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Digital Twins (DT) provide a critical approach to connecting physical structures and corresponding virtual representations through constant observations-to-decision flows, enabling near real-time analysis and assessment of structural health. A critical component of DTs of reinforced concrete structures lies in the definition of prognostic capabilities to predict/infer the system response. This is achieved by devising efficient computational methods for the simulation of the mechanical behavior of the system. This study presents the first step in devising a Multiscale Lattice Discrete Particle Model (M-LDPM) approach to be embedded in a DT framework to allow for forward prediction of damage evolution in the structural system. In the DT framework, a modification of the M-LDPM is proposed to address well-known issues associated with linking the macroscopic mesh configuration and the corresponding representative volume elements, significantly reducing the total computational cost. The effectiveness of the proposed multiscale model is validated by comparing numerical results with the full-order solutions for plain concrete members under 3-point bending, and further investigated by comparison with experimental results on three reinforced concrete beams. [ABSTRACT FROM AUTHOR]

Highlights This study developed a numerical model of multi‐walled carbon nanotube (MWCNT)‐reinforced polymer nanocomposite to predict the tensile and flexural behavior of an earth‐stationed antenna reflector. The objectives of the study were to propose a hierarchical model of nanocomposites and introduce the variability in the dimensions of the CNTs. The CNTs were meshed with beam elements and their inner, outer diameter, and length were randomized, as per field emission scanning electron microscopy image. The distribution of the CNTs was realized with the help of a random sequential adsorption algorithm. The CNT content was varied from 0.1 to 0.4 wt% of epoxy matrix. Periodic boundary conditions were implemented. Micro‐scale results were found to lie within 10% of the Halpin‐Tsai model. A concept of dynamically allocated representative volume element was implemented at the meso‐scale, which was then upscaled to macro‐scale to simulate ASTM D3039 and D790 test conditions. With 0.4 wt% of CNT into the matrix, the tensile and flexural modulus increased by 14.42% and 23.94%. The macro‐scale simulated results were compared with literature, where the tensile and flexural modulus were found to deviate by 7.5% and 3%. This model will later be upgraded with continuous fibrous reinforcement along with MWCNT‐filled epoxy, to simulate the performance of a real composite antenna reflector. Multiscale modeling of MWCNT‐based epoxy nanocomposite was developed. MWCNT was meshed with hollow beam elements as per FESEM images. 0.4 wt% MWCNT enhanced tensile and flexural modulus by 14% and 24%. Numerical tensile and flexural modulus lied within 7.5% and 3% of the tests. [ABSTRACT FROM AUTHOR]

The multiscale model of building structures, as a balanced solution between accuracy and cost, has been widely used in the analysis of structural seismic performance. A reasonable interface connection method can accurately ensure load transfer and motion coordination between models of different scales. In this paper, a novel interface connection device and the corresponding design method for a multiscale test model of building structures were proposed, in which the upper structure with smaller sized components was replaced by a simplified story‐scale model, and the lower structure was adopted as a component‐scale model. The overall and local equations of motion for this multiscale model were established. For the interface connection between different scale models, a design method considering multiparametric similarity of shear force, axial force, and bending moment was proposed. In this method, the internal nodes at the interface of the component scale model were decomposed, and the coupling relationship of internal force between two adjacent nodes was established. The axial force of each node was decoupled into the interstory shear force and bending moment provided together. Additionally, the overturning moment is provided by adding the overlapping domain. According to the equilibrium relationships of the nodes at the interface, the corresponding transfer matrix was provided, and the design method of the interface connection device was proposed. The accuracy and feasibility of the method were validated by static and shaking table tests on a frame structure. [ABSTRACT FROM AUTHOR]

In this study, a multiscale model is developed through secondary development (UMAT and UEXTERNALDB) in Abaqus with the objective of simulating the thermal deformation process with dynamic recrystallization behavior. The model couples the finite element method (FEM) with the multiphase field model (MPFM), thereby establishing bidirectional coupling between macroscopic mechanical behavior and microstructural evolution. A comparison between the single-element hot compression simulation and experimental results demonstrates that the model accurately simulates both the macroscopic mechanical behavior and microstructural evolution during the thermal deformation process, thereby exhibiting high precision. Simulations of the reduction pretreatment (RP) process under different reduction amounts and billet surface temperatures demonstrate that increasing the reduction amount and billet surface temperature significantly enhances both plastic deformation and the volume fraction of dynamic recrystallization in the billet core. This results in the closure of core voids and the refinement of the core microstructure, thereby providing valuable guidance for the development of optimal reduction pretreatment (RP) processes. [ABSTRACT FROM AUTHOR]

To address the problems of strong noise interference, long detection times, uneven fuselage surfaces, lack of visual information in two-dimensional images, and difficulty in automatic detection, an automatic detection algorithm for aircraft skin pit damage based on a multiscale surface model was designed. First, an automatic acquisition platform system was constructed using an unmanned vehicle, a lifting pole, and a depth camera. The point-cloud data of the aircraft skin was obtained using this acquisition platform system. The point-cloud data were then preprocessed using the radius filter algorithm, voxel grid filter algorithm, and moving least squares algorithm. Then, the preprocessed point-cloud data were divided into multiscale regions and split into multiple local skin mesh regions to obtain multiple local grid area data. For each local grid region data, the surface models of each local grid region and regional spatial adjacency were obtained by constructing and optimizing the estimation of the local quadric surface based on the random sampling consensus algorithm. The spatial adjacency, surface model, and its index together form the region tree. The local surface models at different scales were aggregated by storing information and normal vector angles in the region tree to identify the damaged and nondamaged regions. Finally, surface features, such as curvature and normal vector, were used to cluster the pit points in the damaged area, and the pit point-cloud data were aggregated to obtain the final pit damage results. The proposed algorithm was compared with existing traditional algorithms, such as the point-cloud block method and the point feature region growth method based on normal vector and curvature. Experimental results showed that the accuracy, recall, and F-value of the point-cloud block method were 4.00%, 20.00%, and 6.67%, respectively, with an average detection time of 20 s. For the point feature region growth method based on normal vector and curvature, the accuracy, recall, and F-value were 30.77%, 26.67%, and 28.79%, respectively, with an average detection time of 25 s. The accuracy, recall, F-value, and average detection time were significantly improved, with mean values of 92.86%, 86.67%, 89.92%, and 6 s, respectively. Additionally, the detection results of the three algorithms on the aircraft skin engine, fuselage, and wing were compared, and the influence of curvature in different regions on the algorithm was analyzed. The detection results of the proposed algorithm were significantly better than those of existing traditional algorithms, such as the point-cloud block method and the point feature area growth method based on normal vector and curvature. The proposed algorithm achieved the goal of automatically detecting pit damage in aircraft skin scenes.

Subduction interfaces exhibit various slip styles, including slow slip events (SSEs). We use a micromechanics‐based approach to calculate the effective rheology of a shear zone containing ellipsoidal amphibolite clasts deforming by dislocation creep within an interconnected linear‐viscous phyllosilicate‐dominated matrix. Frictional failure occurs if local stress exceeds Mohr‐Coulomb yield strength. At moderate fluid overpressure, mixed‐frictional‐viscous behavior emerges at ∼ ${\sim} $350–560° ${}^{\circ}$C, consistent with a broad zone of mixed fault slip behavior without requiring extreme fluid overpressures. Increasing stress in this transition zone promotes local frictional failure and raises bulk strain rate. If, however, the bulk strain rate increases by more than one order of magnitude, system‐wide frictional sliding becomes preferable. This strain rate increase is insufficient to explain the slip rates observed in geophysically detectable SSEs. Therefore, viscous matrix flow as modeled here cannot explain SSEs without either invoking dynamic weakening within a frictional‐viscous flow or a mechanism switch to dominantly frictional sliding. Plain Language Summary: Subduction plate boundaries are locked near the Earth's surface and will release the stored energy as earthquakes. Subduction zones creep steadily and viscously at deeper depths where temperatures and pressures are high. At the depth of the transition from earthquakes to steady creep, episodic aseismic slip is often observed. Rocks from this region are mixtures of strong, fractured clasts surrounded by a weak matrix. The observations of exhumed rocks suggest that the episodic, aseismic slip may nucleate when local frictional failure occurs in strong clasts, but the surrounding weak matrix stops this failure from generating major earthquakes. However, it is unclear how much the small‐scale rock behavior could be linked to the large‐scale slip. We use a numerical model to simulate the interplay between frictional and viscous creep and calculate the overall behavior of the subduction zone plate boundary. We explore how the slip style changes with depth and determine the transition zone's depth/temperature range. In the transition zone, a small increase in stress or decrease in strength can lead to a change from pure viscous flow to frictional sliding. This study overcomes the scale challenge between the small‐scale features preserved on outcrops and the large‐scale geophysical observations. Key Points: Frictional‐viscous flow in a two‐phase shear zone modeled by a multiscale approach occurs at ∼350–560°C with moderate fluid overpressureStress loading and/or fluid pressure weakening can cause a switch from steady viscous creep to transient frictional slipViscous creep modeled here can accommodate tectonic strain rates but not slow slip events without invoking a switch to frictional sliding [ABSTRACT FROM AUTHOR]

The hybridization technique has recently been used to produce a new generation of composites called pseudo-ductile composites, which have shown higher failure strain compared to conventional composites, minimizing the risks of the occurrence of a catastrophic failure. The pseudo-ductility behavior in these composites is obtained by hybridization of fibers with high and low failure strains. In this study, a multi-scale finite element (FE) model incorporating micro and macro-scales is proposed to predict the failure behavior of pseudo-ductile composites. A micro-scale representative volume element (RVE), consisting of randomly distributed fibers, was generated using a Python code. Periodic boundary conditions (PBCs) were applied to the RVE generated with a periodic geometry. To account for fiber failure and ply fragmentation, the tensile strength of fibers was distributed based on the Weibull distribution function and a user-defined UMAT subroutine was developed. Tensile loading was then applied to the RVE to simulate the composite's mechanical behavior. For validation, an RVE was developed based on experimental data from recent research on thinply and conventional thickness composites. Numerical results were compared to experimental data, demonstrating acceptable agreement. In the final step, following a sequential multi-scale modeling approach, a macro-scale model was constructed based on the outputs of the micro-scale model subjected to tensile and shear loads. The results were compared with experimental data, revealing good agreement. The proposed model allows for the optimization of pseudo-ductile composite structures to achieve a desired set of mechanical properties without the need for conducting extensive experimental material tests. [ABSTRACT FROM AUTHOR]

Direct-acting antiviral agents (DAAs) are known to interfere with various intracellular stages of the hepatitis C virus (HCV) life cycle and have demonstrated efficacy in treating HCV infection. However, DAA monotherapy can lead to drug resistance due to mutations. This paper explores the impact of DAA therapy on HCV dynamics using a multiscale age-structured partial differential equation (PDE) model that incorporates intracellular viral RNA replication within infected cells and two strains of viruses representing a drug-sensitive strain and a drug-resistant mutant variant, respectively. We derived an equivalent ordinary differential equation (ODE) model from the PDE model to simplify mathematical analysis and numerical simulations. We studied the dynamics of the two virus strains before treatment and investigated the impact of mutations on the evolution kinetics of drug-sensitive and drug-resistant viruses, as well as the competition between the two strains during treatment. We also explored the role of DAAs in blocking HCV RNA replication and releasing new virus particles from cells. During treatment, mutations do not significantly influence the dynamics of various virus strains; however, they can generate low-level HCV that may be completely inhibited due to their poor fitness. The fitness of the mutant strain compared to the drug-sensitive strain determines which strain dominates the virus population. We also investigated the prevalence and drug resistance evolution of HCV variants during DAA treatment. [ABSTRACT FROM AUTHOR]

We derive a reduced first-order model from a two-scale asymptotic expansion in a small parameter in order to approximate the solution of a stiff differential equation. The problem of interest is a multiscale Newton--Lorentz equation modeling the dynamics of a charged particle under the influence of a linear electric field and of a perturbed strong magnetic field. First, we show that in short times, the first-order model provides a much better approximation than the zero-order one, since it contains terms evolving at slow time scales. Then, thanks to the source-free property of the equations, we propose a volume-preserving method using a particular splitting technique to solve numerically the first-order model. Finally, it turns out that the first-order model does not systematically provide a satisfactory approximation in long times. To overcome this issue, we implement a recent strategy based on the Parareal algorithm, in which the first-order approximation is used for the coarse solver. This approach allows one to perform efficient and accurate long-time simulations for any small parameter. Numerical results for two realistic Penning traps are provided to support these statements. [ABSTRACT FROM AUTHOR]

This study focuses on the modification and intelligent-digital control of 3D printing materials. Based on the significant effect of basalt fiber in improving toughness, the influence mechanism of basalt fiber substitution rate, length and other factors on the macroscopic properties and microstructure scores of 3D printing cement-based materials was explored. The results show that under the combined effect of basalt fiber length and content, the optimal improvement is achieved with 3 % content and 8 mm length, increasing the flexural strength and compressive strength by 10.21 % (28 days) and 13.11 % (28 days), respectively. And the structure has been optimised at the micro level with an increase in type II gels. Also further use of microdata, through data fusion and refined analysis, a model (R2 > 0.85, error < 10 %) and intelligent control method that can comprehensively and accurately predict the service performance of basalt fiber modified 3D printing cement-based materials are established, which provides new development ideas for the intelligent construction and design of future building structures.

The distribution of fiber diameters plays a crucial role in the transport and sound absorbing properties of a three-dimensional random fibrous (3D-RF) medium. Conventionally, volume-weighted averaging of fiber diameters has been utilized as an appropriate microstructural descriptor to predict the static viscous permeability of 3D-RF media. However, the long wavelength acoustical properties of a 3D-RF medium are also sensitive to the smallest fibers, this is particularly true in the high-frequency regime. In our recent research, we demonstrated that an inverse volume-weighted averaging of fiber diameters can effectively serve as a complementary microstructural descriptor to capture the high-frequency behavior of polydisperse fibrous media. In the present work, we reexamine the identification of two representative volume elements (RVEs) which relies on the reconstruction of 3D-RF microstructures having volume-weighted and inverse-volume weighted averaged fiber diameters, respectively in the low-frequency and high frequency regimes. We investigate the implication of such a weighting procedure on the transport and sound absorbing properties of polydisperse fibrous media, highlighting their potential advantages. Furthermore, we discuss the challenges associated with this research field. Finally, we provide a brief perspective of the future directions and opportunities for advancing this area of study.

The interference of direct‐acting antiviral agents (DAAs) within various steps of the life cycle of hepatitis C virus (HCV) results in a high cure rate for chronic HCV infection. To accurately quantify the effect of DAAs on treatment and to achieve an optimal drug combination for treatment, we formulate an age‐structured multiscale HCV model. This model incorporates intracellular virus RNA replication process corresponding to the mechanism of drug action, two modes of extracellular virus transmission (virus‐to‐cell infection and cell‐to‐cell infection), and antibody immune response. We prove that the threshold dynamics of the pre‐treatment model is completely determined by the basic reproduction number R0$$ {\mathcal{R}}_0 $$ and the antibody immune reproduction number RW$$ {\mathcal{R}}_W $$. Under reasonable assumptions, we obtain long‐term and short‐term approximations for post‐treatment viral loads and identify which approximation performs better under various scenarios. Numerical simulations show that the infection mode of cells mainly affects the reduction of viral load in the third stage, while the antibody immune response is mainly manifested in the clearance of viral particles in the first stage of viral load decrease. With effective treatments, blocking cell‐to‐cell infection and enhancing the antibody immune response can effectively reduce the viral load in a short time period achieving the eradication of infection. Our findings suggest that the HCV infection is highly dynamic post‐DAAs treatment, and early monitoring of viral load decline contributes to the implementation of subsequent treatment regimens. [ABSTRACT FROM AUTHOR]

The study of polymers has continued to gain substantial attention due to their expanding range of applications, spanning essential engineering fields to emerging domains like stretchable electronics, soft robotics, and implantable sensors. These materials exhibit remarkable properties, primarily stemming from their intricate polymer chain network, which, in turn, increases the complexity of precisely modeling their behavior. Especially for modeling elastomers and their fracture behavior, accurately accounting for the deformations of the polymer chains is vital for predicting the rupture in highly stretched chains. Despite the importance, many robust multiscale continuum frameworks for modeling elastomer fracture tend to simplify network deformations by assuming uniform behavior among chains in all directions. Recognizing this limitation, our study proposes a multiscale fracture model that accounts for the anisotropic nature of elastomer network responses. At the microscale, damage in the chains is assumed to be driven by both the chain's entropy and the internal energy due to molecular bond distortions. In order to bridge the stretching in the chains to the macroscale deformation, we employ the maximal advance path constraint network model, inherently accommodating anisotropic network responses. As a result, chains oriented differently can be predicted to exhibit varying stretch and, consequently, different damage levels. To drive macroscale fracture based on damages in these chains, we utilize the micromorphic regularization theory, which involves the introduction of dual local‐global damage variables at the macroscale. The macroscale local damage variable is obtained through the homogenization of the chain damage values, resulting in the prediction of an isotropic material response. The macroscale global damage variable is subjected to nonlocal effects and boundary conditions in a thermodynamically consistent phase field continuum formulation. Moreover, the total dissipation in the system is considered to be mainly due to the breaking of the molecular bonds at the microscale. To validate our model, we employ the double‐edge notched tensile test as a benchmark, comparing simulation predictions with existing experimental data. Additionally, to enhance our understanding of the fracturing process, we conduct uniaxial tensile experiments on a square film made up of polydimethylsiloxane (PDMS) rubber embedded with a hole and notches and then compare our simulation predictions with the experimental observations. Furthermore, we visualize the evolution of stretch and damage values in chains oriented along different directions to assess the predictive capacity of the model. The results are also compared with another existing model to evaluate the utility of our model in accurately simulating the fracture behavior of rubber‐like materials. [ABSTRACT FROM AUTHOR]

Accurate modeling of mass front evolution in fixed beds is determined by considering equilibrium data for adsorbed component concentrations. While incorporating thermodynamic-based adsorption isotherms is crucial, their computational demands are high. Thus, pattern recognition methods offer an efficient solution for applying detailed isotherm models. Here, we employ a surrogate model output in the mass balance equations while solving partial differential equations describing column mass fronts, resulting in a hybrid model via a sequential identification approach. We examine lysozyme's mass front behavior in a silica-packed-bed column across pH 6 to pH 10 in a case study using various ions (Cl - , Br - , and I - ). We establish a Deep Neural Network (DNN) to train a surrogate model using a dataset from a modified non-linear Poisson–Boltzmann equation, incorporating the ionic dispersion potential from Lifshitz Theory. The surrogate model training was archived with 12 hidden layers and used 24,000 pseudo-experimental points (70/30) to exhibit a 0.61% absolute percentage error and R 2 of 0.9999 for test points. The hyper-parameter optimization was essential for the best parity plot results. Results indicate protein elution near isoelectric points due to reduced surface charge density. Additionally, we model mass fronts in a fixed bed at pH 9.0 for different salts. Retention time decreases, and mass front compressibility increases in the order I - > Br - > Cl - due to varying anion polarizability. Results show that elution profiles follow symmetrical behavior, even at higher protein concentration, due to electrolyte concentration gradient. These results demonstrate successful cross-scale linking using DNN. [ABSTRACT FROM AUTHOR]

Thermal damage assessment of cementitious composites is essential for evaluating post-fire health conditions of the engineering structures, as well as the basis for reinforcement and repair after fires. Fibers and fly ash are widely used in cementitious composites due to their excellent properties. However, quantifying and predicting the thermal damage of hybrid fibers reinforced cementitious composites blended with fly ash at high temperatures is still inexplicit. Hence, this study aims to realize multiscale prediction of thermal damage for hybrid fibers reinforced cementitious composites blended with fly ash at high temperatures. First, the volumes of the phase compositions during hydration and dehydration are calculated by the hydration of cement and fly ash and the dehydration of hydration products. Then, a multiscale model is established to predict the thermal damage of hybrid fibers reinforced cementitious composites and verified by the experimental data. At last, the temperature field of tunnel lining structure in fires is obtained by numerical modeling and employing it to predict thermal damage at different thicknesses and moments. Results show that the heating rate determines the dehydration degree of hydration products and the volumes of the phase composites at high temperatures. The proposed multiscale model can reflect the thermal microcracking of cement paste, the interfacial thermal damage between aggregates and the cement paste, and the deterioration of elastic modulus of fibers. After three hours of exposure to fires, serious damage appears at the surface and the thickness of 2 cm and 5 cm of the lining, while there is nearly no damage at a thickness of 30 cm or more. [ABSTRACT FROM AUTHOR]

Hydrogels are soft polymeric materials with promising applications in biomedical fields. Understanding their fracture behavior is crucial for optimizing device design and performance. However, predicting hydrogel fracture is challenging due to the complex interplay between material properties and environmental factors. In this study, a machine learning (ML) approach to predict hydrogel fracture behavior is presented. A multiscale hydrogel fracture model is developed to generate simulation data, which is used to train a predictive neural network model. The ML model utilizes a hierarchical architecture of convolution long short‐term memory units to capture spatial and temporal dependencies in the data. Model predictions are found to closely match simulation results with high accuracy, demonstrating the ability to learn complex fracture processes. Comparison of crack lengths shows the model can generalize across different material parameters. This work highlights the potential of ML for advancing the understanding of hydrogel fracture and soft matter failure. The presented approach provides an efficient framework for predicting fracture in complex materials and systems. [ABSTRACT FROM AUTHOR]

Abstract Subduction interfaces exhibit various slip styles, including slow slip events (SSEs). We use a micromechanics‐based approach to calculate the effective rheology of a shear zone containing ellipsoidal amphibolite clasts deforming by dislocation creep within an interconnected linear‐viscous phyllosilicate‐dominated matrix. Frictional failure occurs if local stress exceeds Mohr‐Coulomb yield strength. At moderate fluid overpressure, mixed‐frictional‐viscous behavior emerges at ∼350–560°C, consistent with a broad zone of mixed fault slip behavior without requiring extreme fluid overpressures. Increasing stress in this transition zone promotes local frictional failure and raises bulk strain rate. If, however, the bulk strain rate increases by more than one order of magnitude, system‐wide frictional sliding becomes preferable. This strain rate increase is insufficient to explain the slip rates observed in geophysically detectable SSEs. Therefore, viscous matrix flow as modeled here cannot explain SSEs without either invoking dynamic weakening within a frictional‐viscous flow or a mechanism switch to dominantly frictional sliding.

In this study, a multiscale model is developed through secondary development (UMAT and UEXTERNALDB) in Abaqus with the objective of simulating the thermal deformation process with dynamic recrystallization behavior. The model couples the finite element method (FEM) with the multiphase field model (MPFM), thereby establishing bidirectional coupling between macroscopic mechanical behavior and microstructural evolution. A comparison between the single-element hot compression simulation and experimental results demonstrates that the model accurately simulates both the macroscopic mechanical behavior and microstructural evolution during the thermal deformation process, thereby exhibiting high precision. Simulations of the reduction pretreatment (RP) process under different reduction amounts and billet surface temperatures demonstrate that increasing the reduction amount and billet surface temperature significantly enhances both plastic deformation and the volume fraction of dynamic recrystallization in the billet core. This results in the closure of core voids and the refinement of the core microstructure, thereby providing valuable guidance for the development of optimal reduction pretreatment (RP) processes.

Simple Summary: The Atlantic herring is one of many migratory fish that may use the geomagnetic field to navigate on its annual migration. The exact mechanism used for detecting the geomagnetic field in fish is still an open discussion, and the two main theories on magnetic sensing in animals are in the main focus: magnetite-based or radical pair-based. Here, we explore whether the cryptochrome 4 protein of fish would be able to carry out the necessary electron transfer activation to create a radical pair to be used for magnetic sensing. Marine fish migrate long distances up to hundreds or even thousands of kilometers for various reasons that include seasonal dependencies, feeding, or reproduction. The ability to perceive the geomagnetic field, called magnetoreception, is one of the many mechanisms allowing some fish to navigate reliably in the aquatic realm. While it is believed that the photoreceptor protein cryptochrome 4 (Cry4) is the key component for the radical pair-based magnetoreception mechanism in night migratory songbirds, the Cry4 mechanism in fish is still largely unexplored. The present study aims to investigate properties of the fish Cry4 protein in order to understand the potential involvement in a radical pair-based magnetoreception. Specifically, a computationally reconstructed atomistic model of Cry4 from the Atlantic herring (Clupea harengus) was studied employing classical molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) methods to investigate internal electron transfers and the radical pair formation. The QM/MM simulations reveal that electron transfers occur similarly to those found experimentally and computationally in Cry4 from European robin (Erithacus rubecula). It is therefore plausible that the investigated Atlantic herring Cry4 has the physical and chemical properties to form radical pairs that in turn could provide fish with a radical pair-based magnetic field compass sensor. [ABSTRACT FROM AUTHOR]

In this paper, a multiscale model is developed for the mass functionally graded (FG) beam-fluid system to investigate its static and dynamic responses based on 3D printed porous beam free vibration tests, which are determined by two aspects. At the microstructural level, the gradient variation is realized by arbitrary distribution of matrix pores, and the effective moduli under specific distribution are obtained using the micromechanics homogenization theory. In the meantime, at the structural level, the mechanical responses of FG porous beams subjected to mass loading are considered in a static fluid environment. Then, the explicit expressions of local finite-element (FE) expressions corresponding to the static and dynamic responses are given in the appendices. The present results are validated against numerical and experimental results from the literature and mechanical tests of 3D printed structures, with good agreement generally obtained, giving credence to the present model. On this basis, a comprehensive parametric study is carried out, with a particular focus on the effects of boundary conditions, fluid density, and slenderness ratio on the bending and vibration of FG beams with several different gradations. [ABSTRACT FROM AUTHOR]

Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on a multiscale gated convolutional neural network (MGCNN). The proposed method allows for the quantitative inference of the maximum wear depth (MWD) of sliding bearings based on online vibration signals. The constructed model adopts a dual-path parallel structure in both the time and frequency domains to process bearing vibration signals, ensuring the integrity of information transmission through residual network connections. In particular, a multiscale gated convolution (MGC) module is constructed, which utilizes convolutional network layers to extract features from sample sequences. This module incorporates multiple scale channels, including long-term, medium-term, and short-term cycles, to fully extract information from vibration signals. Furthermore, gated units are employed to adaptively assign weights to feature vectors, enabling control of information flow direction. Experimental results demonstrate that the proposed method outperforms the traditional CNN model and shallow machine learning model, offering promising support for equipment condition monitoring and predictive maintenance. [ABSTRACT FROM AUTHOR]

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Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently. [ABSTRACT FROM AUTHOR]

Summary: A multiscale model for saturated porous media is proposed, based on the concept of representative volume element (RVE). The physics between macro and micro‐scales is linked in terms of virtual power measures given by the general theory of poromechanics. Then, applying the so‐called Principle of Multiscale Virtual Power, together with suitable admissible constraints on micro‐scale displacement and pore pressure fields, a well‐established variational framework is obtained. This setting allows deriving the weak form of micro‐scale balance equations as well as the homogenization rules for the macro‐scale stress‐like variables and body forces. Whenever the micro‐scale mechanical constitutive functionals admit, as input arguments, the full‐order expansion of the micro‐scale pore pressure field, a size effect is inherited on the macro‐scale material response. The current literature attributes this issue to the so‐called "dynamical" or "second‐order" term of the homogenized flux velocity. It has been commonly suggested that the influence of this term is negligible by assuming infinitely small micro‐scale dimensions. However, such an idea compromises the fundamental notion of the existence of RVE for highly heterogeneous media. In this work, we show that the micro‐scale size dependence can be consistently eliminated by a simple constitutive‐like assumption. Accordingly, slight and selective redefinitions in the input arguments of micro‐scale material laws are proposed, leading to a constitutive formulation that allows the combination of micro‐scale variables with different orders of expansion. Just at this specific (constitutive) level, a reduced‐order expansion is selectively adopted for the micro‐scale pore pressure field. Thus, the RVE notion is restored while still retaining the major effects of the "dynamical" component of the homogenized flux velocity. The proposed formulation is implemented within a finite element squared (FE2$$ {}^2 $$) environment. Some numerical experiments are presented showing the viability of the methodology, including comparisons against analytical, mono‐scale and DNS solutions. [ABSTRACT FROM AUTHOR]

Direct-acting antiviral agents (DAAs) have shown higher cure rates for treating hepatitis C virus (HCV) by directly interfering with different steps of the HCV life cycle. To optimize drug combinations and accurately quantify the antiviral effect of DAAs treatments, mathematical models should include the intracellular virus replication processes. In this study, we develop a two-strain multiscale age-structured model that includes intracellular viral RNA replication and extracellular viral infection. We prove the existence, positivity, and boundedness of the solution, and derive the conditions for the existence and stability of the three steady states (non-infection steady state, boundary steady state, and coexistence steady state). Under certain biologically reasonable assumptions, we obtain the approximate solutions of the viral load decline of the two virus strains (sensitive and drug-resistant virus strains) during treatment, and the long-term approximation is shown to be in good agreement with the solution of the original model. We also carry out numerical simulations using the long-term approximate solution, providing insights into the influence of antiviral effects on the viral load change of the two virus strains during treatment with DAAs. • A multiscale mixed-ODE-PDE model is developed to study HCV infection. • The model includes intracellular RNA replication and extracellular viral infection. • We studied the existence and stability of the three steady states. • The approximate analytical solution is convenient for data comparison. [ABSTRACT FROM AUTHOR]

Chemical looping combustion (CLC) offers innovative carbon capture via oxygen carrier (OC) circulation between separate reactors. Circulating fluidized beds facilitate OC transport between fuel and air reactors for reduction and oxidation. By avoiding direct contact between fuel and air, minimal energy is required for CO2 separation. Disadvantages resulting from the conventional circulating fluidized bed system are the additional energy necessary for OC transportation and the challenge to operate at elevated pressure. A novel approach to CLC is proposed by considering a packed bed system with stationary OC particles undergoing periodic reduction and oxidation stages by shifting feed gas streams. The major design benefit lies in the prospect of process operation at high pressure. Finding optimal operating conditions is a mandatory step prior to implementing the process at industrial level. In this work, COMSOL Multiphysics was used for computational fluid dynamics (CFD) modelling in order to simulate the syngas-based CLC process with ilmenite OC in a packed bed reactor configuration. Mass, momentum and heat transfer mechanisms were accounted for to describe the combustion, purge and regeneration stages within a bed of randomly packed spherical OC particles at both macro- and micro-scale dimensions. The scope of the particle-resolved CFD multiscale model with realistic bed morphology was the in-depth analysis of the intraparticle phenomena occurring during the redox reactions, with emphasis on heat transport. Model results agree with published literature and provide additional understanding regarding the process in order to contribute towards the design of a flexible and energy efficient power plant concept. [ABSTRACT FROM AUTHOR]

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains. [ABSTRACT FROM AUTHOR]

Large Eddy Simulations (LESs) use Sub-Grid Scale (SGS) models to account for the effects of the unresolved scales of turbulence. The complex processes that occur in the small scales make the development of SGS models challenging. This complexity is even compounded in the presence of multiphase physics due to the mutual interactions between the small-scale hydrodynamics and the dispersed phase distribution and behaviour. In this study, we propose to avoid using an SGS model and demonstrate a novel technique to use a Periodic Box (PB) Direct Numerical Simulation (DNS) solver to find and represent the local SGS turbulence for supplementing a LES. This technique involves matching the local characteristic strain rate in the LES with the large-scale characteristic strain rate in the PB DNS. For simplicity, we assume Homogeneous Isotropic Turbulence (HIT) to be a good representation of SGS turbulence. For a test case, viz. HIT, we compare the averaged turbulence spectra from the LES and the PB DNS with the exact solution from a full DNS simulation. The results show an almost seamless coupling between the large and small scales. As such, this model is more accurate than the common Smagorinsky model in describing the properties of small scales while working within the same assumptions. Further, the effective Smagorinsky constant predicted by our model and the DNS simulation agree. Finally, a two-way coupling is introduced where an effective viscosity is computed in the PB DNS and supplied back to the LES. The results show a definitive improvement in the LES while maintaining stability. The findings showcase the capability of a PB DNS to support a LES with a near-exact simulation of the SGS turbulence. [ABSTRACT FROM AUTHOR]

When a steel box girder is constructed using the jacking method, the contact area between the jack and the bottom of the girder is subjected to complex forces, and it is very critical to ensure the local stability of the girder. When the phenomenon of unsynchronized jacking occurs, it will lead to changes in the contact area and affect the structural safety. In order to solve the above problems, this paper takes the background of the incremental launching construction of the main bridge across the Yellow River on Jiao Ping Expressway, adopts the Midas FEA NX 2021 finite element software to establish a finite element hybrid unit model under the maximum cantilever condition for the first time, and analyzes the local stresses in this state. The results show that the local maximum equivalent stress of the steel box girder is 198.301 MPa, which meets the requirements. The effect of jacking asynchrony on the structural forces is analyzed by simulating jacking asynchrony in the local model. The results show that both vertical jacking asynchrony and lateral deflection will lead to an increase in local stresses in the steel box girder and even steel yielding. On the basis of the above single-parameter study, a two-parameter correlation analysis is carried out to obtain the two-parameter control equation of jacking, the control threshold of the vertical jacking height difference is formulated to be 15 mm, and the dynamic control of lateral deflection is realized according to the control equation. Through comparison, it is found that the two-parameter control threshold of jacking synchronization is reduced, which can supplement the unfavorable state missed during single-parameter control and is a safer and more effective means of control. [ABSTRACT FROM AUTHOR]

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains.

DNA-based biosensors have played a huge role in many areas, especially in current global coronavirus outbreak. However, there is a great difficulty in the characterization of piezoelectric and flexoelectric coefficients of the nanoscale DNA film, because the existing experimental methods for hard materials are almost invalid. In addition, the relevant theoretical models for DNA films only consider a single effect without clarifying the difference between the two electromechanical effects on device detection signals. This work aims to present multiscale models for DNA-microcantilever experiments to clarify the competitive mechanism in piezoelectric and flexoelectric effects of DNA films on detection signals. First, a Poisson-Boltzmann (PB) equation is used to predict the potential distribution due to the competition between fixed phosphate groups and mobile salt ions in DNA films. Second, a macroscopic piezoelectric/flexoelectric constitutive equation of the DNA film and a mesoscopic free energy model of the DNA solution are combined to analytically predict the electromechanical coefficients of the DNA film and the relevant microcantilever signals by the deformation equivalent method and Zhang's two-variable method. Finally, the effects of detection conditions on microscopic interactions, electromechanical coupling coefficients, and deflection signals are studied. Numerical results not only agree well with the experimental observations, but also reveal that the piezoelectric and flexoelectric effects of the DNA film should be equivalently modeled when interpreting microcantilever detection signals. These insights might provide opportunities for the microcantilever biosensor with high sensitivity. [ABSTRACT FROM AUTHOR]

Models of electrical activation and recovery in cardiac cells and tissue have become valuable research tools, and are beginning to be used in safety-critical applications including guidance for clinical procedures and for drug safety assessment. As a consequence, there is an urgent need for a more detailed and quantitative understanding of the ways that uncertainty and variability influence model predictions. In this paper, we review the sources of uncertainty in these models at different spatial scales, discuss how uncertainties are communicated across scales, and begin to assess their relative importance. We conclude by highlighting important challenges that continue to face the cardiac modelling community, identifying open questions, and making recommendations for future studies. This article is part of the theme issue Uncertainty quantification in cardiac and cardiovascular modelling and simulation.

This paper presents a mathematical analysis on our proposed physiologically structured PDE model that incorporates multiscale and nonlinear features. The model accounts for both mutated and healthy populations of quiescent and proliferating cells at the macroscale, as well as the microscale dynamics of cell cycle proteins. A reversible transition between quiescent and proliferating cell populations is assumed. The growth factors generated from the total cell population of proliferating and quiescent cells influence cell cycle dynamics. As feedback from the microscale, Cyclin D/CDK 4-6 protein concentration determines the transition rates between quiescent and proliferating cell populations. Using semigroup and spectral theory, we investigate the well-posedness of the model, derive steady-state solutions, and find sufficient conditions of stability for derived solutions. In the end, we executed numerical simulations to observe the impact of the parameters on the model's nonlinear dynamics.

The paper proposes a three-scale binary medium-based constitutive model on the basis of the meso structures and micro components to describe the elasto-plastic mechanical behavior of mudstone samples. Based on the breakage mechanism of geomaterials, mudstone samples are considered as two different materials (bonded and frictional elements) at mesoscales. From micro to meso scales, given the similar but different mineralogy composition and porosity of the bonded and frictional elements at microscale, as well as their separate mechanical characteristics, different homogenization methods are adopted to obtain their respective meso mechanical properties. At the mesoscale, in view of the unique meso structures and the continuous material transformation, the extended self-consistent scheme (SCS) is improved to be adaptable to elasto-plastic composites with varying meso components. With the consideration of the evolution form of the breakage ratio under the external loading being given based on the assumed strength distribution of the meso bonded elements, the mechanical relations between meso and macro scales are established. Finally, on the basis of the mean-field method and combined with the critical mechanical connections between different scales, the micro-meso-macro constitutive model for mudstone samples are proposed. The model validation shows that, with a few model parameters, the proposed model can well reflect the stress and deformation features of mudstone samples with complex micro-components.

This paper presents a mathematical analysis on our proposed physiologically structured PDE model that incorporates multiscale and nonlinear features. The model accounts for both mutated and healthy populations of quiescent and proliferating cells at the macroscale, as well as the microscale dynamics of cell cycle proteins. A reversible transition between quiescent and proliferating cell populations is assumed. The growth factors generated from the total cell population of proliferating and quiescent cells influence cell cycle dynamics. As feedback from the microscale, Cyclin D/CDK 4-6 protein concentration determines the transition rates between quiescent and proliferating cell populations. Using semigroup and spectral theory, we investigate the well-posedness of the model, derive steady-state solutions, and find sufficient conditions of stability for derived solutions. In the end, we executed numerical simulations to observe the impact of the parameters on the model's nonlinear dynamics. [ABSTRACT FROM AUTHOR]

Sliding bearings are vital components in modern industry, exerting a crucial influence on equipment performance, with wear being one of their primary failure modes. In addressing the issue of wear diagnosis in sliding bearings, this paper proposes an intelligent diagnostic method based on a multiscale gated convolutional neural network (MGCNN). The proposed method allows for the quantitative inference of the maximum wear depth (MWD) of sliding bearings based on online vibration signals. The constructed model adopts a dual-path parallel structure in both the time and frequency domains to process bearing vibration signals, ensuring the integrity of information transmission through residual network connections. In particular, a multiscale gated convolution (MGC) module is constructed, which utilizes convolutional network layers to extract features from sample sequences. This module incorporates multiple scale channels, including long-term, medium-term, and short-term cycles, to fully extract information from vibration signals. Furthermore, gated units are employed to adaptively assign weights to feature vectors, enabling control of information flow direction. Experimental results demonstrate that the proposed method outperforms the traditional CNN model and shallow machine learning model, offering promising support for equipment condition monitoring and predictive maintenance.