Macro Scale Research Articles

Gaussian random field-based characterization and reconstruction of cancellous bone microstructure considering the constraint of correlation structure

The macro scale physical properties of cancellous bone materials are governed by the microstructural features, which is of great significance for the multi-scale research of cancellous bone and the inverse design of bone-mimicking materials. Therefore, it is essential to characterize the natural cancellous bone samples, and reconstruct the microstructures with the biomimetic osteointegration and mechanical properties. In this research, a novel approach for the characterization and reconstruction of cancellous bone was proposed, based on the medical image analysis and anisotropic three-dimensional Gaussian random field (GRF). The geometric similarity, i.e. the interface curvature distribution (ISD), was meticulously studied, which is important to the osteointegration ability. And the mechanical properties were validated by the stress-strain curves under the large compressive strain simulated by the smoothed particle hydrodynamic (SPH) method. In addition, the effects of the generation parameters of GRF-based biomimetic microstructures on the apparent properties were analyzed. The ISD results demonstrated that both GRF and micro-CT groups had the similar columnar morphological properties, while the latter had more hyperbolic features. And it was found that the GRF-based biomimetic microstructures and the natural bone samples based on micro-CT (MCT) had the similar failure mode. The concordance correlation coefficient between MCT and GRF pairs was 0.8685, with a Pearson ρ value of 0.8804, and significance level p<0.0001. The Bland-Altman LoA was 0.1647 MPa with 95 % (1.96SD) lower and upper bound value between −0.2892 and 0.6185 MPa. The two groups had almost the same elastic modulus with the mean absolute percentage error (MAPE) of 7.84 %. While the yield stress and total conversion energy of the GRF-based samples were lower than those of the natural bone samples, and the MAPE were 16.99 % and 16.27 %, respectively. Although it meant the lower structural efficiency, the huge design space of this approach and advanced 3D printing technology can provide great potential for the design of orthopedic implants.

Effect of temperature and moisture composite environments on the mechanical properties and mechanisms of woven carbon fiber composites

AbstractTemperature and moisture are important environmental conditions governing the usage of carbon fiber‐reinforced plastics (CFRP). In this study, tensile, compression, and shear tests of woven CFRP composites were conducted in four composite environments: cold‐temperature dry state, room‐temperature dry state, elevated‐temperature dry state, and elevated‐temperature wet state. Fourier transform infrared (FTIR) spectroscopy was used to analyze the molecular composition and structure under these environments. The fracture morphology was also analyzed using scanning electron microscopy (SEM) to understand the deterioration mechanism of the material properties at the macro and micro scales. Finally, a two‐parameter Weibull statistical model was used to evaluate the scattering of the composites. The results demonstrate that the tensile, compressive, and shear properties of the material decrease under harsh conditions, and the degradation effect on the ultimate strength is much greater than that of the modulus. Cold‐temperature dry and elevated‐temperature wet conditions are particularly severe. Composite environments significantly affect the macroscopic failure process and ultimate failure mode of materials. In general, the temperature and humidity of the matrix system acts at the core of the deterioration mechanism of composites, affecting the properties of the fiber/matrix and orthogonal woven fiber interfaces, and eventually resulting in a loss of their constitutive strength and bearing capacity. The theoretical ultimate strength were consistent with the experimental values.Highlights Comprehensive mechanical property testing in multiple composite environments. Degradation mechanisms were revealed from multiscale morphology analysis. Scattering of composites were evaluated using statistical analyses. Cold temperature dry and elevated temperature wet were the worst states. Deterioration effect of environment on matrix and interface is the core failure mechanism.

Sensing the multi-scale landscape functions heterogeneity by big geodata from parcel to urban agglomerations-a case of the Greater Bay Area, China

ABSTRACT Multi-scale landscape functions play a critical role in revealing intricate functional structures within large regions. However, previous studies on landscape functions have predominantly focused on a single macro or micro scale, impeding a holistic multi-scale understanding of the spatial distribution and heterogeneity of landscape functions. To address this gap, this study proposes a framework leveraging the power of big geodata to mine multi-scale landscape functions from parcel to entire urban agglomerations, as well as non-administrative divisions. Our study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Firstly, we integrated multi-source big geodata to derive parcel-scale landscape functions. Subsequently, we employed the Normalized Revealed Comparative Advantage index to derive landscape functions at broader scales, including towns, counties and cities. The effectiveness of our approach is validated through in-field investigations and comparisons with established policy planning positions. The outcomes not only offer distinctive planning insights at various scales but also highlight the versatility of big geodata in extracting landscape functions across scales. This study demonstrates that big geodata is adept at uncovering multi-scale landscape functions irrespective of administrative boundaries, providing valuable insights for fostering multi-scale regional coordinated development.

Pre-trained transformer model as a surrogate in multiscale computational homogenization framework for elastoplastic composite materials subjected to generic loading paths

A composite material typically exhibits complex behavior at the engineer scale, arising from the interactions between its underlying constituent phases, as well as the competitions between micro-processes. It is generally a daunting task to develop an engineering model to adequately capture the essential micro mechanisms that propagate onto the macro scale. To this end, the multiscale computational homogenization (FE2) method enables a consistent coupling across length scales, to give results that compare well with direct numerical simulations having the full micro-structural details, without the need for any constitutive assumptions nor calibrations at the macro scale. Despite its predictive capabilities, the typical computational homogenization method is still computationally too expensive for most practical problems, as the coupling between micro and macro scales are solved simultaneously during its numerical implementation. In this presentation, focusing on the elastoplastic behavior of fiber-reinforced composite, we address this bottleneck with an offline development of a microscopic surrogate model for a given micro-structure, to be incorporated into a standard nonlinear FE framework, for rapid online implementations at the macro scale. For the offline training phase, we adopt the transformer-based architecture within a pre-training and fine-tuning framework. The proposed pre-trained transformer model is capable of parallelizing computations to effectively capture global dependencies within the strain-stress data sequences. To reduce the data generation cost, a constructed source representative volume element (RVE) having a single central heterogeneity with an identical volume fraction with the target RVE is utilized, to rapidly generate a huge source dataset for a pre-training process. The performance of the surrogate model is first demonstrated by comparing its predictions under random loading paths against the reference homogenized RVE responses. Next, the surrogate model is incorporated into a macro FE framework, and its predictive capabilities illustrated via the generic loading of two specimens with different microstructures, each having a different loading–unloading path. Finally, a chunking method is discussed as a potential remedy for managing very long load sequences.

Effects of variable slip length over induced electric potentials in microchannels

The exploration of new alternatives in renewable energy has embraced several branches: from solar, hydroelectric, and wind energy on the macro scale, up to those more recently such as to harvest power from some electrokinetic phenomenon, for instance, the well-known electric generation by means of the induced streaming potential process. This electric potential is found in micro and nano scales, where the physicochemical properties of surfaces have fundamental importance, such as the slippage effect. Most works oriented to determine induced electric potential in micro and nanochannels assume constant Navier slip length, which quantifies the hydrophobic degree of the surface. This assumption could overestimate the performance of the so-called electrokinetic batteries. For this reason, it is necessary to enlarge the analysis of variable slip over electrokinetics in micro and nanoscale.In the present analysis, we develop an analytical procedure to estimate the electric potentials induced in a microchannel by a flowing laminar solution due to osmotic gradients, whose configuration is better known as an electrokinetic forward osmosis battery. Assuming a non-linear dependence of Navier slip as a function of the pressure, we obtain regular perturbative series solutions for velocity, pressure, and the streaming potential. For this purpose, we use the dimensionless decay parameter ϵ̃, which measures the decrease of slippage due to the pressure at the wall as the perturbation parameter. We conclude that the velocity field has not only a longitudinal component if not also a transverse component normal to the surfaces as a consequence of the variable slippage, showing that the classical assumption of fully developed flow is no longer possible. In addition, our data shows that constant slip overpredicts the electric potentials up to 5% in comparison with the case of exponential slip. Finally, the results show that induced electric potentials are benefited from variable slippage; however, the influence of the decay parameter that appears in the boundary condition (32) associated with the variable slip decreases the induced potentials.

Serially connected strip-type acoustic wave resonator using X-40°Y LiNbO3 (XSAR)—feasibility study using macro model

This work presents a new serially connected strip-type acoustic resonator using X40°Y-LiNbO3 (LN) (XSAR). The proposed design is composed of a serial of strip-type bulk acoustic wave resonators connected together. The volume between two strips is penetrated through to eliminate the spurious response and enhance quality (Q) factor. The Euler angle (90°, 90°, 40°) of LN is optimized to obtain the maximal piezoelectric constant e 34, which determines the coupling factor of shear mode of the resonator. Simulation results show that the new design is superior in terms of impedance ratio (Z-ratio) and bandwidth (BW) and exhibits the largest BW of 32% ever reported. The slow shear mode spurious response is successfully removed by the proposed penetrated structure combined with narrow strip. Furthermore, the device was prototyped in macro scale using 350 μm thick LN, by which the ultra-wide BW and potentially good performance in HF were confirmed.

An expansion effect based pseudo-boiling critical point model for supercritical CO&lt;sub&gt;2&lt;/sub&gt;

Heat transfer deterioration (HTD) is one of the important issues in the study of supercritical fluid (SCF) heat transfer. However, when the SCF crosses the pseudo-critical point, the none-quilibrium process occurs in liquid, so SCF is very complicated. Recently, the existence of SCF pseudo-boiling on a macro scale has sparked controversy. There is still no unified understanding of the mechanism of gas-like and liquid-like transition affecting heat transfer. In this work, it is assumed that SCF has a macroscopic phenomenon similar to subcritical flow boiling. By analogy with subcritical boiling heat transfer, a boiling critical point model is proposed to describe the HTD in supercritical CO&lt;sub&gt;2&lt;/sub&gt;. Our study reveals that the HTD caused by pseudo-boiling only occurs under large temperature gradient, which makes the superheated liquid-like layer cover the wall, and the gas-like and liquid-like may present different distribution forms, thus changing the heat transfer characteristics. When the wall temperature is higher than the pseudo-critical temperature and the enthalpy of the fluid layer covering the wall exceeds a certain value, the HTD may occur. The proposed theoretical model can explain the experimental results well, and the prediction accuracy of heat transfer correlation considering pseudo-boiling is greatly improved. In this work, the connection between supercritical heat transfer and subcritical heat transfer is established theoretically, which provides a new idea for studying the deterioration of SCF heat transfer, thus enriching the theory of supercritical heat transfer.

Soft-rigid granular mixtures: Role of particle shape and rolling resistance in response under compressive loads

Mixtures of recycled tyre aggregates and common granular geo-material such as gravel and sand have been widely used for various applications in geotechnical engineering, such as backfill, lightweight construction geomaterial and pavement subbase layers. The mechanical properties and characteristics of various soft-rigid granular blends has been extensively studied in the past. The main objective of these studies is to determine suitable mix designs for different applications and provide a better understanding of material response under applied loads. Experimental and numerical studies indicate the paramount importance of particle interaction at the microscale on the response of the soft-rigid granular mixtures at the macro scale. The stark contrast in stiffness (or flexibility) of soft tyre particles to rigid sand or gravel particles, means that in the soft-rigid mixtures under shearing or compressive loads, the deformation of soft tyre particles and continuous change of shape and contact area and hence constant evolution of frictional resistance plays an important role in the behaviour of these blends. In the present research mixtures of gravel and tyre particles in a range of volume ratios and size ratios were modelled using DEM (discrete element method) and calibrated to match one-dimensional compressibility experimental data obtained by the team. The effect of the shape of soft particles on the compressive response of soft-rigid granular mixtures is studied through rolling resistance in DEM. The role of rolling friction is studied in detail. Rolling resistance is found to be an important parameter to simulate the behaviour of the particle shape of these mixtures. Microscopic studies like spin analysis and share of contact force is performed to understand the observed response better. However, the increase in rolling resistance value beyond a calibrated value is found to have little impact on the macroscopic behaviour of these mixes.

Ecological Suitability Assessment Methods of Waste Pile-Up along Railway Routes Based on Machine Learning Algorithms

Waste pile-up along railway routes poses an important threat to the regional ecological environment. However, there is a lack of methods that assess the ecological suitability of waste pile-up (ESWP) at a macro scale, which is crucial for informed decision-making. We define the ESWP and propose a methodology to measure the level of ESWP along railway routes. Specifically, we focus on the Ya’an to Nyingchi section of the railway, selecting a 30-km buffer zone on either side as the study area. To develop ESWP maps, we employed Landsat 8, digital elevation model (DEM), soil database, land use, and meteorological data. We tested 3 machine learning methods—random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBoost)—using 7 key indicators as input parameters. The performance of these models was evaluated using overall accuracy and the Kappa index. Additionally, we analyzed the relative importance of each indicator on the results. The study reached the following results: Firstly, the combination of selected indicators with machine learning methods effectively assesses the ESWP along railways. Secondly, among the tested methods, DNN demonstrated superior performance, achieving an accuracy of 86.49%, outperforming RF (80.31%) and XGBoost (79.54%). Thirdly, the indicators with the greatest impact on the assessment were biological richness (weight is 0.23), vegetation coverage (weight is 0.20), and soil nutrients (weight is 0.16). These findings provide a novel approach to assessing the ecological suitability and identifying low-risk sites for waste pile-up along railway routes.

Research on optimization of ecosystem service function in mudslide disaster area by landscape ecological engineering technology

Abstract At present, the prevention and control of mudslide disasters are mainly based on geotechnical engineering measures, supplemented by landscape and ecological engineering measures, ignoring the important role of ecological prevention and control engineering in mudslide management. In this paper, the Bailong River Basin is taken as the main research object at the macro scale, supplemented by the municipal districts along the route as the typical mudslide disaster research area at the meso and micro scales. The InVEST model was utilized to evaluate the ecological service functions of each land and pinpoint the sensitive areas that are influencing the evolution of ecological service functions. The quantitative regulation model for landscape ecological integrated planning in the mudslide disaster area was proposed from the perspective of ecological disaster prevention and mitigation through landscape ecological engineering technology. Combined with the successful case of landscape ecological regulation in Region X, it was found that the AWMSI index of the region increased from 56.78 to 79.03 during the regulation period, and the diversity of patches within the landscape was enhanced, which was favorable to species exchange and biodiversity conservation. The mean value of the Habitat Quality Index (HQI) increased from 0.6147 to 0.6579, and the overall level of habitat quality was improved. The annual growth rate of MPI was 0.0002 with a slow growth rate, but the growth rate of MPI increased significantly with the further increase of restoration years. The study's findings can be utilized as a scientific basis for landscape ecological engineering and can also serve as a reference for disaster prevention and ecological restoration in mudflow basins with high activity.

Division and Effect Evaluation of Fracking Outburst Elimination Zones in Surface Extraction Wells of Coalbed Methane

Surface hydraulic fracturing is an important measure for increasing reservoir permeability, which has advantages such as engineering safety and a large impact range and can be implemented ahead of the mine’s underground engineering. However, its underground outburst reduction range and effect are rarely reported, and there is a lack of connection with underground fracturing wells. Taking coalbed methane wells in the Lu’an Mining Area as an example, underground observation, microseismic monitoring, and numerical simulation methods were used to study the fracturing range and outburst control effect of surface wells. The fracturing zone of coalbed methane wells is approximately elliptical in shape, with the main fractures extending along the direction of the maximum horizontal principal stress. It can be divided into five zones: sand laying zone (radius of 140~150 m), fracture propagation zone (radius of about 180 m), fracturing fluid permeability zone (width of about 1 m), gas surge zone (width of 2~3 m), and final desorption zone (width of about 2 m). The stress around the fracturing zone increases along the direction of maximum and minimum principal stresses, while the stress value within the zone decreases, with a range of approximately 5~20% of the original geostress. The outburst reduction index Δh2 in the fracturing zone significantly decreased after fracturing. The percent of Δh2 (&lt; 150 Pa) increased from 38.3% to 100% after fracturing. A model for evaluating the effectiveness of surface fracturing and outburst prevention was proposed, and the model was used in Tunliu Mine. The results showed that the standard‐reaching rate of extraction was high, and the danger of outburst could be completely eliminated. The research results can provide a reference for the arrangement of the coalbed methane wells and can also provide effective guidance for outburst prevention and control work during mining and excavation on a more macro scale. It provides a new idea and method for making up for the shortcomings such as the small impact range and safety hazards of underground fracturing.

An assessment of municipal solid waste management system in Oshogbo, Osun State Nigeria: Challenges and prospects

The study empirically assessed the challenges and prospects of municipal solid waste management system in Oshogbo, Osun State, Nigeria. The study specifically looked into the current - status, the performance of municipal solid waste social control system of the city, and the households solid waste social control practice. Primary data were used and sourced through a well-design survey questionnaire, field observation, reviewing published and unpublished documents, and on spot access. Descriptive statistics was used for the data analysis. The main factors aggravating the present poor state of municipal solid waste social control of Oshogbo were revealed in the study. The results further showed that Osun state waste management agency is not leaving up to its responsibility and delay in collection of wastes, the activity of municipal solid waste social control of the town is majorly executed by micro and macro scale enterprises with very limited contribution of the agency saddled with such responsibilities. Besides this, there is no involvement of non- governmental organization and private sectors as the government of the day portrayed it. Inadequate solid waste social control pattern of the households. The first inadequacy of households is poor handling of short-lived storage material of various houses. That is, trash disposal around the waste container. Likewise, they are uncovering in the rain, light and heat, they do not take cognizance of sustainable solid social control actions for instance, reprocessing, reclaim and composting. Except, for the prohibited solid waste disposal. In addition, they are less motivated to clean their immediate environment. Thus, the study recommends that government waste management agency in conjunction with non-governmental organization should commence training on waste social control practices, with focus on women as agent of change in a community, while the institutions should leave up to their responsibilities.

A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks

This article presents the development of a estimation model using an artificial neural network to estimate the traffic factor parameter, which reflects changes in travel time for a single transport connection. The modeling of traffic is highly challenging due to the complex and non-linear nature of road systems, the interactions between diverse road users, and the ripple effects of congestion. The research problem addressed is the need to consider a wide range of factors that contribute to congestion, as identified in the literature. To address this challenge, a universal Feedforward Neural Network (FNN) was designed, specifically the Multilayer Perceptron (MLP) 38–10-1 model. The input layer neurons of the MLP receive a vector of input values representing various factors such as road types, technical properties, time of day, days of the week, incidents, weather conditions, and population density. The model was trained using a dataset consisting of 41,945 records of fully completed input variables, extracted from a larger dataset of 300,000 travel time measurements collected from a real transport network mapped on a macro scale (Mazovia, Poland). During the research, the model achieved a satisfactory level of Mean Absolute Percentage Error (MAPE) for the test set (9.12%) and a Normalized Root Mean Squared Error (NRMSE) below 0.02, indicating good estimation performance. Furthermore, this work proposes specialized models with the structure of MLP 34–10-1 for individual types of roads, which also demonstrated satisfactory estimation abilities. These models have practical applications in traffic management and planning. Overall, this research addresses the research problem of modeling factors for predicting travel time changes using an artificial neural network approach. The developed models provide accurate estimations and offer potential applications in transportation systems.

Dual-Shell Microcapsules for High-Response Efficiency Self-Healing of Multi-Scale Damage in Waterborne Polymer-Cement Coatings.

Waterborne polymer-cement coatings have been widely applied in building materials due to their organic solvent-free nature, low cost, and eco-friendliness. However, these coatings can easily crack during the drying process as a result of construction environment factors, compromising the barrier performance of the coating and limiting its large-scale application. In this study, a dual-shell self-healing microcapsule was developed, which can effectively heal damage on a macro scale in waterborne polymer-cement coatings. Specifically, this dual-shell self-healing microcapsule was designed with a silica gel shell and a tannic acid-cuprum (TA-Cu) double-shell structure embedded with an epoxy resin (EP) healing agent, which was successfully fabricated via a two-step in situ polymerization. This silica gel shell self-healing microcapsules can effectively load into waterborne polymer-cement coatings. As the coating dries and solidifies, the silica gel shell of the microcapsule also becomes loose and brittle due to dehydration. This improves the mechanical initiation efficiency of the microcapsules in the coating. This study provides a novel approach for the application of self-healing microcapsules in waterborne coating systems, which can significantly reduce cracking during the drying process of waterborne polymer-cement coatings and improve the service life of the coating under complex conditions.

Multiscale viscoelasticity response for bamboo after partial hemicellulose removal treatment

Hemicellulose degradation can decrease the hygroscopicity and result in an improvement of dimensional stability for nature materials. The mechanical properties has always been used as an indicator of hemicellulose degradation, but lack of an indicator that can be reflects the additive effects of treatment condition and the effects of time. This work aimed to use multiscale viscoelasticity response to evaluate the coupled effects of physical/chemical treatment and loading time. Different conditions (hot water, acidic and saturated steam) were tested on bamboo. Macroscopic creep of bamboo slices and microscopic creep behavior of cell wall were determined. The relationship between gradient structure and viscoelasticity has been obtained. The outer slice, i.e., bamboo green, had best strength and flexural toughness. Middle slice exhibits significant viscoelasticity response. Inner slice, also called bamboo yellow, exhibits little viscoelasticity response but bad strength. The relationship between treatment conditions and macroscopic/microscopic creep also has been obtained. At macro scale, the acid group exhibits significant viscoelasticity response, while the water group and steam group exhibit more elasticity response, especially for water groups. At micro scale, the steam-treated and acid-treated group are all showed significant viscoelasticity response, while the water-treated group had lowest relative creep. This paper could be a reasonable selection support of bamboo materials for stiffness or flexible products.

Farmland expansion and intensification do not foster local food self-sufficiency. Insights from the Mediterranean area

Bridging the gap between the micro and the macro scale in modelling food security to inform context-specific regionalised policies remains a major scientific challenge. A better understanding of the relations between global and local drivers impacting local food self-sufficiency (LFSS) is essential. We applied to the whole Mediterranean environmental area (Southern and Northern) a modelling framework for structural estimates (PLS-PM) using qualitative and quantitative methods to combine local-level information from field surveys and participatory workshops with global-level data. Our findings show that farmland expansion and intensification spatially disconnected from urban consumption areas do not appear to foster LFSS. On the other hand, public policies appear key to enhancing LFSS in the Mediterranean area if appropriate to the particular regional context. We outline how this multi-level modelling methodology can contribute to a place-based approach by informing context-specific regionalised policies aimed at food security.

Numerical Simulation of the Residual Stress at the Interface between Thermal Barrier Coating and Nickel-Based Single-Crystal Superalloy Based on Crystal Plasticity Theory

Residual stress plays an important role in the formation and growth of cracks in thermal barrier coatings and single-crystal superalloy substrates. In this study, a finite element model for a planar double-layer thermal barrier coating and a crystal plasticity finite element model based on dislocation slip-induced plastic deformation of single-crystal materials were established to analyze the residual stress in the coatings and the substrate, considering the creep and crystal plasticity of the substrate materials. The simulation results show that the thermal barrier coatings bear most of the stress generated by high temperatures, and the residual stress of the substrate is small. By comparing the two material properties to calculate the interface stress when the amplitude of the interface between the substrate and the coating is 30 μm and the thickness of the thermal grown oxide layer is 5 µm, the interfacial stress of the substrate at the macro scale was found to be similar to the interfacial stress at the micro slip system scale. Based on the cumulative shear strain, it was determined that the [001]-, [011]-, and [111]-oriented alloys activated the 12, 8, and 4 groups, respectively, under the combined action of thermal stress and centrifugal force of the coating. Comparing the activation of different initial orientation slip systems and the magnitude of the yield stress provides a theoretical foundation to study the structural integrity of single-crystal alloys.

Probabilistic fatigue strength assessment of cross-ply laminates: Exploring effects of manufacturing defects through a two-scale modeling approach

The study presents a two-scale modeling approach allowing for an efficient fatigue strength evaluation on a macro scale considering a micro-mechanical defect characterization of a Carbon Fiber Reinforced Polymer (CFRP) material. The modeling approach integrates a macro model with the effective elastic properties from micro-mechanical simulations considering voids. This enables the analysis of defects’ influence on material fatigue strength using a probabilistic weakest link approach. A CFRP laminate with a cross-ply layup was investigated. Two simulation case studies demonstrate the effect of void content and size on the characteristic fatigue strength. An experimental investigation was conducted testing the laminates in tension–tension fatigue verifying the predicted numerical behavior. The numerical models identify a difference in the characteristic fatigue strength consistent with the fatigue test results. It is numerically concluded that the investigated CFRP material’s fatigue strength is affected by the presence of voids and even with only a slight difference in the global void volume fraction a scatter in fatigue strength is identified.

Comparison of lubrication mechanism and friction behavior of graphene on stainless steel substrate at different scales

Graphene at both nano and macro scales reduces friction in stainless steel, but there are differences in the lubrication mechanisms of graphene at the two scales. In macroscopic friction, graphene achieves lubrication through the combined effect of low friction properties of the material itself and relative sliding at the friction interface. In nano-friction, the relative sliding of graphene at the friction interface does not occur, and the lubrication is realized only by the low friction property of graphene. In nano-friction, the bulge structure of graphene on a stainless-steel substrate causes a two-stage increase in friction. At the same time, friction strengthening behavior and stick–slip behavior occur in graphene during nano-friction.

Multi-{Physics, Phase, Scale} Computational Modeling of Interface-Coupled Problems in Redox Flow Battery Design

Motivated by an urgent need to deploy large-scale energy storage technologies to integrate intermittent renewables, redox flow batteries (RFBs) are gaining prominent attention due to their interesting features including higher durability and power-energy decoupling. RFBs are rechargeable electrochemical systems in which an electrolyte solution containing dissolved or suspended active species is stored in external tanks and pumped through the electrochemical stack - consisting of flow fields, porous electrodes, and membranes - where the active species undergo electrochemical reactions to charge/discharge the battery. However, large-scale deployment of RFBs has feasibility issues regarding costs and efficiency, which can be tackled by tuning their multi-scale design parameters [1].Developing a coupled multi-scale computational model for RFBs can facilitate our understanding of the interplay between intricate phenomena occurring at several length and time scales (depicted schematically in the figure), ranging from transport phenomena, i.e., mass, charge, momentum, and heat, on the cell level (macro and meso scales) to microstructure effects and wettability behavior on the pore-scale (micro and nano scales). Furthermore, the modeling framework should capture the reaction kinetics and multi-phase chemistry of emerging flow battery concepts (e.g. metal-air) including plating and stripping reactions [2].In this research, we are developing a multi-{physics, phase, scale} model for the optimization of next-generation RFBs in the cell-, electrode-, and pore-scale. The model includes free fluid flow (representing flow field channels) over a porous medium (porous electrode) and solves the transport mechanisms over the entire computational domain with a focus on the electrolyte-electrode interface. The model is developed using finite element/volume methods combined with pore network modeling techniques, implemented using various open-source modeling frameworks including OpenFOAM [3], FEniCS [4], and OpenPNM [5].In this presentation, I will first discuss the structure of the modeling framework including the continuum transport models, the pore network model, and the employed numerical schemes. Then, I will present the coupling between continuum and porous medium models. Finally, I will highlight some initial results focused on the efficiency of the coupling of the electrolyte fluid transport with the porous medium and pore network representation of the electrode [6]. We hope that the developed model can be a functional tool to assess and optimize RFB materials and operating conditions and can be broadly adopted to model emerging flow battery chemistries. References K. Chakrabarti et al., Sustainable Energy & Fuels. 4, 5433–5468 (2020).Hein et al., ACS Applied Energy Materials. 3, 8519–8531 (2020).G. Weller et al., Computers in Physics. 12, 620 (1998).Alnæs et al., Archive of Numerical Software, in press, doi:10.11588/ANS.2015.100.20553.Gostick et al., Computing in Science & Engineering. 18, 60–74 (2016).van der Heijden et al., Journal of The Electrochemical Society. 169, 040505 (2022). Figure 1