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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]

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]

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 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]

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]

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. [ABSTRACT FROM AUTHOR]

Structured catalytic reactors are enjoying an increasingly important role in the reaction engineering world. At the same time, there are large and growing efforts to use advanced computational models to describe such reactors. The structured reactor represents a multi-scale problem that is typically modelled at the largest scale only, with sub-models being used to improve the model granularity. Rather than a literature review, this paper provides an overview of the key factors that must be considered when choosing these sub-models (or scale bridges). The example structured reactor selected for illustration purposes is the washcoated honeycomb monolith design. The sub-models reviewed include those for pressure drop, inter- and intra-phase mass and heat transfer, and effective thermal conductivity. [ABSTRACT FROM AUTHOR]

Insects fall prey to the Venus flytrap (Dionaea muscipula) when they touch the sensory hairs located on the flytrap lobes, causing sudden trap closure. The mechanical stimulus imparted by the touch produces an electrical response in the sensory cells of the trigger hair. These cells are found in a constriction near the hair base, where a notch appears around the hair's periphery. There are mechanosensitive ion channels (MSCs) in the sensory cells that open due to a change in membrane tension; however, the kinematics behind this process is unclear. In this study, we investigate how the stimulus acts on the sensory cells by building a multi-scale hair model, using morphometric data obtained from μ -CT scans. We simulated a single-touch stimulus and evaluated the resulting cell wall stretch. Interestingly, the model showed that high stretch values are diverted away from the notch periphery and, instead, localized in the interior regions of the cell wall. We repeated our simulations for different cell shape variants to elucidate how the morphology influences the location of these high-stretch regions. Our results suggest that there is likely a higher mechanotransduction activity in these 'hotspots', which may provide new insights into the arrangement and functioning of MSCs in the flytrap. Dataset: 10.3929/ethz-b-000448954 [ABSTRACT FROM AUTHOR]

The purpose of this work is to find an effective image segmentation method for lab-based micro-tomography (µ-CT) data of carbon fiber reinforced polymers (CFRP) with insufficient contrast-to-noise ratio. The segmentation is the first step in creating a realistic geometry (based on µ-CT) for finite element modelling of textile composites on meso-scale. Noise in X-ray imaging data of carbon/polymer composites forms a challenge for this segmentation due to the very low X-ray contrast between fiber and polymer and unclear fiber gradients. To the best of our knowledge, segmentation of µ-CT images of carbon/polymer textile composites with low resolution data (voxel size close to the fiber diameter) remains poorly documented. In this paper, we propose and evaluate different approaches for solving the segmentation problem: variational on the one hand and deep-learning-based on the other. In the author's view, both strategies present a novel and reliable ground for the segmentation of µ-CT data of CFRP woven composites. The predictions of both approaches were evaluated against a manual segmentation of the volume, constituting our "ground truth", which provides quantitative data on the segmentation accuracy. The highest segmentation accuracy (about 4.7% in terms of voxel-wise Dice similarity) was achieved using the deep learning approach with U-Net neural network. [ABSTRACT FROM AUTHOR]

Insects fall prey to the Venus flytrap ( Dionaea muscipula ) when they touch the sensory hairs located on the flytrap lobes, causing sudden trap closure. The mechanical stimulus imparted by the touch produces an electrical response in the sensory cells of the trigger hair. These cells are found in a constriction near the hair base, where a notch appears around the hair's periphery. There are mechanosensitive ion channels (MSCs) in the sensory cells that open due to a change in membrane tension; however, the kinematics behind this process is unclear. In this study, we investigate how the stimulus acts on the sensory cells by building a multi-scale hair model, using morphometric data obtained from μ-CT scans. We simulated a single-touch stimulus and evaluated the resulting cell wall stretch. Interestingly, the model showed that high stretch values are diverted away from the notch periphery and, instead, localized in the interior regions of the cell wall. We repeated our simulations for different cell shape variants to elucidate how the morphology influences the location of these high-stretch regions. Our results suggest that there is likely a higher mechanotransduction activity in these 'hotspots', which may provide new insights into the arrangement and functioning of MSCs in the flytrap.