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Understanding the impact of the relativistic motion of a chiral molecule on its optical response is a prime challenge for fundamental science, but it also has a direct practical relevance in our search for extraterrestrial life. To contribute to these significant developments, we describe a multi–scale computational framework that combines quantum chemistry calculations and full–wave optical simulations to predict the chiral optical response from molecules moving at relativistic speeds. Specifically, the effect of a relativistic motion on the transmission circular dichroism (TCD) of three life–essential biomolecules, namely, B–DNA, chlorophyll a, and chlorophyll b, is investigated. Inspired by previous experiments to detect interstellar chiral molecules, we assume that the molecules move between a stationary observer and a light source, and we study the rotationally averaged TCD as a function of the speed of the molecule.We find that the TCD spectrum that contains the signatures of the molecules shifts with increasing speed to shorter wavelengths, with the effects already being visible for moderate velocities. [ABSTRACT FROM AUTHOR]

Abstract Understanding the impact of the relativistic motion of a chiral molecule on its optical response is a prime challenge for fundamental science, but it also has a direct practical relevance in our search for extraterrestrial life. To contribute to these significant developments, we describe a multi–scale computational framework that combines quantum chemistry calculations and full–wave optical simulations to predict the chiral optical response from molecules moving at relativistic speeds. Specifically, the effect of a relativistic motion on the transmission circular dichroism (TCD) of three life–essential biomolecules, namely, B–DNA, chlorophyll a, and chlorophyll b, is investigated. Inspired by previous experiments to detect interstellar chiral molecules, we assume that the molecules move between a stationary observer and a light source, and we study the rotationally averaged TCD as a function of the speed of the molecule.We find that the TCD spectrum that contains the signatures of the molecules shifts with increasing speed to shorter wavelengths, with the effects already being visible for moderate velocities.

High‐temperature electrochemical cells have received increasing interest in the past years as important elements in an energy transition scenario. Their employment in power‐to‐gas or gas‐to‐power systems naturally requires an accurate design that takes into account the effect of operating conditions and geometries on both the performance of the cells themselves and their integration with other units. To this end, it is important to develop flexible yet comprehensive models for their description and to identify performance parameters that allow a concise assessment of their efficiency. The aim of the present work is to identify the limitations – in terms of applicability and consistency – of the mathematical models developed to date to describe high‐temperature electrochemical cells, as well as the key characteristics that they should possess. A brief review of literature in this field is then reported, to identify the research areas that still need to be explored. [ABSTRACT FROM AUTHOR]

The advancement of technology has led to a significant increase in thermal loads, thus presenting new challenges in heat dissipation. Traditional single-phase cooling systems are often inadequate to meet these demands. As a result, phase-change technologies utilizing boiling and condensation, which can achieve high heat transfer coefficients, have garnered considerable attention. To delve into the complex physics of boiling heat transfer, researchers are increasingly turning to numerical simulation methods such as the Volume of Fluid (VOF) and the Diffuse Interface (DI) approaches. The VOF method, widely employed for macro-scale simulations ranging from micrometers to millimeters, effectively tracks bubble growth and detachment. Conversely, the DI method represents the interface as a continuous phase field and is primarily used for mesoscale simulations spanning from nanometers to micrometers. While the DI method excels in resolving mesoscale interfacial phenomena, it is computationally expensive for larger domains. Considering the strengths and weaknesses of both the VOF and DI methods, there is a growing interest in developing a multi-scale modeling approach that amalgamates their benefits. To pursue this objective, initial efforts are being made to evaluate the scaling capability of VOF towards lower spatial and temporal limits. Hence, an enhanced and customized VOF methodology has been developed within the OpenFOAM toolbox. This methodology is employed to investigate various bubble growth scenarios, progressively exploring its applicability at lower temporal and spatial scales to identify the lower limits of its application. By taking this first step towards combining the strengths of both the VOF and DI methods through a multi-scale modeling approach, the presented paper paves the way for enhancing the accuracy and efficiency of modelling approaches for boiling heat transfer while tackling a challenge associated with varying spatial and temporal scales. This endeavor not only pushes the boundaries of computational fluid dynamics but also holds promise for addressing real-world thermal management issues in diverse technological applications.

Abstract High‐temperature electrochemical cells have received increasing interest in the past years as important elements in an energy transition scenario. Their employment in power‐to‐gas or gas‐to‐power systems naturally requires an accurate design that takes into account the effect of operating conditions and geometries on both the performance of the cells themselves and their integration with other units. To this end, it is important to develop flexible yet comprehensive models for their description and to identify performance parameters that allow a concise assessment of their efficiency. The aim of the present work is to identify the limitations – in terms of applicability and consistency – of the mathematical models developed to date to describe high‐temperature electrochemical cells, as well as the key characteristics that they should possess. A brief review of literature in this field is then reported, to identify the research areas that still need to be explored.

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.

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]

Recent progress in our understanding of cancer mostly relies on the systematic profiling of patient samples with high-throughput techniques like transcriptomics. With this approach, one can find gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, omics data alone cannot generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational modeling is a promising approach that would benefit from protocols to integrate the data generated by the high-throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale cancer models. In the method, we conduct transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We then determine global and local sensitivity through systematic model simulations that assess the relevance of parameter variations in triggering therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in a melanoma micrometastasis. The application of the workflow identifies three distinct scenarios of therapy resistance.

Rate equation modelling is performed to simulate ${\mathrm{D_2}}$ and ${\mathrm{D_2}}+{\mathrm{D}_2^+}$ exposure of the ${\mathrm{W}(110)}$ surface with varying coverage of oxygen atoms (O) from the clean surface up to 0.75 monolayer of O. Density Functional Theory (DFT) calculated energetics are used as inputs for the surface processes and desorption energies are optimized to best reproduce the Thermal Desorption Spectrometry (TDS) experiments obtained for ${\mathrm{D_2}}$ exposure. For the clean surface, the optimized desorption energies (1.10 eV–1.40 eV) are below the DFT ones (1.30 eV–1.50 eV). For the O covered surface, the main desorption peak is reproduced with desorption energies of 1.10 eV and 1.00 eV for 0.50 and 0.75 monolayer of O respectively. This is slightly higher than the DFT predicted desorption energies. In order to simulate satisfactorily the total retention obtained experimentally for ${\mathrm{D_2}}+{\mathrm{D}_2^+}$ exposure, a sputtering process needs to be added to the model, describing the sputtering of adsorbed species (D atoms) by the incident D ions. The impact of the sputtering process on the shape of the TDS spectra, on the total retention and on the recycling of D from the wall is discussed. In order to better characterize the sputtering process, especially its products and yields, atomistic calculations such as molecular dynamics are suggested as a next step for this study.

While the modification of surface contacts offers significant potential for friction reduction, obtaining an underlying consistent friction behaviour of real-life experiments and virtual simulations is still an ongoing challenge. In particular, most works in the literature only consider idealised geometries that can be parametrised with simple analytical functions. In contrast to this approach, the current work describes the establishment of a digital twin of a pin-on-disk tribometer whose virtual geometry is completely replicated from real-life post-test topography measurements and fed into a two-scale mixed lubrication solver. Subsequently, several calibration steps are performed to identify the sensitivities of the friction behaviour towards certain geometry features and enable the digital twin to robustly represent the Stribeck curve of the physical experiments. Furthermore, a derivation of the Hersey number is used to generalise the obtained friction behaviour for different dynamic viscosities and allow the validation of the presented method. [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]

COVID-19 pandemic promoted a lot of research activities in relation to mucosalivary fluid airborne transport. Indeed, infection mechanisms are the result of mucosalivary fluid droplets exchange and the knowledge in this area is still largely inadequate. One of the main challenges concerns the modelling of mucosalivary fluid complex nature. Specifically, this is a key element to predict small diameters dry nuclei formation which are highly relevant from the transmission risk point of view. For this reason, in this paper we present and discuss the development of a new multi-scale modelling technique which incorporates the Population Balance Equation into a standard particle-source-in-cell method. Thus, the effectiveness of the aforementioned technique in droplet nuclei generation modelling is showed and discussed. Also the impact of velocity boundary conditions at the mouth print is assessed as well as the effect of the correlations for mass transfer showing that their neglect causes an underestimation in distance reached by the droplets. [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]

Laser-Based Powder Bed Fusion is one of the most widely used additive manufacturing processes, mainly due to its high-quality output. End users would greatly benefit from a virtual simulation of the process; however, the modelling of the process is very complicated and slow and therefore restricted mainly to academic users. In this work, a computationally efficient approach to the thermal modelling of PBF-LB/M is presented. This approach is based on the enthalpy method and the division of the simulation into three characteristic scales of the process. Despite the small runtime of the simulations, the model captures the critical phenomena of the process achieving sufficient accuracy. [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]

Abstract 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.

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]

Background: Repetitive transcranial magnetic stimulation (rTMS) induces long-term changes of synapses, but the mechanisms behind these modifications are not fully understood. Although there has been progress in the development of multi-scale modeling tools, no comprehensive module for simulating rTMS-induced synaptic plasticity in biophysically realistic neurons exists.., Objective: We developed a modelling framework that allows the replication and detailed prediction of long-term changes of excitatory synapses in neurons stimulated by rTMS., Methods: We implemented a voltage-dependent plasticity model that has been previously established for simulating frequency-, time-, and compartment-dependent spatio-temporal changes of excitatory synapses in neuronal dendrites. The plasticity model can be incorporated into biophysical neuronal models and coupled to electrical field simulations., Results: We show that the plasticity modelling framework replicates long-term potentiation (LTP)-like plasticity in hippocampal CA1 pyramidal cells evoked by 10-Hz repetitive magnetic stimulation (rMS). This plasticity was strongly distance dependent and concentrated at the proximal synapses of the neuron. We predicted a decrease in the plasticity amplitude for 5 Hz and 1 Hz protocols with decreasing frequency. Finally, we successfully modelled plasticity in distal synapses upon local electrical theta-burst stimulation (TBS) and predicted proximal and distal plasticity for rMS TBS. Notably, the rMS TBS-evoked synaptic plasticity exhibited robust facilitation by dendritic spikes and low sensitivity to inhibitory suppression., Conclusion: The plasticity modelling framework enables precise simulations of LTP-like cellular effects with high spatio-temporal resolution, enhancing the efficiency of parameter screening and the development of plasticity-inducing rTMS protocols., Competing Interests: Conflict of interest statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abstract 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.

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]

While the modification of surface contacts offers significant potential for friction reduction, obtaining an underlying consistent friction behaviour of real-life experiments and virtual simulations is still an ongoing challenge. In particular, most works in the literature only consider idealised geometries that can be parametrised with simple analytical functions. In contrast to this approach, the current work describes the establishment of a digital twin of a pin-on-disk tribometer whose virtual geometry is completely replicated from real-life post-test topography measurements and fed into a two-scale mixed lubrication solver. Subsequently, several calibration steps are performed to identify the sensitivities of the friction behaviour towards certain geometry features and enable the digital twin to robustly represent the Stribeck curve of the physical experiments. Furthermore, a derivation of the Hersey number is used to generalise the obtained friction behaviour for different dynamic viscosities and allow the validation of the presented method.

[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]

Cancer invasion of the surrounding tissue is a multiscale process of collective cell movement that involves not only tumour cells but also other immune cells in the environment, such as the tumour-associated macrophages (TAMs). The heterogeneity of these immune cells, with the two extremes being the pro-inflammatory and anti-tumour M1 cells, and the anti-inflammatory and pro-tumour M2 cells, has a significant impact on cancer invasion as these cells interact in different ways with the tumour cells and with the ExtraCellular Matrix (ECM). Experimental studies have shown that cancer cells co-migrate with TAMs, but the impact of these different TAM sub-populations (which can change their phenotype and re-polarise depending on the microenvironment) on this co-migration is not fully understood. In this study, we extend a previous multi-scale moving boundary mathematical model, by introducing the M1-like macrophages alongside with their exerted multi-scale effects on the tumour invasion process. With the help of this model we investigate numerically the impact of re-polarising the M2 TAMs into the anti-tumoral M1 phenotype and how such a strategy affects the overall tumour progression. In particular, we investigate numerically whether the M2→M1 re-polarisation could depend on time and/or space, and what would be the macroscopic effects of this spatial- and temporal-dependent re-polarisation on tumour invasion.

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]

COVID-19 pandemic promoted a lot of research activities in relation to mucosalivary fluid airborne transport. Indeed, infection mechanisms are the result of mucosalivary fluid droplets exchange and the knowledge in this area is still largely inadequate. One of the main challenges concerns the modelling of mucosalivary fluid complex nature. Specifically, this is a key element to predict small diameters dry nuclei formation which are highly relevant from the transmission risk point of view. For this reason, in this paper we present and discuss the development of a new multi-scale modelling technique which incorporates the Population Balance Equation into a standard particle-source-in-cell method. Thus, the effectiveness of the aforementioned technique in droplet nuclei generation modelling is showed and discussed. Also the impact of velocity boundary conditions at the mouth print is assessed as well as the effect of the correlations for mass transfer showing that their neglect causes an underestimation in distance reached by the droplets.

Laser-Based Powder Bed Fusion is one of the most widely used additive manufacturing processes, mainly due to its high-quality output. End users would greatly benefit from a virtual simulation of the process; however, the modelling of the process is very complicated and slow and therefore restricted mainly to academic users. In this work, a computationally efficient approach to the thermal modelling of PBF-LB/M is presented. This approach is based on the enthalpy method and the division of the simulation into three characteristic scales of the process. Despite the small runtime of the simulations, the model captures the critical phenomena of the process achieving sufficient accuracy.

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]

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]

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.

Featured Application: High-quality and high-throughput eco-friendly regimes of laser cutting glasses, sapphire and other transparent materials are found based on the model. Possible practical applications are in display manufacturing, the automotive and other industries. A strategy is proposed for achieving a practically important mode of laser processing—a so-called "smooth" laser filamentation cutting (LFC) of transparent materials by a moving beam of a pulse-periodic pico- or subpicosecond laser. With such cutting, the surface of the sidewalls of the cuts have a significantly improved smoothness, and, as a result, the laser-cut plates have increased resistance to damage and cracking due to mechanical impacts during their subsequent use. According to the proposed analytical model, for the case when each filament is formed only by a single laser pulse, the strategy of such cutting is defined by a set of necessary conditions, whose implementation requires, in turn, a certain selection and matching with each other of irradiation parameters (pulse duration and energy, repetition rate, pitch of filaments following) and of material parameters—thermal, optical and mechanical strength constants. The model shows good agreement with experiments on sapphire and tempered glass. Besides, LFC modes are also predicted that provide very high cutting speeds of the order of 1–25 m/s or more, or allow cutting with an extremely low average laser power, but still at a speed acceptable for practical applications. [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]

Recycled cement paste as a promising shape stabilisation support of phase change material (PCM) has been introduced to prepare cement-based phase change energy storage composite material (PCESCM) due to its advantages regarding building energy conservation and recycling construction waste. In this study, to fully consider the degrees of freedom of material design, a multi-scale framework using lattice Boltzmann method (LBM) is proposed to predict the effective thermal conductivities of hierarchical cement-based PCESCM. The representative volume elements (RVEs) of cement-based PCESCM covering nano-, micro-, and meso-scale are established capturing the complex structural characteristics. Afterwards, an LBM-based thermal conduction model is proposed to simulate the thermal conduction through the reconstructed RVEs. The results indicate that at the nano-scale the thermal conductivity of colloidal C-S-H increases with the packing density of gel particles for both saturated and dried states. At the micro-scale, the thermal conductivities of matrix and PCM composite are highly dependent on the degree of water/PCM saturation. At the meso-scale, the incorporation of PCM composites can lead to a reduction in thermal conductivity. In general, the simulated thermal conductivities of hierarchical cement-based PCESCM using the LBM-based model agree well with the measured values or the estimated results using the analytical model.

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]

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.

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]

• 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]

Brain-related experiments are limited by nature, and so biological insights are often limited or absent. This is particularly problematic in the context of brain cancers, which have very poor survival rates. To generate and test new biological hypotheses, researchers have started using mathematical models that can simulate tumour evolution. However, most of these models focus on single-scale 2D cell dynamics, and cannot capture the complex multi-scale tumour invasion patterns in 3D brains. A particular role in these invasion patterns is likely played by the distribution of micro-fibres. To investigate the explicit role of brain micro-fibres in 3D invading tumours, in this study, we extended a previously introduced 2D multi-scale moving-boundary framework to take into account 3D multi-scale tumour dynamics. T1 weighted and DTI scans are used as initial conditions for our model, and to parametrise the diffusion tensor. Numerical results show that including an anisotropic diffusion term may lead in some cases (for specific micro-fibre distributions) to significant changes in tumour morphology, while in other cases, it has no effect. This may be caused by the underlying brain structure and its microscopic fibre representation, which seems to influence cancer-invasion patterns through the underlying cell-adhesion process that overshadows the diffusion process.