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We introduce a mathematical model for the mechanical behaviour of the eukaryotic cell cytoskeleton. This discrete model involves a regular array of pre-stressed protein filaments that exhibit resistance to enthalpic stretching, joined at cross-links to form a network. Assuming that the inter-cross-link distance is much shorter than the length scale of the cell, we upscale the discrete force balance to form a continuum system of governing equations and deduce the corresponding macroscopic stress tensor. We use these discrete and continuum models to analyse the imposed displacement of a bead placed in the domain, characterizing the cell rheology through the force–displacement curve. We further derive an analytical approximation to the stress and strain fields in the limit of small bead radius, predicting the net force required to generate a given deformation and elucidating the dependency on the microscale properties of the filaments. We apply these models to networks of the intermediate filament vimentin and demonstrate good agreement between predictions of the discrete, continuum and analytical approaches. In particular, our model predicts that the network stiffness increases sublinearly with the filament pre-stress and scales logarithmically with the bead size. [ABSTRACT FROM AUTHOR]

Grain crushing underpins key mechanical behaviours of granular materials. A variety of factors, including grading, particle shapes and loading conditions, have been recognised to affect the crushability of grains and the overall behaviour of a granular material. Among them, the role of intermediate principal stress in a general stress condition on the shear behaviour of crushable granular sand remains less understood, owing to the scarcity of experimental data and analytical tools available. In this paper, a multi-scale computational approach is employed to investigate the shear behaviour of crushable granular sand under general stress conditions with varying intermediate principal stresses and confining pressures. The computational approach features multi-scale coupling between non-smooth contact dynamics and peridynamics, and offers a rigorous way to consider the intertwined evolution of particle size and shape during the process of grain crushing. The numerical study helps to quantify comprehensively and analyse the grain crushing-induced changes of macro- and micro-scale material behaviours including strength, deformability, particle size and shape evolution, particle-scale forces and contact conditions, and the development of anisotropy. The competition between a void-filling mechanism due to grain size change and enhanced friction and interlocking due to grain shape change in dictating the deformation of crushable sand is further discussed. The findings offer insights into the complex behaviours of crushable granular materials under general stress conditions and facilitate future development of physics-based constitutive theories on crushable sand. [ABSTRACT FROM AUTHOR]

Tidal turbine infrastructure is currently in the large-scale prototype and short-term demonstration phase. However, the immediate requirement is to develop materials, processes and long-term life predictive facilities for tidal turbine plant that has decades of operational lifetime requirements. Computational modelling is a key tool to interpret the experimental data, understand the relevant mechanisms and provide a predictive capability for the performance of aged components for industries. The goal of this paper is a prediction of the long-term life of marine-based glass/epoxy and carbon/epoxy composite laminates aged in artificial seawater with 3.5% salinity based on Arrhenius degradation theory and tensile strength retention over 180 days ageing at room temperature and 60°C. Three different analytical models (linear and exponential) were implemented to calculate time shift factors and corresponding life in a real marine environment. Additionally, multi-scale modelling has been implemented via a representative volume element approach for square and hexagonal cells, and two-step homogenization of textile composites in accordance with nanoindentation testing for matrix/resin cells and fibre constraint cells after 90 days of immersion in saltwater. In general, the multi-scale modelling in ABAQUS and TexGen4SC was able to approximate (with about 10% difference) the mechanical properties of dry and aged composite laminates. [ABSTRACT FROM AUTHOR]

The use of biocomposite materials in different fields requires enhanced knowledge of the nonlinear phenomena related to their complex mechanical behaviour. Namely, nonelastic mechanisms such as damage, plasticity, and rupture, potentially coupled with viscosity incidence, affect the structure's lifetime. In this context, the aim of this paper is to propose a simplified and accessible approach that leads to modelling the overall mechanical behaviour of the composite material. The studied laminate composites were made of thermoplastic Elium resin reinforced with flax plies. Experimental quasistatic and dynamic characterisations were conducted via tensile and cyclic tensile tests to investigate the structure's mechanical properties and corresponding failure mechanisms. The test results clearly demonstrated the presence of significant nonlinear specific coupling effects due to both flax fibre and Elium resin. The proposed multi-scale modelling analysis assumes two principal entities: the ply constituted by one or more layers of unidirectional fibres and the fibre-matrix interface considered as a surface layer of resin. Each entity was characterized by specific damage variables that were determined by mechanical tests. Model parameters were experimentally identified and expressed in the behaviour model to simulate the composite structure subjected to low-velocity impact using the Pam-Crash F.E. numerical code. [ABSTRACT FROM AUTHOR]

This research article aims to estimate the consequence of graphene sheet (GSs) reinforcement in carbon fiber composite on the elastic modulus of the composite. Aluminum (Al) is taken as matrix material. Molecular dynamics (MD) approach is employed to perform the nanoscale modeling of the GS-embedded Al nanocomposite. The resulting elastic modulus of nanocomposite is further utilized to estimate the elastic modulus of nanocomposite at micro-and macro-scale. At higher scales, i.e. micro- and macro-scale, semi-empirical and analytical methods have been utilized to calculate the elastic modulus of the composite. The elastic moduli of carbon fiber–embedded GS–Al nanocomposites are found to be enhanced relative to the pristine carbon fiber–reinforced composites, irrespective of stacked or non-stacked GSs. It is established that stacking of GSs only affects marginally the elastic moduli of carbon fiber–reinforced Al composites. [ABSTRACT FROM AUTHOR]

The austenite-ferrite transformation is the key metallurgical tool to tailor properties of low alloyed steels and remains an active area of research. Models have yet to be developed with truly predictive capabilities for phase transformations in multi-component commercial steels. This review provides a critical analysis of the various austenite-to-ferrite diffusional transformation model approaches that have been significantly broadened over the past decade by modelling at different length scales, i.e. classical macro-scale models have been augmented with simulations at the meso-scale and atomistic scale. Both semi-empirical and fundamental models are reviewed with an emphasis on polygonal ferrite formation in low and medium carbon steels. Formation of ferrite with more complex morphologies (i.e. irregular/bainitic/Widmanstätten ferrite) is also discussed. In particular, approaches to describe the interaction of alloying elements with the austenite-ferrite interface are critically analysed. The paper concludes with an outlook on the proposed austenite-ferrite transformation modelling work for the next decade. [ABSTRACT FROM AUTHOR]

The rapid development of industrial technology and the increasing computational power have provided the possibility to improve the accuracy of multiphase flow field simulation studies. In addition, the chaotic nature of boiling phenomena increases the difficulty of experimental studies, and there is an urgent need to improve the computational methods to meet the needs of industrial applications. This paper presents a comprehensive review of the published literatures on the study of pool boiling using computational methods in the last two decades, including macroscopic-scale computational methods based on continuous medium theory, mesoscopic-scale methods based on lattice Boltzmann method (LBM), and nanoscale molecular dynamics. The advantages and disadvantages of different approaches to study bubble dynamics, including nucleation mechanisms, bubble growth, bubble detachment, and nucleation sites density, are evaluated based on different modeling features and phase change mechanisms. After considering micro-layer evaporation, wall convection, and transient conduction in the macroscopic scale model, the shape diffraction of isolated bubbles and departure diameters obtained by the macroscopic approach agree well with experimental data, and the Rensselaer Polytechnic Institute model achieves promising results for the simulation of low concentration nanofluids as well. The coupling of Shan–Chen model (S–C model) and Peng–Robinson (P–R) equation of state and considering the thermal lattice Boltzmann approach can effectively solve the phase separation problem, and the simulation results can match the theoretical analysis with the highest accuracy. In addition to the above results, a complete boiling heat transfer curve was successfully simulated for the first time using the LBM method. Molecular dynamics provides an in-depth mechanistic explanation of nucleation of nanobubbles in microstructures and on different wettability surfaces in terms of free energy and pressure fluctuations. Although different methods have achieved different degrees of success in pool boiling simulations, problems of boundary capture and heat and mass transfer near macroscopic methods, mesh accuracy in mesoscopic methods, treatment of density ratios and error terms, and accuracy of gas–liquid interfaces in molecular dynamics methods still limit the development of numerical computation. Therefore, this review also presents the challenges and future directions of simulation methods for modeling at different scales from the authors' perspective. Multi-scale coupling methods will be highlighted as an important goal to accommodate the development of advanced pool boiling simulations. [ABSTRACT FROM AUTHOR]

Recently, physics-informed neural networks (PINNs) have offered a powerful new paradigm for solving problems relating to differential equations. Compared to classical numerical methods, PINNs have several advantages, for example their ability to provide mesh-free solutions of differential equations and their ability to carry out forward and inverse modelling within the same optimisation problem. Whilst promising, a key limitation to date is that PINNs have struggled to accurately and efficiently solve problems with large domains and/or multi-scale solutions, which is crucial for their real-world application. Multiple significant and related factors contribute to this issue, including the increasing complexity of the underlying PINN optimisation problem as the problem size grows and the spectral bias of neural networks. In this work, we propose a new, scalable approach for solving large problems relating to differential equations called finite basis physics-informed neural networks (FBPINNs). FBPINNs are inspired by classical finite element methods, where the solution of the differential equation is expressed as the sum of a finite set of basis functions with compact support. In FBPINNs, neural networks are used to learn these basis functions, which are defined over small, overlapping subdomains. FBINNs are designed to address the spectral bias of neural networks by using separate input normalisation over each subdomain and reduce the complexity of the underlying optimisation problem by using many smaller neural networks in a parallel divide-and-conquer approach. Our numerical experiments show that FBPINNs are effective in solving both small and larger, multi-scale problems, outperforming standard PINNs in both accuracy and computational resources required, potentially paving the way to the application of PINNs on large, real-world problems. [ABSTRACT FROM AUTHOR]

Automating the creation and calibration of microkinetic models enables researchers to perform detailed analysis of reaction mechanisms as well as multi‐scale modelling of reactors based on ab initio calculations. The example of electrochemical CO2 reduction shows that this requires a structured and standardised representation of concepts across multiple scales. Massive progress has been made in this area through dynamic knowledge graphs, whereby the first partial successes can be reported in the area of mechanism generation, quantum mechanical calculations as well as calibration with experimental data. [ABSTRACT FROM AUTHOR]

The discrete element method (DEM) is the most dominant method for the numerical prediction of dynamic behaviour at grain or particle scale. Nevertheless, due to its discontinuous nature, the DEM is inherently unable to describe microscopic features of individual bodies which can be considered as continuous bodies. To incorporate microscopic features, efficient numerical coupling of the DEM with a continuous method is generally necessary. Thus, a generalised multi-scale PD–DEM framework is developed in this work. In the developed framework, meshfree discretised Peridynamics (PD) is used to describe intra-particle forces within bodies to capture microscopic features. The inter-particle forces of rigid bodies are defined by the DEM whereas a hybrid approach is applied at the PD–DEM interface. In addition, a staggered multi-scale time integration scheme is formulated to allow for an efficient numerical treatment of both methods. Validation examples are presented and the applicability of the developed framework to capture the characteristics mixtures with rigid and deformable bodies is shown. [ABSTRACT FROM AUTHOR]

To relate the thermomechanical behaviour and microstructural evolution of the heat-assisted incremental sheet forming (SPIF) for Ti–6Al–4 V sheet, multi-scale models based on crystal plasticity finite element model (CPFEM), representative elementary volume (RVE), and cellular automaton (CA) were established in this article. An experimental-scale CPFEM was built in advance to provide macrograin level strain, strain rate, and temperature output data, which were then used in RVE and CA as inputs to simulate the microstructural evolution throughout the SPIF process. The output results include the pole figures, dynamic recrystallisation (DRX) maps, and statistics results of the alpha (α) phase of the Ti–6Al–4 V (α-Ti) alloy from stage to stage throughout the 700 ∘ C SPIF. The obtained statistics results of dislocation density, grain orientation angles, and grain size were verified with electron backscatter diffraction (EBSD). The CA analysis of the DRX progress at the upper, centre, and lower regions of the deformed workpiece has discovered that the incremental increase of the strain rate has significant effects on the dislocation density that produced a proportional relationship to the DRX percentage. The results revealed that the strain rate has strong domination in affecting the slip of grains orientation, which overcomes the effect of temperature increase in this SPIF process. Furthermore, it was found that a large amount of DRXed grains were generated at the grain boundaries, which reduced the mean grain size and increased the DRX percentage, which produced an inversely proportional relationship to the dislocation density. The thermomechanical behaviour on the SPIF workpiece was increased incrementally, which is insufficient for DRX initiation in the upper region, and the behaviour was enhanced at the centre and lower region, which can be corresponded to the microstructural evolution throughout the process. [ABSTRACT FROM AUTHOR]

We study crystalline surface evolution in extreme environments, where high-energy atoms impinge on a crystalline surface to cause sputtering, growth and surface roughening. For our model system, we study the evolution of the Ni(111) surface under Ni atom bombardment, using Molecular Dynamics (MD) simulation and a Sutton-Chen force field, where the uppermost surface layers are free to move, supported by thermostatically controlled layers above a rigid template. The MD statistics of sputtering and sticking are used to aid the development of a computationally efficient kinetic Monte Carlo (KMC) code. Comparisons between the simulation surface morphologies are used to tune the KMC growth rules so that the key statistical features of the MD structures are captured by the KMC model. This model is then employed to explore the predicted behaviour over length and times scales much larger than those accessible to MD. While the MD-KMC approach is well known, this application to the complex surface growth encountered in energetic atomic bombardment illuminates the complexities of relating atomistic events to morphological evolution. The work shows how simulation methodology can be extended to provide predictive capabilities, paving the way for design tools for engineering processes such as plasma deposition film growth. [ABSTRACT FROM AUTHOR]

Transmission models for infectious diseases are typically formulated in terms of dynamics between individuals or groups with processes such as disease progression or recovery for each individual captured phenomenologically, without reference to underlying biological processes. Furthermore, the construction of these models is often monolithic: they do not allow one to readily modify the processes involved or include the new ones, or to combine models at different scales. We show how to construct a simple model of immune response to a respiratory virus and a model of transmission using an easily modifiable set of rules allowing further refining and merging the two models together. The immune response model reproduces the expected response curve of PCR testing for COVID-19 and implies a long-tailed distribution of infectiousness reflective of individual heterogeneity. This immune response model, when combined with a transmission model, reproduces the previously reported shift in the population distribution of viral loads along an epidemic trajectory. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'. [ABSTRACT FROM AUTHOR]

In this review paper, a critical assessment of the main degradation processes in three key components of solid oxide fuel cells and electrolysers (negative and positive electrodes and the interconnect) is undertaken, attempting prioritization of respective degradation effects and recommendation of the best approaches in their experimental ascertainment and numerical modeling. Besides different approaches to quantifying the degradation rate of an operating solid oxide cell (SOC), the latest advancements in microstructural representation (3D imaging and reconstruction) of SOC electrodes are reviewed, applied to the quantification of triple‐phase boundary (TPB) lengths and morphology evolution over time. The intrinsic degradation processes in the negative (fuel) electrode and the positive (oxygen) electrode are discussed, covering first the composition and governing mechanisms of the respective electrodes, followed by a comprehensive evaluation of the most important factors of degradation during operation. By this systematic identification of dominant degradation processes, measurement techniques, and modeling approaches, the foundations are laid for the definition of meaningful accelerated stress testing of SOC cells and stacks, which will help the technology achieve the constantly more demanding durability targets in market applications. [ABSTRACT FROM AUTHOR]

As landscape‐scale conservation models grow in prominence, assessments of how wildlife utilise multiple‐use landscapes are required to inform effective conservation and management planning. Such efforts should incorporate multi‐species perspectives to maximise value for conservation, and should account for scale to accurately capture species‐environment relationships.We show that the random forest machine learning algorithm can be used to model large‐scale sign‐based data in a multi‐scale framework. We used this method to investigate scale‐dependent habitat associations for 16 mammal species of high conservation importance across the southern Kavango Zambezi (KAZA) Transfrontier Conservation Area in Botswana and Zimbabwe.Our findings revealed substantial variation in factors shaping habitat use across species, and illustrate that different species often have divergent responses to the same environmental and anthropogenic factors, and differ in the scales at which they respond to them. For all variables across all species, scale optimisation most often selected our largest scale.Precipitation, soil nutrients, and vegetation appeared to be the most important factors determining mammal distributions, likely through their associations with food resources for herbivores and, in turn, prey availability for carnivores.Anthropogenic pressures also had an important influence, with many species selecting against areas with high cattle density. The variety of relationships with human density indicated that species vary in their tolerance of humans. We found a consistent positive relationship with areas under high protection, and negative relationship with unprotected and less‐strictly protected areas.Policy implications. Through a novel application of random forest modelling to spoor data from 16 mammal species, this study highlights the importance of adopting a multi‐scale, multi‐species approach for decision‐making processes that depend on understanding wildlife distributions and habitat associations, such as protected area and corridor prioritisation. The findings identify changing rainfall patterns and increasing livestock numbers as emerging trends that may impact wildlife distributions, both within sub‐Saharan Africa and on a global scale. Wildlife management authorities should use modelling exercises and adaptive management to ensure that protected area networks remain fit for purpose under anticipated changes in rainfall under climate change, and explore initiatives that promote coexistence of wildlife and livestock. [ABSTRACT FROM AUTHOR]

Increasing efforts points to the understanding of how to maximize the capabilities of the adaptive immune system to fight against the development of immune and inflammatory disorders. Here we focus on the role of T cells as immune cells which subtype imbalance may lead to disease onset. Specifically, we propose that autoimmune disorders may develop as a consequence of a metabolic imbalance that modulates switching between T cell phenotypes. We highlight a Systems Biology strategy that integrates computational metabolic modelling with experimental data to investigate the metabolic requirements of T cell phenotypes, and to predict metabolic genes that may be targeted in autoimmune inflammatory diseases. Thus, we propose a new perspective of targeting T cell metabolism to modulate the immune response and prevent T cell phenotype imbalance, which may help to repurpose already existing drugs targeting metabolism for therapeutic treatment. [ABSTRACT FROM AUTHOR]

Architected materials and mechanical metamaterials are known for their unique macroscopic properties and complex behaviour that often defy conventional continuum mechanics. Therefore, in this contribution, a recent multi-scale second-order computational homogenisation method (Dos Santos et al., 2023) is employed to explore these materials under finite strains. The approach combines a second gradient continuum theory at the macro-scale and a representative volume element (RVE) with classical first-order continuum mechanics at the micro-scale. The Method of Multi-scale Virtual Power ensures a consistent scale transition. The predictive capability and applicability of the second-order computational strategy are evaluated through coupled multi-scale numerical simulations. These simulations involve two- and three-dimensional problems, with a strong focus on the development of novel metamaterials, while also accounting for diverse loading conditions, such as tension/compression-induced undulation, bending, and compression-induced torsion. Comparisons with first-order homogenisation and Direct Numerical Simulations validate the approach. Analysis of homogenised consistent tangents reveals valuable insights into macroscopic properties. Overall, the results highlight the capability of the second-order strategy to capture significant phenomena, including second-order deformation modes, coupling deformation mechanisms, and size effects. • A multi-scale second-order homogenisation method is used to explore metamaterials. • A second gradient continuum theory is combined with first-order continuum mechanics. • An assessment is conducted through coupled multi-scale numerical simulations. • Novel metamaterials are investigated for diverse loading conditions. • Higher-order deformation modes, coupling mechanisms and size effects are captured. [ABSTRACT FROM AUTHOR]

In this work, we investigated the effect of carbon nanotubes addition and agglomeration formation on the mechanical and electrical properties of CNT–polymer-based nanocomposites. Six specimens with carbon nanotubes (CNTs) fractions of 0%, 0.5%, 1%, 2%, 4% and 5% were manufactured and characterized by dynamic mechanical analysis (DMA) and four-probe method. The stress–strain curves and electrical conductivity properties were obtained. Scanning electron microscopy (SEM) was used to characterize both agglomeration and porosity formation. By employing micromechanics, through representative volume element (RVE), finite element analysis (FEA) and resistor network model (RNM), the Young's modulus and electrical conductivity values were calculated. The samples' elastic moduli showed an increment, reaching the maximum value at a CNTs fraction of 2%, thereafter an adverse effect was caused in the high CNT percentage samples. The final electrical conductivity seemed greatly altered with the addition of CNTs, reaching the percolation threshold at 2%. The unavoidable formation of CNT agglomerates appeared to influence the final physical properties. The CNT agglomerates adversely affect the mechanical performance of high-CNT-percentage samples. Conversely, an exponential increment in the electrical conductivity was presented as the agglomerates formed networks allowing the transport of electrons through the tunnelling effect. These phenomena were experimentally and numerically confirmed, showing a good correlation. [ABSTRACT FROM AUTHOR]

Using the notion of eigenstrain produced by the defects formed in a material exposed to high energy neutron irradiation, we develop a method for computing macroscopic elastic stress and strain arising in components of a fusion power plant during operation. In a microstructurally isotropic material, the primary cause of macroscopic elastic stress and strain fields is the spatial variation of neutron exposure. We show that under traction-free boundary conditions, the volume-average elastic stress always vanishes, signifying the formation of a spatially heterogeneous stress state, combining compressive and tensile elastic deformations at different locations in the same component, and resulting solely from the spatial variation of radiation exposure. Several case studies pertinent to the design of a fusion power plant are analysed analytically and numerically, showing that a spatially varying distribution of defects produces significant elastic stresses in ion-irradiated thin films, pressurised cylindrical tubes and breeding blanket modules. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lewis number deviates from one in the entrance length. • The catalyst activity do not modify the initial Nu and Sh inside of the channels. • Re modifies the thermal entrance length because of the reduction of the flow area. This paper is devoted to the multi-scale model of the heat and mass transfer inside of a honeycomb monolith substrate. Due to computational limitations, monoliths are modelled as a continuum in full-scale catalytic reactors models. That makes it necessary to use correlations or sub-models derived from channel scale results to account for a physically consistent heat and mass transfer inside of the substrate. In this paper detailed computational models at a channel and reactor scales are analyzed. Catalytic oxidation of CO is used as a reaction and the fluid properties are considered to be temperature-dependent. First, a channel scale model is used to analyze Nusselt, Sherwood, Lewis, and Damköhler numbers inside of the monolith channels. Secondly, sub-models obtained at a channel level are implemented in a full-scale reactor model using the continuum approach, to evaluate the impact of using detailed vs. highly simplified correlations for heat and mass transfer. The reactor scale model accounts for the transitions of the flow regime, entrance length effects, an-isotropic substrate thermal conductivity and temperature-dependent fluid properties. According to the results, the Lewis number can deviate significantly from one in the entrance length, however, it approaches asymptotically to unity as the flow develops. Regarding Nusselt and Sherwood, current interpolating methodologies are not able to predict the correct value in the entrance region when Damköhler is low, nonetheless, are reasonably accurate for the asymptotic one. [ABSTRACT FROM AUTHOR]

Paper, a porous-fibrous network, is made up of hydrophilic fibres, which are notably susceptible to deformations due to variations in moisture content. The response of paper sheets to full or partial wetting, based on the water-induced fibre swelling, has mostly been predicted in the past using highly idealised two-dimensional network geometries. The purpose of the present paper is to establish how precisely the two-stage process of determining effective hygro-expansivity under uniform wetting by a two-dimensional model followed by bending analysis using a continuum model predicts the curl of a fully three-dimensional network. In the present multi-scale modelling work, the moisture-induced homogenised sheet scale curl, derived from hygro-elastic deformations using non-uniform moisture profiles, is analysed based on an idealised non-woven three-dimensional fibrous network finite element model. First, the effective paper sheet swelling properties, derived computationally for the uniform wetting condition, are compared with analytically homogenised hygro-elastic properties. Whereas the analytically predicted elastic modulus has an inaccuracy of only ∼ 5%, the hygro-expansivity coefficient is over-predicted by roughly a third for a network with straight fibres. The difference is smaller if waviness of the fibres and wrap-around in the fibre bonds are taken into account. Fibre alignment turns out to have little influence. Subsequently, paper sheet curl predictions made by the analytical model are compared with that computed from the three-dimensional network model. The inaccuracy of the fully analytical model, based on the two-dimensional network description, relative to the fully detailed three-dimensional computational model with straight fibres is on the order of 40%–50%. The fidelity of the simple model is primarily constrained by the inaccuracy of the effective hygro-expansion coefficient. If the expansivity extracted from the three-dimensional simulations under uniform wetting is inserted, the inaccuracy drops to less than 10%. The continuum bending description hence performs adequately, provided it is based on a reliable estimation of the uniform expansion behaviour of the material. • Hygroscopic curl of paper sheets may be predicted using homogenised models. • We assess the accuracy of such predictions. • As a reference simulations of an idealised three-dimensional fibre network are used. • The predictions are accurate provided an accurate hygro-expansivity is used. • Two-dimensional network models may not be sufficient for this. [ABSTRACT FROM AUTHOR]

Ship-induced waves are an increasingly relevant hydrodynamic forcing factor in waterways travelled by large seagoing ships. The discrepancy between the small-scale wave-structure interaction near embankments and the larger-scale wave generation and propagation poses challenges for the prediction of ship-induced waves as a multi-scale problem. Therefore, a novel hydrodynamic coupling interface is presented that allows information transfer from the shallow-water-equation (SWE) solver REEF3D::SFLOW to the 3D-RANS-solver REEF3D::CFD. The implementation consists of a one-way coupling, where the solution from the SWE solver is imposed to one or multiple relaxation zones of the CFD solver. A series of verification cases shows that the implementation of the interface is accurate and only small deviations are introduced due to the 2D-3D dimensional mismatch of the numerical models involved. An application is presented, showing how the coupled SWE-CFD model can be employed to study ship-induced groin overtopping. • Presentation of a coupling interface between a SWE solver and a RANSE-solver. • The error induced by a vertical velocity assumption in the interface is found to be small. • The versatility of the approach is showcased by simulating ship-induced groin overtopping. [ABSTRACT FROM AUTHOR]

The present contribution focuses on the analysis of diverse deformation mechanisms that impact the behaviour of PC/ABS blends using computational homogenisation. This includes analysing internal particle cavitation, PC/ABS interface debonding, and PC matrix shear-yielding. The goal is to investigate the optimal composition for specific applications and create tailored materials. The work involves establishing a microstructure Representative Volume Element, defining the constitutive description of both material phases, and explicitly modelling PC/ABS interfaces and matrix damage to achieve accurate predictions. A Python programme is devised to efficiently integrate zero-thickness cohesive interface elements around ABS particles, incorporating the PPR potential-based cohesive model to characterise the interface. Additionally, the finite strain visco-elastic visco-plastic constitutive model of the PC matrix is extended to incorporate a damage variable, addressing the shear-yielding failure mechanism. The PC/ABS blend's thermomechanical response is homogenised using first-order hierarchical multi-scale analyses. The impact of considering the interface phase in the microstructure is assessed through various numerical analyses. The synergy between the constitutive models effectively captures the blend's behaviour. These findings lay the foundation for broader applicability beyond PC/ABS blends, paving the way for future studies in the field. • Development of a homogenisation framework to study the behaviour of PC/ABS blends. • Examining the individual influence and synergistic effects of the deformation mechanisms. • Determining the optimal size and mesh discretisation for Representative Volume Elements. • Analysing how interfacial and bulk material parameters impact the behaviour. • Evaluating which loading conditions are most affected by considering realistic interfaces. [ABSTRACT FROM AUTHOR]

The sensory hairs of the Venus flytrap (Dionaea muscipula Ellis) detect mechanical stimuli imparted by their prey and fire bursts of electrical signals called action potentials (APs). APs are elicited when the hairs are sufficiently stimulated and two consecutive APs can trigger closure of the trap. Earlier experiments have identified thresholds for the relevant stimulus parameters, namely the angular displacement θ and angular velocity ω . However, these experiments could not trace the deformation of the trigger hair's sensory cells, which are known to transduce the mechanical stimulus. To understand the kinematics at the cellular level, we investigate the role of two relevant mechanical phenomena: viscoelasticity and intercellular fluid transport using a multi-scale numerical model of the sensory hair. We hypothesize that the combined influence of these two phenomena and ω contribute to the flytrap's rate-dependent response to stimuli. In this study, we firstly perform sustained deflection tests on the hair to estimate the viscoelastic material properties of the tissue. Thereafter, through simulations of hair deflection tests at different loading rates, we were able to establish a multi-scale kinematic link between ω and the cell wall stretch δ . Furthermore, we find that the rate at which δ evolves during a stimulus is also proportional to ω . This suggests that mechanosensitive ion channels, expected to be stretch-activated and localized in the plasma membrane of the sensory cells, could be additionally sensitive to the rate at which stretch is applied. [ABSTRACT FROM AUTHOR]

Tissue engineered bone scaffolds are potential alternatives to bone allografts and autografts. Porous scaffolds based on triply periodic minimal surfaces (TPMS) are good candidates for tissue growth because they offer high surface-to-volume ratio, have tailorable stiffness, and can be easily fabricated by additive manufacturing. However, the range of TPMS scaffold types is extensive, and it is not yet clear which type provides the fastest cell or tissue growth while being sufficiently stiff to act as a bone graft. Nor is there currently an established methodology for TPMS bone scaffold design which can be quickly adopted by medical designers or biologists designing implants. In this study, we examine six TPMS scaffold types for use as tissue growth scaffolds and propose a general methodology to optimise their geometry. At the macro-scale, the optimisation routine ensures a scaffold stiffness within suitable limits for bone, while at the micro-scale it maximises the cell growth rate. The optimisation procedure also ensures the scaffold pores are of sufficient diameter to allow oxygen and nutrient delivery via capillaries. Of the examined TPMS structures, the Lidinoid and Split P cell types provide the greatest cell growth rates and are therefore the best candidates for bone scaffolds. [ABSTRACT FROM AUTHOR]

In this paper, a multi-scale modelling approach has been developed to predict the progressive damage and failure behaviour of 2D woven SiC/SiC composites. At the tow scale, non-linear tow properties have been determined by a micromechanics-based damage model, in which two scalar damage variables were introduced to characterize the fibre-dominated and matrix-dominated damage, respectively. Based on periodic boundary conditions, a meso-scale unit cell model has been established to simulate the macroscopic stress-strain responses and progressive damage processes of the composite under uniaxial tensile, compressive and in-plane shear loadings, respectively. In the numerical method, the non-linear properties of constituent materials have been implemented by the user defined subroutine, USDFLD of the finite element package, Abaqus. The numerical results and their comparisons with experimental stress-strain curves have been presented. The failure mechanisms of the composite under each loading have been also discussed. The high efficiency and prediction accuracy of the model make it possible to analyse large scale woven composites. [ABSTRACT FROM AUTHOR]

This study proposed a crystallographic texture-based multi-scale modelling framework to predict mechanical anisotropy of textured aluminium alloy sheet. The multi-scale scheme was constructed by the combination of a full-field crystal plasticity model in mesoscopic scale and a newly developed phenomenological yield function in continuum scale. In this approach, the mechanical anisotropy of materials is directly obtained from crystal plasticity (CP) modelling without extensive directional tensile tests. The results show that the multi-scale scheme predicted earing profiles coincide well with experimental measurements. This multi-scale modelling could be utilised to design an optimised blank shape with minimum earing for the deep-drawn component, and a convoluted cut-edge was devised accordingly. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate the effect of manufacturing induced in-plane tow misalignment and out-of-plane tow crimp on the mechanical properties of a heavy-tow unidirectional non-crimp fabric (UD-NCF) composite. The elastic constants and failure onset (strength) are predicted by employing a multiscale computational approach. Micro-scale finite element (FE) models that explicitly represent the fibers and matrix within the tow microstructure were used to predict the effective properties of the tow. Meso-scale FE models comprised of the homogenized tows and surrounding matrix were used to predict the properties of a UD NCF composite lamina. Four meso-scale models, identified as ideal, crimp, misalignment and real, were considered in this study. No manufacturing defects were represented in the ideal model, while out-of-plane crimp, in-plane misalignment and both out-of-plane crimp and in-plane misalignment were accounted for in the crimp model, misalignment model and real model, respectively. Predicted lamina stiffness based on the real model are found to be in an excellent agreement with available experimental data, which was not always the case for the other three models. The longitudinal and transverse strength predictions are found to be dependent on the chosen local failure criteria for each model. Max-stress and Puck's fiber failure criteria provide an excellent estimate of longitudinal strength while the Puck's inter-fiber failure and Tsai-Hill criteria predict transverse strength with good accuracy. The feasibility to accurately predict the mechanical properties of heavy tow non-crimp fabric composites by incorporating their inherent micro-structural defects is demonstrated in this study. [ABSTRACT FROM AUTHOR]

Multi-scale structures, as found in nature (e.g., bone and bamboo), hold the promise of achieving superior performance while being intrinsically lightweight, robust, and multi-functional. Recent years have seen a rapid development in topology optimization approaches for designing multi-scale structures, but the field actually dates back to the seminal paper by Bendsøe and Kikuchi from 1988 (Computer Methods in Applied Mechanics and Engineering 71(2): pp. 197–224). In this review, we intend to categorize existing approaches, explain the principles of each category, analyze their strengths and applicabilities, and discuss open research questions. The review and associated analyses will hopefully form a basis for future research and development in this exciting field. [ABSTRACT FROM AUTHOR]

In this work, a multi-scale model established from the concept of periodic homogenization is utilized to predict the cyclic and time-dependent response of thermoplastic-based woven composites. The macroscopic behaviour of the composite is determined from finite element simulations of the representative unit cell of the periodic microstructure, where the local non-linear constitutive laws of the components are directly integrated, namely, the matrix and the yarns. The thermoplastic matrix is described by a phenomenological multi-mechanisms constitutive model accounting for viscoelasticity, viscoplasticity and ductile damage. For the yarns, a hybrid micromechanical–phenomenological constitutive model accounting for anisotropic damage and anelasticity induced by the presence of a diffuse micro-crack network is utilized. The capabilities of the overall multi-scale model are validated by comparing the numerical predictions with experimental data. Further illustrative examples are also provided, where the composite undergoes time-dependent deformations under uni-axial and non-proportional multi-axial loading paths. The multi-scale model is also employed to analyze the influence of the local deformation processes on the macroscopic response of the composite. [ABSTRACT FROM AUTHOR]

• Multi-scale combined RVE-cohesive elements approach is accurate in reproducing the mechanical response of FDM-printed components. • The effect of the main controllable printing parameters on mechanical properties is examined experimentally and numerically. • Different damage mechanisms taking place are investigated starting from microscope observations. • Finite-Elements simulations accurately reproduce the experiments. • Homogenization technique is useful to model macroscopic 3D-printed parts by assigning homogenized properties. Mechanical strength of 3D-printed components dramatically depends on printing process parameters. These can be usually set over a relatively wide range, in combinations that determine the microstructure morphology and the resulting mechanical behaviour. The present investigation focuses on the relationship between revealed structure and resulting mechanical properties of FDM-printed ABS specimens. The peculiar structures, examined at the meso- and microscale, are modelled by a finite-element Representative Volume Element (RVE) approach, in conjunction with cohesive elements to reproduce the sealing efficiency between fused filaments. The simulation of the tensile response up to failure falls within the 95% of confidence with experiments. Also, homogenized response of RVE determines spatial material constants useful for the effective numerical simulations of functional components, and intra- and inter-layer damage mechanisms are distinguished providing hints for the structural optimization. [ABSTRACT FROM AUTHOR]

• Long-distance avian migrants across different taxa are faced with global declines. • Nightjars use general habitat characteristics for high-order habitat selection. • Fine scale selection is reliant on variables related to a habitat's suitability. • Landscape composition and -configuration both influence selection decisions. • Selecting diverse landscapes may allow individuals to minimize time during migration. Long-distance migrants are constrained by widely separated hospitable habitats in geographically isolated locations, making them vulnerable to environmental change, both through natural and anthropogenic causes. Knowledge about their resource selection decisions is imperative to understand the drivers of their declines. The distinct periods within an annual cycle, when individuals experience different environmental circumstances, are inextricably linked through carry-over effects which can have important consequences for the individual, and consequently the population. In this study, we employ precise archival GPS-tracking data of European Nightjars (Caprimulgus europaeus) and high-resolution global land cover data to examine habitat selection during the sedentary wintering and breeding periods, as well as during autumn and spring migration, using a correlational approach. We demonstrate how nightjars use general habitat characteristics, such as landscape diversity, for high-order habitat selection, while resource selection at a finer spatial scale is reliant on fine-scale variables related to a habitat's suitability, such as surface area of grassland and shrubland. We show that nightjars favour spatially diverse landscapes, which allows them to minimize time spent searching for optimal habitats. The considerable variation in the drivers of habitat selection between and within seasons shows how anthropogenic land-use change can have an array of different impacts on migrants by influencing large- and fine-scale habitat selection. This study shows the advantages of an individual based GPS-tracking approach, combined with high spatial resolution remote sensing data, and highlights the need for full annual-cycle research on scale dependent habitat selection of long-distance avian migrants. [ABSTRACT FROM AUTHOR]

• Multi-scale modelling illuminates concrete deterioration at varying length scales. • Multi-scale modelling significantly contributes to sustainable infrastructure. • Challenges like data accuracy and computational complexity necessitate advancements. • Facilitates precise performance predictions, aiding informed decisions in maintenance. The properties of concrete are degraded during service by coupled physical and chemical processes that operate at several length scales, and the prediction of its performance in engineering structures requires multi-physics, multi-scale modelling approaches. The aim of this paper is to provide a comprehensive overview of the current modelling techniques for analysis of concrete deterioration. The paper covers the fundamentals of modelling at several length scales, as well as the bridging/transition between scales, and the numerical methods based on continuum and discrete formulations appropriate to different scales. Considered are the key chemical and physical deterioration processes of carbonation, chloride ingress, freeze–thaw damage, and abrasion. The paper also reviews the validation and verification of multi-scale models and discusses future trends such as data science integration and sustainable concrete design. It is expected that the information presented here will be a valuable resource for researchers and practitioners in the field, highlighting advancements and stimulating future research in multi-scale modelling of concrete deterioration. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a numerical, micromechanical model to predict the evolution of autogenous shrinkage of hydrating cement paste at early age (up to 7 days). Autogeneous shrinkage can be important in high-performance concrete characterized by low water to cement (w/c) ratios. The occurrence of this phenomenon during the first few days of hardening may result in early-age cracking in concrete structures. A good prediction of autogeneous shrinkage is necessary to achieve better understanding of the mechanisms and the deployment of effective measures to prevent early-age cracking. Design/methodology/approach: Three-dimensional digital microstructures from the hydration modelling platform μic of cement paste were used to simulate macroscopic autogenous shrinkage based on the mechanism of capillary tension. Elastic and creep properties of the digital microstructures were calculated by means of finite element (FE) method homogenization. Autogenous shrinkage was then estimated as the average hydrostatic strain resulting from the capillary stress that was globally applied on the simulated digital microstructures. For this estimation, two approaches of homogenization technique, i.e. analytical poro-elasticity and numerical creep-superposition were used. Findings: The comparisons of between the simulated and experimentally measured deformations indicate that the creep-superposition approach is more reasonable to estimate shrinkage at different water to cement ratios. It was found that better estimations could be obtained at low degrees of hydration, by assuming a loosely packed calcium silicate hydrates (C-S-H) growing in the microstructures. The simulation results show how numerical models can be used to upscale from microscopic characteristics of phases to macroscopic composite properties such as elasticity, creep and shrinkage. Research limitations/implications: While the good predictions of some cement paste properties from the microstructure at early age were obtained, the current models have several limitations that are needed to overcome in the future. Firstly, the limitation of pore-structure representation is not only from lack understanding of C-S-H structure but also from the computational complexity. Secondly, the models do not consider early-age expansion that usually happens in practice and appears to be superimposed on an underlying shrinkage as observed in experiments. Thirdly, the simplified assumptions for mechanical simulation do not accurately reflect the solid–liquid interactions in the real partially saturated system, for example, the globally applying capillary stress on the boundary of the microstructure to find the effective deformation, neglecting water flow and the pore pressure. Last but not least, the models, due to the computational complexities, use many simplifications such as FE approximation, mechanical phase properties and creep statistical data. Originality/value: This study holistically tackles the phenomenon of autogeneous shrinkage through microstructural modelling. In a first such attempt, the authors have used the same microstructural model to simulate the microstructural development, elastic properties, creep and autogeneous shrinkage. The task of putting these models together was not simple. The authors have successfully handled several problems at each step in an elegant manner. For example, although several earlier studies have pointed out that discrete models are unable to capture the late setting times of cements due to mesh effects, this study offers the most effective solution yet on the problem. It is also the first time that creep and shrinkage have been modelled on a young evolving microstructure that is subjected to a time variable load. [ABSTRACT FROM AUTHOR]

The hybrid particle-field molecular dynamics method is an efficient alternative to standard particle-based coarse grained approaches. In this work, we propose an automated protocol for optimisation of the effective parameters that define the interaction energy density functional, based on Bayesian optimisation. The machine-learning protocol makes use of an arbitrary fitness function defined upon a set of observables of relevance, which are optimally matched by an iterative process. Employing phospholipid bilayers as test systems, we demonstrate that the parameters obtained through our protocol are able to reproduce reference data better than currently employed sets derived by Flory-Huggins models. The optimisation procedure is robust and yields physically sound values. Moreover, we show that the parameters are satisfactorily transferable among chemically analogous species. Our protocol is general, and does not require heuristic a posteriori rebalancing. Therefore it is particularly suited for optimisation of reliable hybrid particle-field potentials of complex chemical mixtures, and extends the applicability corresponding simulations to all those systems for which calibration of the density functionals may not be done via simple theoretical models. [ABSTRACT FROM AUTHOR]

Ternary Al–Mg–Si alloys have been modelled based on a multi-scale approach that spans across atomistic and mesoscale models and uses theoretically determined parameters. First, a cluster expansion model for total energy has been trained for atomistic configurations (FCC lattice) based on the data from density functional simulations of electronic structure. Free energy curves as a function of solute (Mg, Si) concentrations and disorder have been obtained by using this parameterisation together with meta-dynamics Monte Carlo sampling. In addition, free energy data, surface tensions as well as strain energy using the linear elasticity theory have been collected to be combined for a mesoscale phase-field model. The application of this approach shows that the formation of a layered MgSi phase, with (100) planes, is a particularly stable solute aggregation motif within the Al host matrix. Moreover, the phase-field model demonstrates that the preferred shape of the MgSi precipitates is needle-like (in FCC), and they can act as precursors for the important and well-known β ″-type precipitates which are formed by translating one Mg column by a 1/2 lattice vector. The results provide theoretical evidence that the solute aggregation into needle-like MgSi domains (precipitates) is an inherent property of Al-Mg-Si alloys, and that it takes place even without the presence of vacancies which is a precondition for the eventual formation β ″ precipitates. Image, graphical abstract [ABSTRACT FROM AUTHOR]

Orthotropic steel deck (OSD) is widely used in long-span cable-stayed bridges, but vehicle-induced dynamic stresses at its deck-to-rib (DTR) joints often make OSD vulnerable to local failure or fatigue damage. This paper presents a framework for vehicle-induced dynamic stress analysis of DTR joints of OSD in a cable-stayed bridge. The multi-scale finite element model of the bridge is first developed using solid elements to simulate DTR joints of OSD interacted with pavement (local model), shell elements to simulate the most vulnerable girder segment (intermediate model), and beam/truss elements to simulate the rest part of the bridge (global model). Road vehicles are modelled as a combination of several rigid bodies connected by a series of springs, dampers and pivots. The vehicle-induced dynamic stress responses at the DTR joints of a long-span cable-stayed bridge are finally computed to assess the effects of bridge dynamics, vehicle dynamics, asphalt pavement temperature, roughness and vehicle velocity. The results show that the dynamically coupled vehicle-bridge system should be considered and that pavement temperature and roughness are two profound factors affecting the stress responses of DTR joints. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic emerged in Wuhan, China, in December 2019 and caused a serious threat to global public health. In Morocco, the first confirmed COVID-19 case was reported on March 2, 2020. Since then, several non-pharmaceutical interventions were used to slow down the spread of the disease. In this work, we use a previously developed multi-scale model of COVID-19 transmission dynamics to quantify the effects of restricting population movement and wearing face masks on disease spread in Morocco. In this model, individuals are represented as agents that move, become infected, transmit the disease, develop symptoms, go into quarantine, die by the disease, or become immunized. We describe the movement of agents using a social force model and we consider both modes of direct and indirect transmission. We use the model to simulate the impact of restricting the movement of the population movement and mandating the wearing of masks on the spread of COVID-19. The model predicts that adopting these two measures would reduce the total number of cases by 64%. Furthermore, the relative incidence of indirect transmission increases when control measures are adopted. [ABSTRACT FROM AUTHOR]

Catalytic dry reforming under industrially relevant conditions of high pressures and high temperatures poses severe challenges towards catalyst materials and process engineering. The demanding conditions under which the reaction is performed lead to a coupling of reactions occurring in the gas phase and reactions which are catalyzed by the material employed as catalyst. A profound analysis of the mechanisms occurring in gas phase and resulting products from gas phase reactions is key to understanding part of the challenges that any catalyst material, irrespective of its nature, will have to cope with. The deposition of coke on an active catalyst is as well one of the most limiting factors for catalyst lifetime and catalyst activity in dry reforming. Therefore, an understanding of the thermodynamics behind coke formation and an intricate description of the mechanisms driving the evolution of coke is a vital piece of the picture. Acid‐base properties of the catalyst material and the role and nature of the active metal do also need to be considered. A large part of the review deals with mechanisms which are relevant for coke gasification and insights into materials properties, which are relevant to allow for reaction pathways along these lines. The review article focusses on research results which have been achieved using model systems – typically the analysis of model systems is a more rewarding exercise compared to fully formulated industrial catalyst systems, as here more elucidating structure‐property relationships can be drawn. Additionally the article discusses dry methane reforming in the context of alternative syngas generation technologies and attempts to create an application perspective for the reader in the context of a sustainable approach towards carbon capture and storage. [ABSTRACT FROM AUTHOR]

The article offers information on different types of raw materials. Topics include forgotten contributions to science and engineering also mark the origins of materials science; sophisticated (technical instruments to explore the world around us at the smallest and the largest length scales; and simplest version of the molecular Nano machineries.

• Neointimal hyperplasia (NIH) is a major obstacle to peripheral graft patency. • NIH progression is influenced by haemodynamic forces in the lumen. • Compliance mismatch between host vessel and graft can impact these forces. • We used computational fluid dynamics and a model of NIH to assess this impact. • We observed a small impact of compliance in the cases analysed. Neointimal hyperplasia (NIH) is a major obstacle to graft patency in the peripheral arteries. A complex interaction of biomechanical factors contribute to NIH development and progression, and although haemodynamic markers such as wall shear stress have been linked to the disease, these have so far been insufficient to fully capture its behaviour. Using a computational model linking computational fluid dynamics (CFD) simulations of blood flow with a biochemical model representing NIH growth mechanisms, we analyse the effect of compliance mismatch, due to the presence of surgical stitches and/or to the change in distensibility between artery and vein graft, on the haemodynamics in the lumen and, subsequently, on NIH progression. The model enabled to simulate NIH at proximal and distal anastomoses of three patient-specific end-to-side saphenous vein grafts under two compliance-mismatch configurations, and a rigid wall case for comparison, obtaining values of stenosis similar to those observed in the computed tomography (CT) scans. The maximum difference in time-averaged wall shear stress between the rigid and compliant models was 3.4 Pa, and differences in estimation of NIH progression were only observed in one patient. The impact of compliance on the haemodynamic-driven development of NIH was small in the patient-specific cases considered. [ABSTRACT FROM AUTHOR]

This paper represents an overview of the elements of the user-friendly simulation system, developed for computational analysis and optimization of the quality and productivity of the electromagnetically direct-chill cast semi-products from aluminium alloys. The system also allows the computational estimation of the design changes of the casting equipment. To achieve this goal, the electromagnetic and the thermofluid process parameters are coupled to the evolution of Lorentz force, temperature, velocity, concentration, strain and stress fields as well as microstructure evolution. This forms a multi-physics and multi-scale problem of great complexity, which has not been demonstrated before. The macroscopic fluid mechanics, solid mechanics, and electromagnetic solution framework is based on local strong-form meshless formulation, involving the radial basis functions and monomials as trial functions, and local collocation or weighted least squares approximation. It is coupled to the micro-scale by incorporating the point automata solution concept. The entire macro-micro solution concept does not require meshing and space integration. The solution procedure can be easily and efficiently automatically adapted in node redistribution and/or refinement sense, which is of utmost importance when coping with fields exhibiting sharp gradients, which occur in the phase-change problems. The simulation system is coded from scratch in modern Fortran. The elements of the experimental validation of the system and the demonstration of its use for round billet casting in IMPOL Aluminium Industry are shown. [ABSTRACT FROM AUTHOR]

The single crystal elastic anisotropy is one of the most important properties affecting the macroscopic response of polycrystalline materials, both in the elastic regime and the early stages of plastic deformation. In this work, the impact of monocrystalline parameters on the mechanical behaviour of cubic polycrystals is studied in detail. The analysis is conducted with an RVE-based computational homogenisation framework which includes several original strategies and criteria developed to enable the efficient statistical analysis of virtual micro-structures. In particular, these developments include a stress-driven adaptation to the usually employed strain-driven homogenisation approach, used to study the plastic polycrystalline response in stress space. The effects of number of grains and realisations considered, finite element mesh discretisation level, and algorithms used to generate both the grain morphology and orientation distribution are also assessed. Considering a number of materials with cubic symmetry, expressions for the number of grains required for an elastically isotropic response are deduced, along with bounds for the homogenised elastic stiffness components of oligocrystals, in both cases as a function of the monocrystalline elastic anisotropy. In the plastic domain, multiple microscopic and macroscopic criteria to describe the yield onset of such polycrystals are evaluated and compared. This is done for both elastically isotropic and anisotropic polycrystals, where once again the effect of different levels of monocrystalline elastic anisotropy are evaluated. The numerically derived yield surface of isotropic polycrystals is fitted to both Tresca and von Mises yield functions, and for anisotropic oligocrystals, the statistical bounds for the expected distribution of micro-yield stresses are deduced, in terms of both the number of grains and of the level of single crystal elastic anisotropy. The results obtained are thus a step towards clarifying the effect of important micro-structural parameters in establishing the minimum representative volume element (RVE) size for metallic alloys in elasto-plastic applications. • The role of monocrystalline elastic anisotropy on the macroscopic response of cubic polycrystals is quantified. • RVE size expressions (minimum number of grains) are deduced for elastic cubic polycrystals in terms of elastic anisotropy. • Elastic stiffness bounds are derived for anisotropic oligocrystals, as a function of microstructural features. • For isotropic aggregates, Tresca and von Mises yield functions are calibrated from stress-driven homogenisation analyses. • Statistical bounds for the expected distribution of micro-yield stresses are deduced for cubic oligocrystals. [ABSTRACT FROM AUTHOR]

Aim: Deforestation is rapidly altering Southeast Asian landscapes, resulting in some of the highest rates of habitat loss worldwide. Among the many species facing declines in this region, clouded leopards rank notably for their ambassadorial potential and capacity to act as powerful levers for broader forest conservation programmes. Thus, identifying core habitat and conservation opportunities are critical for curbing further Neofelis declines and extending umbrella protection for diverse forest biota similarly threatened by widespread habitat loss. Furthermore, a recent comprehensive habitat assessment of Sunda clouded leopards (N. diardi) highlights the lack of such information for the mainland species (N. nebulosa) and facilitates a comparative assessment. Location: Southeast Asia. Methods: Species–habitat relationships are scale‐dependent, yet <5% of all recent habitat modelling papers apply robust approaches to optimize multivariate scale relationships. Using one of the largest camera trap datasets ever collected, we developed scale‐optimized species distribution models for two con‐generic carnivores, and quantitatively compared their habitat niches. Results: We identified core habitat, connectivity corridors, and ranked remaining habitat patches for conservation prioritization. Closed‐canopy forest was the strongest predictor, with ~25% lower Neofelis detections when forest cover declined from 100 to 65%. A strong, positive association with increasing precipitation suggests ongoing climate change as a growing threat along drier edges of the species' range. While deforestation and land use conversion were deleterious for both species, N. nebulosa was uniquely associated with shrublands and grasslands. We identified 800 km2 as a minimum patch size for supporting clouded leopard conservation. Main conclusions: We illustrate the utility of multi‐scale modelling for identifying key habitat requirements, optimal scales of use and critical targets for guiding conservation prioritization. Curbing deforestation and development within remaining core habitat and dispersal corridors, particularly in Myanmar, Laos and Malaysia, is critical for supporting evolutionary potential of clouded leopards and conservation of associated forest biodiversity. [ABSTRACT FROM AUTHOR]

We developed a multi-scale model for the prediction of precipitation kinetics of binary alloys and present its application to the formation of Guinier–Preston zones (GPZ) in Al-rich binary Al–Cu alloys. The approach is covering three length scales: (i) Starting from density functional theory (DFT) calculations a Cluster Expansion (CE) for the binary Al–Cu system on the fcc lattice is constructed. Based on the obtained CE (ii) a lattice Monte Carlo (MC) sampling technique, known as the overlapping distribution method, is used to calculate cluster free energies of precipitate clusters. Finally (iii) a meso-scale Cluster Dynamics (CD) model is constructed by taking the cluster free energies as input. The result is a fast, parameter free method that is able to predict the evolution of precipitate size distributions. To validate the model kinetic MC simulations on the precipitation of GPZ are conducted. The results agree well with those obtained by our model. When comparing our results to experimental data a problem of time scaling arises, which is discussed in the results section. Image 1 • A model for the prediction of GPZ precipitation in Al–Cu alloys is constructed. • The approach includes DFT, Cluster Expansion, Monte Carlo and Cluster Dynamics. • The model's results show a good comparability to results from kinetic Monte Carlo. [ABSTRACT FROM AUTHOR]

When addressing the flow of concentrated suspensions composed of rods, dense clusters are observed. Thus, the adequate modelling and simulation of such a flow requires addressing the kinematics of these dense clusters and their impact on the flow in which they are immersed. In a former work, we addressed a first modelling framework of these clusters, assumed so dense that they were considered rigid and their kinematics (flow-induced rotation) were totally defined by a symmetric tensor c with unit trace representing the cluster conformation. Then, the rigid nature of the clusters was relaxed, assuming them deformable, and a model giving the evolution of both the cluster shape and its microstructural orientation descriptor (the so-called shape and orientation tensors) was proposed. This paper compares the predictions coming from those models with finer-scale discrete simulations inspired from molecular dynamics modelling. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the bending behaviour of a reinforced concrete (RC) beam with different non-uniform frost damages. A multi-scale poromechanical approach is developed to build a bridge between the material deterioration process and the structural mechanical performance by considering the global heat and moisture transport and rebar confinement, as well as the local phase changes and poromechanical deformation. The real-time frost damage process is first simulated and experimentally validated for different depths of exposure on both the compression and tension sides and then used for testing the residual bending behaviour. The failure modes, load-carrying capacity and strain distribution of the reinforcement are fairly consistent with the experimental results. The bearing capacity decreases with the increment of the freeze-thaw cycles (FTC) and the frost exposure depth. Reinforcing bars can restrain not only crack propagation but also frost damage. Thus, the bearing capacity of the damaged beam on the compression side drops more than that on the tension side for the same exposure depth. Finally, an indirect method for the use of the equivalent deteriorated mechanical properties is compared with the detailed real-time simulation, and a simple design method is proposed for RC beams with partial frost damage. [ABSTRACT FROM AUTHOR]

Coupled multi-scale finite element analyses have gained traction over the last years due to the increasing available computational resources. Nevertheless, in the pursuit of accurate results within a reasonable time frame, replacing these high-fidelity micromechanical simulations with reduced-order data-driven models has been explored recently by the modelling community. In this work, two classes of machine learning models are trained for a porous hyperelastic microstructure to predict (i) whether the microscopic equilibrium problem is likely to fail and (ii) the stress–strain response. The former may be used to identify critical macroscopic points where one may fall back to the high-fidelity analysis and possibly apply convergence bowl-widening techniques. For the latter, both a linear regression with polynomial features and artificial Neural Networks have been used, and the required stress–strain derivatives for solving the equilibrium problem have been derived analytically. A weight regularisation is introduced to stabilise the tangent operator and several strategies are discussed for imposing null stresses in undeformed configurations for both regression models. The regression techniques, here analysed exclusively in the context of porous hyperelastic materials, evidence very promising prospects to accelerate multi-scale analyses of solids under large deformation. • Methodology to speed up the high computational time of coupled multi-scale analyses. • Benchmark of classification techniques to identify critical analysis points. • Comparison of regression models to predict the stress–strain response and derivates. • New insights on strategies to enforce zero stress in absence of deformation. • Integration of classification and regression models towards a robust framework. [ABSTRACT FROM AUTHOR]