arXiv subjects

Marco Ellero

Publications and source records attributed to Marco Ellero.

At least 19 recordsLinked to original sources

arXiv2026

Towards the Multiscale Design of Pressure Sensitive Adhesives

Pressure-sensitive adhesives (PSAs) are soft polymeric materials that exhibit complex rheological and mechanical behavior gov- erned by the interplay between polymer architecture, crosslink density, and entanglement constraints. Predicting their rheological properties from underlying microstructure remains a central challenge in adhesive design. In this work, we adopt a multiscale com- putational framework based on the Lagrangian Heterogeneous Multiscale Method (LHMM), coupling a macroscopic continuum description with a mesoscale polymer network model featuring breakable bonds embedded in a viscous medium. The approach enables consistent information transfer across scales and captures both elastic network response and viscous dissipation. The framework is calibrated using experimental rheological data and tensile measurements for four PSA formulations with varying gel fractions and crosslink densities. The simulations reproduce key experimental trends in storage modulus (G'), loss modulus (G"), and tensile stress-strain behavior under planar extension, while differentiating the distinct mechanical signatures of each formula- tion. The results elucidate how crosslink density and effective network connectivity control stiffness, stress localization, and failure characteristics. Overall, the proposed multiscale methodology provides a predictive platform for linking microstructural design pa- rameters to macroscopic mechanical properties and offers a rational basis for the formulation and optimization of next-generation PSAs.

cond-mat.soft↗

arXiv2025

Lagrangian Heterogeneous Multiscale Method (LHMM) for Simulating Polymer Solutions/Melts Behavior under Complex Flows using DPD-SPH

We present a Lagrangian Heterogeneous Multiscale Method (LHMM) for simulating the non-Newtonian rheology of polymer melts in complex two-dimensional flows. The method couples Dissipative Particle Dynamics (DPD) at the microscale with a GENERIC-compliant Smoothed Particle Hydrodynamics (SPH) at the macroscale, in a concurrent framework, overcoming the limitations of traditional Eulerian-based methods in capturing long-memory and history-dependent effects. At the microscale, DPD serves as a virtual rheometer, employing FENE (Finitely Extensible Nonlinear Elastic) bead-spring polymer chains. This approach provides key rheological properties, including shear-thinning and zero-shear-rate viscosities, relaxation times, and viscoelastic dynamics, which are quantified via Carreau-Yasuda fitting and spectral analysis. The LHMM couples SPH-derived strain rates with microscopic stress responses using the Irving-Kirkwood formalism. This approach enables a concurrent interaction between macroscopic strain rates and microscopic stress tensors, ensuring a consistent viscoelastic response across scales. The method is validated against benchmark flows, including Reverse Poiseuille Flow and flow through a Periodic Array of Cylinders, across Weissenberg numbers $0.5 < \text{Wi} < 30$ and low Reynolds numbers ($\text{Re} < 1$). A final demonstration of flow in a 2D porous medium highlights LHMM's capability to handle highly heterogeneous geometries. The LHMM is implemented in LAMMPS, making it suitable for integrating multiple models to describe microscales. In contrast, large-scale simulations efficiently utilize GPU and CPU resources, managing multiple coupling and time-scaling levels to maintain numerical stability and accuracy. The framework offers a predictive, constitutive-free tool that links microscopic polymer dynamics to macroscopic flow behavior, making it suitable for multiscale applications.

physics.flu-dyn↗

arXiv2025

Mesoscale Simulations of Thrombin Activation and Fibrin Formation in Microvascular and In Vitro Settings

Blood coagulation is governed by tightly regulated reaction networks that unfold within a flowing, heterogeneous microvascular environment. Reduced kinetic models of the intrinsic and extrinsic pathways have seen limited in vitro validation, and their behavior within spatially resolved flow fields remains largely unexplored. Here, we embed two established reduced networks into a recently proposed mesoscale particle-based framework that resolves fluid momentum transport alongside multispecies advection-diffusion-reaction dynamics. We investigate the initiation phase of coagulation by simulating thrombin formation in microvascular geometries and in vitro assays, and we assess the framework's ability to reproduce thrombin generation curves (TGCs) under physiologically relevant conditions. We further examine how variations in fibrinogen levels - an important determinant of clot structure and a biomarker for inflammation and thrombosis - affect thrombin and fibrin formation. Overall, this study provides a unified computational approach for analysing how biochemical kinetics interact with transport processes, offering insights relevant to thrombosis modeling and blood diagnostics.

physics.flu-dyn↗

arXiv2025

Concentration Matters: Enhancing Particle Settling in Narrow Tilted Channels

Particles are known to sediment faster in containers with tilted walls than in vertical ones, a phenomenon known as the Boycott effect. In this work, we investigate how the tilt angle influences sedimentation in narrow channels across different particle volume fractions. Using particle-resolved computational fluid dynamics simulations, we reveal that there exists a concentration-dependent optimal tilt angle that maximizes sedimentation rates. Furthermore, at large tilt angles, the flow profiles across the channel deviate from the classical parabolic shape. We show that these non-parabolic profiles can be accurately captured by a one-dimensional Brinkman model, providing a predictive framework for understanding and tuning sedimentation in tilted geometries. Our findings demonstrate the potential to control and optimize particle settling by adjusting the channel tilt according to particle concentration, opening new possibilities for design in industrial and laboratory processes.

physics.flu-dyn↗

arXiv2025

PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling

Recent advances in Scientific Machine Learning have shown that second-order methods can enhance the training of Physics-Informed Neural Networks (PINNs), making them a suitable alternative to traditional numerical methods for Partial Differential Equations (PDEs). However, second-order methods induce large memory requirements, making them scale poorly with the model size. In this paper, we define a local Mixture of Experts (MoE) combining the parameter-efficiency of ensemble models and sparse coding to enable the use of second-order training. Our model -- \textsc{PINN Balls} -- also features a fully learnable domain decomposition structure, achieved through the use of Adversarial Adaptive Sampling (AAS), which adapts the DD to the PDE and its domain. \textsc{PINN Balls} achieves better accuracy than the state-of-the-art in scientific machine learning, while maintaining invaluable scalability properties and drawing from a sound theoretical background.

cs.LG↗

arXiv2025

Smoothed Dissipative Particle Dynamics for Mesoscale Advection-Diffusion-Reaction Problems

Smoothed dissipative particle dynamics (SDPD) is a widely used particle-based method for modelling soft matter systems at mesoscopic and macroscopic scales, offering thermodynamic consistency and direct control over the fluid's transport properties. Here, we present an SDPD model that incorporates the transport of reactants on scales smaller than the discretising particles, including the evolution of compositional fields. The proposed methodology is well-suited for modelling complex systems governed by advection-diffusion-reaction (ADR) dynamics. Implemented in LAMMPS, the model is validated using a range of benchmark problems spanning diffusion-dominated, reaction-dominated, and coupled ADR regimes. Our simulation results demonstrate that the implemented SDPD model effectively captures complex behaviours, such as Turing pattern formation. The proposed model holds promise for applications across various fields, including biology, chemistry, materials science, and environmental engineering.

physics.flu-dyn↗

arXiv2025

A semi-analytical solution for the lubrication force between two spheres approaching in viscoelastic fluids described by the Oldroyd-B model under small Deborah numbers

Viscoelastic fluids play a critical role in various engineering and biological applications, where their lubrication properties are strongly influenced by relaxation times ranging from microseconds to minutes. Although the lubrication mechanism for Newtonian fluids is well-established, its extension to viscoelastic materials - particularly under squeezing flow conditions - remains less explored. This study presents a semi-analytical solution for the lubrication force between two spheres approaching in a Boger fluid under small Deborah numbers. Unlike previous works that assumed a Newtonian velocity field, we derive the velocity profile directly from the mass-momentum conservation and Oldroyd-B constitutive equations using lubrication theory and order-of-magnitude analysis techniques. Under steady-state conditions, viscoelasticity induces a marginal increase in the surface-to-surface normal force as a result of the increased pressure required to overcome the original resistance from the first normal-stress difference. Transient analyses reveal that the normal lubrication force is bounded by two Newtonian plateaus and is nonsymmetric as the spheres approach or separate. Our findings highlight the role of viscoelasticity in improving load capacity and provide new insight for modelling dense particle suspensions in Boger fluids, where short-range interactions dominate.

physics.flu-dyn↗

arXiv2025

Computational Modelling of Thixotropic Multiphase Fluids

Multiphase systems are ubiquitous in engineering, biology, and materials science, where understanding their complex interactions and rheological behavior is crucial for advancing applications ranging from emulsion stability to cellular phase separation. This study presents a numerical methodology for modeling thixotropic multiphase fluids, emphasizing the transient behavior of viscosity and the intricate interactions between phases. The model incorporates phase-dependent viscosities, interfacial tension effects, and the dynamics of phase separation, coalescence, and break-up, making it suitable for simulating systems with complex flow regimes. A key feature of the methodology is its ability to capture thixotropic behavior, where viscosity evolves over time due to microstructural changes induced by shear history. This approach enables the simulation of aging and recovery processes in materials such as gels, emulsions, and biological tissues. The model is rigorously validated against benchmark cases, demonstrating its accuracy in predicting multiphase systems under static and dynamic conditions. Subsequently, the methodology is applied to investigate systems with varying levels of microstructural evolution, revealing the impact of thixotropic dynamics on overall system behavior. The results provide new insights into the time-dependent rheology of multiphase fluids and highlight the versatility of the model for applications in industrial and biological systems involving complex fluid interactions.

physics.flu-dyn↗

arXiv2025

Integral fractional viscoelastic models in SPH: LAOS simulations versus experimental data

The rheological behaviour of a polymer was investigated by performing numerical simulations in complex flow and comparing them to experiments. For our simulations, we employed a Smoothed Particle Hydrodynamics scheme, utilizing an integral fractional model based on the K-BKZ framework. The results are compared with experiments performed on melt-state isotactic polypropylene under medium and large amplitude oscillatory shear. The numerical results are in good agreement with the experimental data, and the model is able to capture and predict both the linear and the non-linear viscoelastic behaviours of the polymer melt. Results show that equipping SPH with an integral fractional model is promising approach for the simulation of complex polymeric materials under realistic conditions.

cond-mat.soft↗

arXiv2025

A GENERIC-guided active learning SPH method for viscoelastic fluids using Gaussian process regression

When applying machine learning methods to learn viscoelastic constitutive relations, the polymer history dependence in viscoelastic fluids and the generalization ability of machine learning models are challenging. In this paper, guided by the general equation for nonequilibrium reversible-irreversible coupling (GENERIC) framework, a novel GENERIC-guided active learning smoothed particle hydrodynamics (${\rm{G^2ALSPH}}$) method is proposed to obtain effective constitutive relations for reliable simulations of viscoelastic flows. By utilizing the GENERIC framework, the target viscoelastic constitutive relation is reduced to a simple functional relation between the eigenvalues of the conformation tensor and the eigenvalues of its thermodynamically conjugated tensorial variable, which incorporates the flow-history-dependent memory effect. Based on data and Gaussian process regression (GPR), a new active learning strategy is developed to obtain the simplified constitutive relation, in which the generalization ability is ensured by actively acquiring more data points when needed. Moreover, a novel relative uncertainty is devised to establish an accuracy evaluation tool for the GPR prediction results, which reduces the number of required training data points while maintaining accuracy. Furthermore, the SPH method combined with the latest techniques serves as an effective macroscopic numerical method. Eventually, the Poiseuille flows and the flows around a periodic array of cylinders at different Weissenberg numbers are simulated to validate the effectiveness and accuracy of the ${\rm{G^2ALSPH}}$ method. The Oldroyd-B model is used as the ground truth constitutive relation to provide data for GPR, bringing analytical solutions for comparison. The excellent performance demonstrates that the ${\rm{G^2ALSPH}}$ method has promising applications in data-driven simulations of viscoelastic fluids.

physics.flu-dyn↗

arXiv2025

Simulating non-Brownian suspensions with non-homogeneous Navier slip boundary conditions

Fluid-structure interactions are commonly modeled using no-slip boundary conditions. However, small deviations from these conditions can significantly alter the dynamics of suspensions and particles, especially at the micro and nano scales. This work presents a robust implicit solvent method for simulating non-colloidal suspensions with non-homogeneous Navier slip boundary conditions. Our approach is based on a regularized boundary integral formulation, enabling accurate and efficient computation of hydrodynamic interactions. This makes the method well-suited for large-scale simulations. We validate the method by comparing computed drag forces on homogeneous and Janus particles with analytical results. Additionally, we consider the effective viscosity of suspensions with varying slip lengths, benchmarking against available analytical no-slip and partial-slip theories.

cond-mat.soft↗

arXiv2025

Settling dynamics of non-Brownian suspension of spherical and cubic particles in Stokes flow

The present study investigates the gravity-driven settling dynamics of non-Brownian suspensions of spherical and cubic particles within a triply periodic domain. The effect of solid volume fraction on the evolving microstructure of a suspension is numerically examined by employing the Rigid MultiBlob method under Stokes flow conditions. Our simulations accurately reproduce macroscopic trends observed in experiments and show strong agreement with established semi-empirical correlations over a wide range of volume fractions. It is observed that the hydrodynamic interactions between particles and the surrounding fluid dominate the settling mechanism at low to moderate solid volume fractions, whereas, frequent collisions between particles in a highly packed space tend to suppress velocity fluctuations at denser regime. The transport properties for dilute suspensions are found to be primarily shaped by an anisotropic microstructure, though this anisotropy decreases as many-body interactions significantly grow at higher volume fractions. Notably, cubic particles exhibit lower anisotropy in velocity fluctuations compared to spheres, due to more effective momentum and energy transfer from the gravity-driven direction to transverse directions. Moreover, cubic particles also display higher velocity fluctuations and particle rebounding after collisions, which may create a transient tendency for cubes to move against gravity more readily than spheres. The distinctive behaviors of cubic and spherical particles can notably influence the efficiency of size segregation in bidisperse mixtures. Interestingly, local velocity fluctuation intensity increases for both particle types in mixed suspensions, affecting the local sedimentation velocity field. Overall, spherical particles are found to settle faster than cubes at low to moderate volume fractions.

physics.flu-dyn↗

arXiv2024

Hammering at the entropy: A GENERIC-guided approach to learning polymeric rheological constitutive equations using PINNs

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 the training of the Neural Network is guided by an evolution equation for the conformation tensor which is GENERIC-compliant. We compare two training methodologies for the data-driven PINN constitutive models: one trained on data from the analytical solution of the Oldroyd-B model under steady-state rheometric flows (PINN-rheometric), and another trained on in-silico data generated from complex flow CFD simulations around a cylinder that use the Oldroyd-B model (PINN-complex). The capacity of the PINN models to provide good predictions are evaluated by comparison with CFD simulations using the underlying Oldroyd-B model as a reference. Both models are capable of predicting flow behavior in transient and complex conditions; however, the PINN-complex model, trained on a broader range of mixed flow data, outperforms the PINN-rheometric model in complex flow scenarios. The geometry agnostic character of our methodology allows us to apply the learned PINN models to flows with different topologies than the ones used for training.

physics.flu-dyn↗

arXiv2024

Numerical Simulations of Spatiotemporal Instabilities in Discontinuous Shear Thickening Fluids

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 (SPH) approach. The model reproduces the spatially homogeneous instability mechanism based on the competition between the inertial and microstructural timescales, with good congruence to the theoretical predictions. Spatial inhomogeneities arising from a stress-splitting instability are rationalised within the context of local components of the microstructure evolution. Using this effect, the addition of non-locality in the model is found to produce an alternative mechanism of temporal instabilities, driven by the inhomogeneous pattern formation. The reported arrangement of the microstructure is generally in agreement with the experimental data on gradient pattern formation in DST. Simulations in a parameter space representative of realistic DST materials resulted in aperiodic oscillations in measured shear rate and stress, driven by formation of gap-spanning frictional structures.

physics.flu-dyn↗

arXiv2023

Microstructural Smoothed Particle Hydrodynamics Model and Simulations of Discontinuous Shear-Thickening Fluids

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 Particle Hydrodynamic (SPH) simulation. The scalar model was implemented in an SPH scheme and tested in two flow geometries. Three distinct ratios of local to non-local microstructural effects were probed: weak, moderate, and strong non-locality. Strong and moderate cases yielded excellent agreement with flow curves constructed via the Wyart--Cates (WC) model, with the moderate case exhibiting banding patterns. Weak non-locality produced stress-splitting instability, resulting in discontinuous stress fields and poor agreement with the WC model. The mechanism of the stress-splitting has been explored and contextualised by the interaction of local microstructure evolution and the stress-control scheme. Velocity profiles obtained in body force-driven channel flow were found to be in excellent agreement with the analytical solution, yielding an upward inflection corresponding to the typical S-curve. Simulations carried out at increasing driving forces exhibited a decrease in flow. We showed that even the simple scalar model can capture some of the key properties of DST materials, laying the foundation for further SPH study of instabilities and pattern formation.

physics.flu-dyn↗

arXiv2023

Smoothed Particle Hydrodynamics simulations of integral multi-mode and fractional viscoelastic models

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 materials, such as the change in power-law relaxation pattern during the relaxation process of some materials. Using the Lagrangian Smoothed-Particle Hydrodynamics (SPH) method we can easily track particle history; this allows us to solve integral constitutive models in a novel way, without relying on complex tasks. Hence, we develop here a SPH integral viscoelastic method which is first validated for simple Maxwell or Oldroyd-B models under Small Amplitude Oscillatory Shear flows (SAOS). By exploiting the structure of the integral method, a multi-mode Maxwell model is then implemented. Finally, the method is extended to include fractional constitutive models, validating the approach by comparing results with theory under SAOS.

physics.flu-dyn↗

arXiv2023

Ridge instability in dense suspensions caused by the second normal stress difference

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 fluids and the force balance, we show that the flat free surface is unstable when the second normal stress difference $N_2$ is negatively proportional to shear stress and the gravity component perpendicular to the wall is weak enough. Such instability is inevitable to grow into a ridge-like surface profile oriented parallel to the flow direction. We use the instability criterion to predict the critical slope angle for the formation of ridge patterns. The estimated critical angle was found to be in agreement with experimental observations for a cornstarch suspension.

cond-mat.soft↗

arXiv2023

Exploring the link between coffee matrix microstructure and flow properties using combined X-ray microtomography and smoothed particle hydrodynamics simulations

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 this article, we use X-ray micro-computed (microCT) technique to capture the microscopic details of coffee matrices at particle-level and perform fluid dynamics simulation based on the smoothed particle hydrodynamics method (SPH) with the 3D reconstructured data. Information like flow permeability and tortuosity of the matrices can be therefore obtained from our simulation. We found that inertial effects can be quite significant at the normal pressure gradient conditions typical for espresso brewing, and can provide an explanation for the inconsistency of permeability measurements seen in the literature. Several types of coffee powder are further examined, revealing their distinct microscopic details and resulting flow features. By comparing the microCT images of pre- and post-extraction coffee matrices, it is found that a decreasing porosity profile (from the bottom-outlet to the top-inlet) always develops after extraction. This counterintuitive phenomenon can be explained using a pressure-dependent erosion model proposed in our prior work. Our results show that microCT scan can provide useful microscopic details for coffee extraction modelling and establish the basis for a data-driven numerical framework to explore the link between coffee powder microstructure and extraction dynamics. The latter is the prerequisite to study the time evolution of both volatile and non-volatile organic compounds and then the flavour profile of coffee brews.

physics.flu-dyn↗