100 to 0.5x10^6 and show that the current approach significantly reduces the grid size requirement for accurate force evaluation.

physics.flu-dyn↗

arXiv2022

Lattice Fluid Dynamics: Thirty-five Years Down the Road

We comment on the role and impact of lattice fluid dynamics on the general landscape of computational fluid dynamics. Starting from the historical development of Lattice Gas Cellular Automata, we move on to a cursory appraisal of the main applications of the Lattice Boltzmann method and challenges ahead.

physics.flu-dyn↗

arXiv2022

First Post-Minkowskian approach to turbulent gravity

We compute the metric fluctuations induced by a turbulent energy-matter tensor within the first order Post-Minkowskian approximation. It is found that the turbulent energy cascade can in principle interfere with the process of black hole formation, leading to a potentially strong coupling between these two highly nonlinear phenomena. It is further found that a power-law turbulent energy spectrum $E(k) \sim k^{-n}$ generates metric fluctuations scaling like $x^{n-2}$, where $x$ is a four-dimensional distance from an arbitrary origin in spacetime. This highlights the onset of metric singularities whenever $n <2$, meaning that $2d$ fluid turbulence ($n=3$) yields smooth %(differentiable) metric fluctuations, scaling like $x$, while $3d$ turbulence ($n=5/3$) yields a weakly singular metric $x^{-1/3}$and purely random fluctuations, $n=1$, generate a stronger $1/x$ singularity. Finally, the effect of metric fluctuations on the geodesic motion of test particles is also discussed as a potential technique to extract information on the spectral characteristics of fluctuating spacetime.

gr-qc↗

arXiv2022

Discrete Boltzmann multi-scale modeling of non-equilibrium multiphase flows

The aim of this paper is twofold: the first is to formulate and validate a multi-scale discrete Boltzmann method (DBM) based on density functional kinetic theory for thermal multiphase flow systems, ranging from continuum to transition flow regime; the second is to present some new insights into the thermo-hydrodynamic non-equilibrium (THNE) effects in the phase separation process. Methodologically, DBM includes three main pillars: (i) the determination of the fewest kinetic moment relations, which are required by the description of significant THNE effects beyond the realm of continuum fluid mechanics, (ii) the construction of appropriate discrete equilibrium distribution function recovering all the desired kinetic moments, (iii) the detection, description, presentation and analysis of THNE based on the moments of the non-equilibrium distribution ($f-f^{(eq)}$). The incorporation of appropriate additional higher-order thermodynamic kinetic moments considerably extends the DBM's capability of handling larger values of the liquid-vapor density ratio, curbing spurious currents, and ensuring mass-momentum-energy conservation. Compared with the DBM with only first-order THNE (Gan et al. Soft Matter 11,5336), the model retrieves kinetic moments beyond the third-order super-Burnett level, and is accurate for weak, moderate, and strong THNE cases even when the local Knudsen number exceeds $1/3$. Physically, the ending point of the linear relation between THNE and the concerned physical parameter provides a distinct criterion to identify whether the system is near or far from equilibrium. Besides, the surface tension refrains the local THNE around the interface, but expands the THNE range and strengthens the THNE intensity away from the interface through interface smoothing and widening.

physics.flu-dyn↗

arXiv2022

Predicting today's cosmological constant via the Zel'dovich-Holographic connection

This Letter proposes a solution of the Vacuum Energy and the Cosmological Constant (CC) paradox based on the Zel'dovich's ansatz, which states that the observable contribution to the vacuum energy density is given by the gravitational energy of virtual particle-antiparticle pairs, continually generated and annihilated in the vacuum state. The novelty of this work is the use of an ultraviolet cut-off length based on the Holographic Principle, which is shown to yield current values of the CC in good agreement with experimental observations.

gr-qc↗

arXiv2022

Fast kinetic simulator for relativistic matter

We present a new family of relativistic lattice kinetic schemes for the efficient simulation of relativistic flows in both strongly-interacting (fluid) and weakly-interacting (rarefied gas) regimes. The method can also deal with both massless and massive particles, thereby encompassing ultra-relativistic and mildly-relativistic regimes alike. The computational performance of the method for the simulation of relativistic flows across the aforementioned regimes is discussed in detail, along with prospects of future applications for Quark-Gluon Plasma, electron flows in graphene and systems in astrophysical contexts.

physics.comp-ph↗

arXiv2022

Bjorken flow revisited: analytic and numerical solutions in flat space-time coordinates

In this work we provide analytic and numerical solutions for the Bjorken flow, a standard benchmark in relativistic hydrodynamics providing a simple model for the bulk evolution of matter created in collisions between heavy nuclei. We consider relativistic gases of both massive and massless particles, working in a ( 2 + 1 ) and ( 3 + 1 ) Minkowski space-time coordinate system. The numerical results from a recently developed lattice kinetic scheme show excellent agreement with the analytic solutions.

nucl-th↗

arXiv2022

Machine learning assisted droplet trajectories extraction in dense emulsions and their analysis

This work analyzes trajectories obtained by YOLO and DeepSORT algorithms of dense emulsion systems simulated by Lattice Boltzmann methods. The results indicate that the individual droplet's moving direction is influenced more by the droplets immediately behind it than the droplets in front of it. The analysis also provides hints on constraints on writing down a dynamical model of droplets for the dense emulsion in narrow channels.

physics.flu-dyn↗

arXiv2022

Collective Behavior of Crowded Drops in Microfluidic Systems

Droplet microfluidics, in which micro-droplets serve as individual reactors, has enabled a wide range of high-throughput biochemical processes. Unlike solid wells typically used in current biochemical assays, droplets are subject to instability and can undergo breakup, especially under fast flow conditions. Although the physics of single drops has been studied extensively, the flow of crowded drops or concentrated emulsions, where droplet volume fraction exceeds 80 percent, is relatively unexplored in microfluidics. In this article and the related invited lecture from the 74th Annual Meeting of the American Physical Society's Division of Fluid Dynamics, we describe the collective behavior of drops in a concentrated emulsion by tracking the dynamics and the fate of individual drops within the emulsion. At the slow flow limit of the concentrated emulsion, we observe an unexpected order, where the velocity of individual drops in the emulsion exhibits spatiotemporal periodicity. As the flow rate increases, the emulsion transitions from a solid-like to a liquid-like material, and the spatiotemporal order in the flow is lost. At the fast flow limit, droplet breakup starts to occur. We show that droplet breakup within the emulsion follows a probability distribution, in stark contrast to the deterministic behavior in classical single-drop studies. In addition to capillary number and viscosity ratio, break-up probability is governed by a confinement factor that measures drop size relative to a characteristic channel length. The breakup probability arises from the time-varying packing configuration of the drops. Finally, we discuss recent progress in computation methods for recapitulating the flow of concentrated emulsions.

cond-mat.soft↗

arXiv2022

DropTrack -- automatic droplet tracking using deep learning for microfluidic applications

Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. Specifically, droplet tracking in dense emulsions is challenging as droplets move in tightly packed configurations. Sometimes the individual droplets in these dense clusters are hard to resolve, even for a human observer. Here, two deep learning-based cutting-edge algorithms for object detection (YOLO) and object tracking (DeepSORT) are combined into a single image analysis tool, DropTrack, to track droplets in microfluidic experiments. DropTrack analyzes input videos, extracts droplets' trajectories, and infers other observables of interest, such as droplet numbers. Training an object detector network for droplet recognition with manually annotated images is a labor-intensive task and a persistent bottleneck. This work partly resolves this problem by training object detector networks (YOLOv5) with hybrid datasets containing real and synthetic images. We present an analysis of a double emulsion experiment as a case study to measure DropTrack's performance. For our test case, the YOLO networks trained with 60% synthetic images show similar performance in droplet counting as with the one trained using 100% real images, meanwhile saving the image annotation work by 60%. DropTrack's performance is measured in terms of mean average precision (mAP), mean square error in counting the droplets, and inference speed. The fastest configuration of DropTrack runs inference at about 30 frames per second, well within the standards for real-time image analysis.

cs.CV↗

arXiv2022

Role of Oxygen Functionalities in Graphene Oxide Architectural Laminate Subnanometer Spacing and Water Transport

Active research in nanotechnology contemplates the use of nanomaterials for engineering applications. However, a primary challenge is understanding the effects of nanomaterial properties on industrial device performance and translating unique nanoscale properties to the macroscale. One emerging example is graphene oxide (GO) membranes for separation processes. Thus, here we investigate how individual GO properties can impact layered GO characteristics and water permeability. GO chemistry and morphology were controlled with easy-to-implement photo-reduction and sonication techniques and were quantitatively correlated offering a valuable tool to speed up the characterization process. For example, one could perform chemical analysis and concurrently obtain morphology information or vice versa. Chemical GO modification allows for fine control of GO oxidation state and GO laminate nanoarchitecture enabling controlled synthesis of a GO architectural laminate (GOAL). The GOAL can be considered as the selective layer of the membrane created by interconnected sub-nanometer channels, characterized by a length and a height (i.e., GO spacing), through which water molecules permeate. Water permeability was measured for eight GOAL characterized by different GO chemistry and morphology, and indicate that GO nanochannel height dictates water transport. The simulations indicate a no-slip Darcy-like water transport regime inside the GOAL due to the presence of basal oxygen functionalities. The experimental and simulation evidence presented in this letter help create a clearer picture of water transport in GO and can be used to rationally design more effective and efficient GO membranes.

physics.flu-dyn↗