Macroscopic Point Research Articles

A knowledge structure of unmet medical needs of people with disabilities

Objectives: With the increasing number of people with disabilities, the importance of their health condition has increased since they have a higher risk of noncommunicable diseases and less access to healthcare services. Many studies on the them have focused on factors influencing their unmet medical needs. However, the no study has reviewed the research trend of the unmet medical needs experience of the people with disabilities. Thus, we identified the knowledge structure of this research topic and determined its research trend. Methods: We conducted a text network analysis of the abstracts of 4723 papers published before November 2020. A total of 17,295 words remained after preprocessing. We used these words to identify the knowledge structure of the research topic, its core keywords, and change of research topic by time. Results: “Care,” “illness,” “service,” and “health” appeared as the main keywords that formed four topics. The proportion of each topic changed by time. We investigated the research field from a macroscopic point of view by looking at the overall network structure and understanding the research trends. Conclusion: We expect that this study will contribute to the establishment of a knowledge structure related to the unmet medical needs of the people with disabilities.

Experimental study of carbonate rock-oil-acid solution in the oil well acidizing from a molecular and macroscopic points of view

Experimental study of carbonate rock-oil-acid solution in the oil well acidizing from a molecular and macroscopic points of view

고전문학 기반 콘텐츠 창작의 실제

This article analyzes the assignments submitted by students in the Media Storytelling Department to the <classical literature and digital content> course and examines students' perceptions and applications of classical literature. The results of the analysis of students' assignments are as follows. First, there is no hesitation in borrowing, expanding, revising, or mash-up. There is a strong tendency to cut out some of the narratives necessary for the content without hesitation and insert them into the person-centered narrative he planned. Second, they show interest in the characteristics of the characters and the person's narrative. From a macroscopic point of view, they show the characteristic of paying more attention to small narratives centered on characters rather than dealing with the entire narrative of the classical literature. Third, we try a thematic approach to bring the social problems that are prevalent around us into their works. However, it is unfortunate that there are many cases where the planned medium is converted into web novels after feeling pressure to implement the content as a visual image. Students play two roles: learners of classical literature and creators who produce content at the same time. Classical literature dispels the preconceived notion that it is difficult and bizarre, and learns the characteristics of media and trans storytelling in the process of converting it back to various media, feeling the joy of reading classics from the realization that it is full of diverse and novel stories and characters. Instructors should encourage the diverse use of media, but guide students to recognize classical literature as valuable materials to obtain creative materials.

A microstructure-based graph neural network for accelerating multiscale simulations

Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic integration point. A plethora of surrogate modeling strategies attempt to alleviate this cost by learning to predict macroscopic stresses from macroscopic strains, completely replacing the microscale models. In this work, we introduce an alternative surrogate modeling strategy that allows for keeping the multiscale nature of the problem, allowing it to be used interchangeably with an FE solver for any time step. Our surrogate provides all microscopic quantities, which are then homogenized to obtain macroscopic quantities of interest. We achieve this for an elasto-plastic material by predicting full-field microscopic strains using a graph neural network (GNN) while retaining the microscopic constitutive material model to obtain the stresses. This hybrid data-physics graph-based approach avoids the high dimensionality originating from predicting full-field responses while allowing non-locality to arise. In addition, this approach introduces beneficial inductive bias to the model by encoding microscopic geometrical features. By training the GNN on a variety of meshes, it learns to generalize to unseen meshes, allowing a single model to be used for a range of microstructures. The embedded microscopic constitutive model in the GNN implicitly tracks history-dependent variables and leads to improved accuracy. While the microscopic stresses are fully dependent on the microscopic strains, we found it crucial to include both microscopic strains and stresses in the loss function. We demonstrate for several challenging scenarios that the surrogate can predict complex macroscopic stress–strain paths. As the computation time of our method scales favorably with the number of elements in the microstructure compared to the FE method, our method can significantly accelerate FE2 simulations.

Inverse identification of thermal behaviour of a paraffin-based phase change material in complete and partial phase change cycles

From the macroscopic point of view, phase change hysteresis (PCH) means that a phase change process (e.g. solidification) does not follow the same temperature-enthalpy path as the opposite phase change process (melting). Although the PCH is observed in most phase change materials (PCMs), it is often neglected in computational models, resulting in discrepancies when compared to experimental data. The PCH can particularly be an issue in the modelling of latent heat thermal energy storage systems, where incomplete (partial) phase transitions are quite common. Lab-scale experimental methods for characterisation of PCMs, such as differential scanning calorimetry or the temperature-history method, employ only small PCM samples, and the obtained results are often insufficient for predicting the thermal behaviour of large volumes of PCMs. The present study explores numerical modelling approaches to the PCH, addressing both complete and partial melting-to-solidification cycles. A set of validation experiments was performed, focusing on phase transitions in a paraffin-based PCM enclosed in a rectangular cavity. An inverse identification method was used to minimise the root mean square error (RMSE) of temperatures in the PCM using the particle swarm optimisation method. A two-curve approach showed the highest accuracy in complete phase change cycles, with a 62% improvement in the RMSE when compared to the manufacturer data. As for cycles with partial phase changes, a curve-scale approach showed superior behaviour, reducing the RMSE as much as 99%. Conversely, another investigated approach – a curve-track model – exhibited inferior performance, making it less suitable for the modelling of partial phase changes.

Macroscopic phenomenon of exchange coupling and microscopic effect on magnetization reversal in sintered Nd–Fe–B magnets

Permanent magnets of Nd13.5Fe79.18M1.52B5.8 and Nd13.5Fe79.76M0.94B5.8 were prepared, respectively, via strip casting, jet milling and sintering followed by annealing. By adding the non-ferromagnetic elements M (Al, Cu, Ga and Zr) into the magnets, it could not only modify the microstructure, but also regulate the exchange coupling effect in the sintered magnets. From the macroscopic point of view, the recoil loops exhibit spring behavior in Nd13.5Fe79.76M0.94B5.8, indicating that the energy barrier can be overcome by the intergranular exchange coupling. From the microcosmic point of view, the exchange coupling can increase the domain wall size by suppressing the nucleation of reversed domains, and so the activation volume increases with thermal activation. In Nd13.5Fe79.76M0.94B5.8 the exchange coupling effect is stronger, and both the coercivity of 15.0 kOe and the remanence of 14.3 kGs are a little higher than those of Nd13.5Fe79.18M1.52B5.8 magnets in which the content of non-ferromagnetic elements is a little higher and the exchange coupling effect is weaker. Thus, the exchange coupling does not decrease the coercivity due to the exchange coupling suppressing the nucleation of reversed domains, though the microstructure is inhomogeneous in the sintered magnets of Nd13.5Fe79.76M0.94B5.8. Reducing the defect size and decreasing the defect concentration should be a practical way to improve the coercivity in Nd–Fe–B permanent magnets.

Microscopic perspective of the combined collector CTAB/SPA on the flotation separation of fluorite and calcite: Adsorption morphology and interaction force

It is difficult to separate fluorite and calcite due to their similar surface properties. This article applied combined collector cetyltrimethylammonium bromide (CTAB)/styrene phosphonic acid (SPA) to separate fluorite from calcite and explored the adsorption morphology and interaction force. Flotation results indicated that effective separation between fluorite and calcite with CTAB/SPA at a molar ratio of 1:1, at the concentration of 1×10−4 mol/L and pH 11.0. Surface tension measurements indicated a synergistic effect of CTAB/SPA in solution. The combined collector increased the hydrophobicity difference between fluorite and calcite surfaces. Zeta potential measurements illustrated that both CTAB and SPA adsorbed on fluorite and calcite surfaces, with CTAB as the main collector. Atomic force microscope (AFM) analysis further demonstrated that more CTAB/SPA adsorbed on fluorite surface than that on the calcite surface, resulting in a stronger hydrophobic force between fluorite particles. It was concluded that CTAB, as the main collector in the combined collector, adsorbed on the fluorite surface, while SPA interspersed between the CTAB molecules, with an adsorption model proposed. These macroscopic and microscopic points provide a possibility for the flotation separation between fluorite and calcite.

A machine learning method of accelerating multiscale analysis for spatially varying microstructures

Multiscale computing for heterogeneous materials provides a powerful and fidelity approach to handling situations where a suitable macroscopic constitutive model is unavailable. However, there have been very few instances in the industrial sector where concurrent multiscale modeling techniques have been adopted, primarily because they require significant computational resources. In particular, when dealing with large numbers of representative volume elements (RVEs) with different microstructure morphologies throughout the macrostructure. In this paper, the novelty lies in providing an artificial neural network (ANN) model-based multiscale approach for accelerating multiscale analysis of the elliptical inclusion-reinforced composites with spatially varying microstructures, which give up the assumption of global periodicity. To tackle this challenge, firstly, a series of RVEs with different microstructure morphologies characterized by inclusion orientations and aspect ratios, under different load conditions, are simulated to generate training data. Second, the back propagation neural network (BPNN) is trained offline using data acquired from the RVE simulation for learning the underlying and possibly RVE morphologies. The trained BPNN replaces the complex microscopic mechanical solution problems over each macroscopic integration point at each iteration, to update the macroscopic stress-strain responses and it significantly reduces the computational cost of multiscale simulations. Then, the trained neural network is implemented on ABAQUS via user-defined materials (UMAT) and its effectiveness is verified by comparison with the classical multiscale finite element method (FE2) method. Finally, the outstanding computational capability of the proposed approach is further demonstrated through two numerical examples.

Thermoelectric Power of a Single van der Waals Interface between Carbon Nanotubes.

Control of van der Waals interfaces is crucial for fabrication of nanomaterial-based high-performance thermoelectric devices because such interfaces significantly affect the overall thermoelectric performances of the device due to their relatively high thermal resistance. Such interfaces could induce different thermoelectric power from the bulk, i.e., interfacial thermoelectric power. However, from a macroscopic point of view, a correct evaluation of the interfacial thermoelectric power is difficult owing to various interface configurations. Therefore, the study of the thermoelectric properties at a single interface is crucial to address this problem. Herein, we used in situ transmission electron microscopy and nanomanipulation to investigate the thermoelectric properties of carbon nanotubes and their interfaces. The thermoelectric power of the bridged carbon nanotubes was individually measured. The existence of the interfacial thermoelectric power was determined by systematically changing the contact size between the two parallel nanotubes. The effect of interfacial thermoelectric power was qualitatively supported by Green's function calculations. When the contact length between two parallel nanotubes was less than approximately 100 nm, the experimental results and theoretical calculations indicated that the interface significantly contributed to the total thermoelectric power. However, when the contact length was longer than approximately 200 nm, the total thermoelectric power converged to the value of a single nanotube. The findings herein provide a basis for investigating thermoelectric devices with controlled van der Waals interfaces and contribute to thermal management in nanoscale devices and electronics.

Analysis of non-linear losses in a parallel plate thermoacoustic stack

PurposeThis study aims to analyse the non-linear losses of a porous media (stack) composed by parallel plates and inserted in a resonator tube in oscillatory flows by proposing numerical correlations between pressure gradient and velocity.Design/methodology/approachThe numerical correlations origin from computational fluid dynamics simulations, conducted at the microscopic scale, in which three fluid channels representing the porous media are taken into account. More specifically, for a specific frequency and stack porosity, the oscillating pressure input is varied, and the velocity and the pressure-drop are post-processed in the frequency domain (Fast Fourier Transform analysis).FindingsIt emerges that the viscous component of pressure drop follows a quadratic trend with respect to velocity inside the stack, while the inertial component is linear also at high-velocity regimes. Furthermore, the non-linear coefficient b of the correlation ax + bx2 (related to the Forchheimer coefficient) is discovered to be dependent on frequency. The largest value of the b is found at low frequencies as the fluid particle displacement is comparable to the stack length. Furthermore, the lower the porosity the higher the Forchheimer term because the velocity gradients at the stack geometrical discontinuities are more pronounced.Originality/valueThe main novelty of this work is that, for the first time, non-linear losses of a parallel plate stack are investigated from a macroscopic point of view and summarised into a non-linear correlation, similar to the steady-state and well-known Darcy–Forchheimer law. The main difference is that it considers the frequency dependence of both Darcy and Forchheimer terms. The results can be used to enhance the analysis and design of thermoacoustic devices, which use the kind of stacks studied in the present work.

Evaluation of Comt2, Comt3, Cyp1b1, and Esr1 gene polymorphisms as risk factor for endometrial polyp

ABSTRACT The aim of this study is to evaluate COMT2, COMT3, CYP1B1, and ESR1 gene polymorphisms and occurrence of endometrial polyps. In addition, we intended to evaluate the clinical and epidemiological features of patients with and without the presence of the disease, characterizing the possible risk factors. A cross-sectional study was performed, with a total of 309 women, including 236 in the group of women with endometrial polyp confirmed by hysteroscopy and anatomical pathological examination and 73 in the group of people with diagnostic hysteroscopy without abnormal findings from the macroscopic point of view. Polymorphisms of four genes were studied: COMT2 (rs4680), COMT3 (rs5031015), CYP1B1 (rs1056836), and ESR1 (rs2234693). Polymorphism genotyping was determined using real-time polymerase chain reaction. Considering the results, no differences were identified between the two groups with respect to age, body mass index, diabetes, dyslipidemia, or smoking. The group of women without endometrial polyps showed higher use of hormone therapy than the other group (16.4 percent versus 3.8 percent, p < .001). The COMT2, COMT3, CYP1B1, and ESR1 genes exhibited no significant difference for the occurrence of endometrial polyp between the two groups. The research concluded that no correlation was identified between the genetic polymorphisms evaluated and the presence of endometrial polyps.

From Compositional Petri Net Modeling to Macro and Micro Simulation by Means of Stochastic Simulation and Agent-Based Models

Computational modeling has become a widespread approach for studying real-world phenomena by using different modeling perspectives, in particular, the microscopic point of view concentrates on the behavior of the single components and their interactions from which the global system evolution emerges, while the macroscopic point of view represents the system’s overall behavior abstracting as much as possible from that of the single components. The preferred point of view depends on the effort required to develop the model, on the detail level of the available information about the system to be modeled, and on the type of measures that are of interest to the modeler; each point of view may lead to a different modeling language and simulation paradigm. An approach adequate for the microscopic point of view is Agent-Based Modeling and Simulation, which has gained popularity in the last few decades but lacks a formal definition common to the different tools supporting it. This may lead to modeling mistakes and wrong interpretation of the results, especially when comparing models of the same system developed according to different points of view. The aim of the work described in this paper is to provide a common compositional modeling language from which both a macro and a micro simulation model can be automatically derived: these models are coherent by construction and may be studied through different simulation approaches and tools. A framework is thus proposed in which a model can be composed using a Petri Net formalism and then studied through both an Agent-Based Simulation and a classical Stochastic Simulation Algorithm, depending on the study goal.

A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior.

An efficient monolithic multiscale numerical manifold model for fully coupled nonlinear saturated porous media

This paper presents an efficient monolithic computational homogenization model for transient nonlinear hydro-mechanical analysis within the framework of Numerical Manifold Method (NMM). The proposed model is on the same theoretical basis as the FE2 method. The scale transitions are achieved through the extended Hill-Mandel theorem so that the microscopic fluid and solid dynamic effects are fully incorporated. The two-scale simulations are solved in a monolithic manner and the microscopic problems of all macroscopic integration points are decoupled from each other to prevent size of the system of equations from soaring to exceedingly large. By conveying microscale unbalanced forces and tangent operators to the macroscale level, the micro- and macroscale problems are solved in the same Newton loop such that unnecessary microscopic iterations based on estimated macroscopic variables in the conventional nested homogenization m are avoided. By solving benchmark numerical examples, the proposed model proves to be capable of capturing transient hydro-mechanical responses accurately. Moreover, in contrast to the conventional nested homogenization model, the proposed model saves around 40% of computational costs for nonlinear hydro-mechanical analysis. Using the framework of numerical manifold, the presented model can be easily extended to multiscale analyses involving complex boundaries, interfaces and fractures.

Absolute nodal coordinate formulation – Multilevel finite element framework for the nonlinear multi-scale multibody dynamic analysis of composite structures

In this paper, a multi-scale modeling approach for the dynamic response of flexible multibody system made of composite material is suggested using the absolute nodal coordinate formulation (ANCF) – multilevel finite element (FE2) method. On many occasions, the analysis of composite structures requires a multi-scale modeling approach. FE2 method is one of the famous multi-scale modeling methods. And it is a versatile computational homogenization approach that is commonly applicable to many different materials. Because the present FE2 is capable of computing the mechanical response at two different scales simultaneously, ANCF element will be used at the macroscopic scale. And the microscopic RVE at each macroscopic integration point will be obtained by the finite element method. Since ANCF element uses only the absolute nodes and gradients, the mass matrix will be constant. Due to such characteristics, when combined with FE2, the linearization process will become easier compared against the existing formulation. And Coriolis or centrifugal force will not need to be considered in the dynamic formulation. Therefore, computational cost will be reduced, and the relevant algebraic manipulation will become simplified. Several numerical examples are presented to demonstrate the improved accuracy and diminished computational cost of the present suggestion.

Elucidating the dynamics of hydrogen embrittlement in duplex stainless steel

We used slow-strain rate testing with in-situ microstructure imaging during electrochemical hydrogen charging to understand hydrogen embrittlement of super duplex stainless steel. Tensile deformation during hydrogen absorption softens the austenite and ferrite phases, lowering the macroscopic yield point and fracture strain. In contrast, when hydrogen absorption precedes micro-tensile testing, it strengthens the microstructure, highlighting a complex dual response. Computational analyses showed hydrogen atoms are trapped at phase boundaries, increasing the ferrite phase's hardness but reducing the austenite phase's hardness. However, once the boundaries are passivated, further entering hydrogen can diffuse rapidly without energy barriers, resulting in softening of both phases.

General methodological approach for the seismic assessment of masonry aggregates

Strong earthquakes have recently shown the vulnerability of masonry structures. In Italy, most of the historical centres are characterized by adjacent masonry structures connected in aggregate that have been subjected to structural and functional changes during time. Their structural behaviour shall be studied to avoid catastrophic outcomes after seismic events. In the technical literature, many studies are available to identify the most vulnerable structures in historical centres from a macroscopic point of view, or the seismic behaviour of some typical structures was studied in detail with sophisticated numerical methods providing a specific seismic vulnerability assessment. However nowadays there is not a general and standard procedure available, mostly methodological, based on well-known analysis methods, that can be followed to evaluate the structural behaviour under seismic actions of any building aggregate, and that allows to identify effective retrofitting interventions. Moreover, national regulations typically do not provide a standard procedure that can be followed by practitioners for such kind of problems. Thus, the purpose of this paper is to suggest a protocol to be used in common design, based on a broad blend of analyses that can be carried out with commercial software. It relies in a variety of numerical analyses, spanning from simple eigenvalue simulations to full 3D pushover computations, assuming different hypotheses for the material behaviour. The suggested method has been applied and benchmarked to an ex-monastery in northern Italy and several structural considerations to provide a sufficient insight useful for practitioners are provided.

Modeling the material behavior of heterogeneous materials considering a two‐scale FE–FFT‐based simulation framework

AbstractIn various engineering applications, components subjected to high mechanical or multi‐physical loads such as thermal, thermo‐mechanical, or electro‐thermal loads are predominantly made of metals or composites. These materials are characterized by polycrystalline or multi‐phase microstructures which determine the overall material response. However, since the microscopic material behavior is directly related to the distribution, size, and morphology of the individual grains or phase constituents, precise predictions of the macroscopic material response require simulations of the microstructural behavior. Hence, this work considers a two‐scale finite element (FE) and fast Fourier transform (FFT)‐based simulation framework, which is an efficient alternative to the classical method for the simulation of periodic unit cells. Assuming scale separation, the macroscopic and microscopic boundary value problems are first solved individually and subsequently linked by performing a scale transition in terms of averaging the macroscopic quantities over the corresponding local fields. While the macroscopic solution is computed by means of the finite element method, the microscopic boundary value problem, which is embedded as a periodic unit cell in each macroscopic integration point, is solved using an FFT‐based simulation method. In general, these microscopic simulations allow to model grain‐scale phenomena such as martensitic phase transformation or plastic deformation caused by dislocation glide which can be observed in polycrystalline materials. In this work, we present a two‐scale FE–FFT‐based model to capture the martensitic phase transformations in shape memory alloys. In order to demonstrate the applicability of the model, a numerical example is given.

A simple machine learning-based framework for faster multi-scale simulations of path-independent materials at large strains

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.

Edge effects on the electromagnetic response of inhomogeneous type-II superconductors

From a macroscopic point of view, the edges of a type II superconductor are degraded non-homogeneous regions compared with the bulk of the sample. This paper presents numerical simulations of a long superconducting bar with square cross section subjected to an axial external magnetic field, the edges effect on its electromagnetic properties is studied. The edges of the sample are modeled as finite width regions with lower critical current density than the rest of the material. The simulations are based on a continuum electrodynamics model which describes the magnetic induction dynamics of partially penetrated states. Unlike a simpler homogeneous superconductor, in an inhomogeneous material rough flux fronts are formed. It was established that the stochastic profile of the current density produces jets of magnetic flux near a macrodefect.