Mesoscale Simulations Research Articles

Meso-scale analysis of shear performance and size effect of CFRP sheets strengthened RC beams

In order to reflect and reveal the shear failure process and mechanism of carbon fiber reinforced plastic (CFRP) sheets strengthened reinforced concrete (RC) beam, a mechanical analysis model is established by using a 3D meso-scale simulation method. The model considers the heterogeneity of concrete materials, the interaction behaviors between reinforcements and concrete, and the complex interaction behaviors between CFRP sheets and concrete, simultaneously. Different types of meso-scale models of RC beam strengthened with CFRP sheets are designed to explore the influences of stirrup ratio and CFRP fiber ratio on the nominal shear strength and size effect behaviors. Test results indicate that: 1) The action mechanisms of stirrups and CFRP sheets are similar; 2) The nominal shear strength of CFRP sheets strengthened RC beams increases with the increase of stirrup ratio and CFRP fiber ratio, while the degree of the increasing range decreases; 3) The nominal shear strength of RC beams strengthened with CFRP sheets decreases with the increase of section size, nevertheless increasing the stirrup ratio and the CFRP fiber ratio will weaken the size effect; 4) Based on the numerical test results, a novel size effect law simultaneously considering the stirrup ratio, the CFRP fiber ratio and the mutual effects between them is proposed.

Numerical simulation of rock erosion performance of a high-speed water jet using an immersed-body method

Water jet drilling (WJD) is an effective technique for drilling micro-holes in the subsurface for reservoir stimulation . This study aims to investigate numerically the failure mechanism of rock during WJD and to assess the WJD performance before drilling. A 3D fluid-solid coupling model is developed for simulating WJD by coupling a mechanical solver based on the combined finite-discrete element method (FDEM) with a fluid solver using an immersed-body method. The new numerical model is capable of simulating crack initiation and propagation and fragment removal under the impact load of a high-speed water jet. The poroelastic effect is implemented via Biot's theory of poroelasticity . The numerical results show that: (1) rock failure is only observed in the Gildehaus sandstone with the lowest strengths among the three types of rock tested, (2) most of the cracks are tensile failures and pure shear cracks are rare, mixed mode cracks account for 15%∼40% of the total crack number depending on the mechanical boundary conditions , (3) increased water back pressure significantly suppresses jet erosion. The poroelastic effect on the rock failure is insignificant in the mesoscale simulations and will be further investigated using a microscale model in future.

Atomic simulations of primary irradiation damage in U–Mo–Xe system

To shed a light on Xe bubble nucleation in U–Mo fuel from the view of primary irradiation damage, a reported U–Mo–Xe potential under the framework of embedded atom method has been modified within the range of short and intermediate atomic distance. The modified potential can better describe the interactions between energetic particles, and can accurately reproduce the threshold displacement energy surface calculated by the first-principles method. Then, molecular dynamics simulations of primary irradiation damage in U–Mo–Xe system have been conducted under different contents. The raise of Xe concentration brings about a remarkable promotion in residual defect quantity and generates bubbles in more over-pressured state, which suggests an acceleration of irradiation damage under the accumulation of the fission gas. Meanwhile, the addition of Mo considerably reduces the residual defect count and hinders irradiation-induced Xe diffusion especially at high contents of Xe, corroborating the importance of high Mo content in mitigation of irradiation damage and swelling behavior in U–Mo fuel. In particular, the variation of irradiation damage with respect to contents suggests a necessity of taking into account the influence of local components on defect evolution in mesoscale simulations.

Multiscale approach to determine the anisotropic mechanical properties of polyisocyanurate metal panels using FEM simulations

Building on previous publications, in this work an existing mesoscale simulation model was first extended to predict the non-linear anisotropic mechanical behavior of foam structures. Subsequently, a comparison between experimental and simulated data was performed to validate the selected simulation parameters. Here, good agreements were observed, especially for compressive loading. Based on these results, a multiscale approach was described in which the local foam properties were applied to macroscale models of PIR-based metal panels to predict the overall component properties. A further comparison of selected macroscopic experimental and simulated data also showed a good agreement. Due to the high computational and modeling effort, a sensitivity analysis was finally performed on the influence of the resolutions of the local mesoscale foam properties on the macroscopic metal panel properties. It was found that for the metal panels considered, it is sufficient to model the (almost) homogeneous core layer and outer edge areas with one layer each. These findings can also be applied to other metal panels with different thicknesses for future investigations, since it is known from previous investigations that the metal panels have qualitatively comparable property distributions.

Sensitivity analysis of mesoscale simulations to physics parameterizations over the Belgian North Sea using Weather Research and Forecasting – Advanced Research WRF (WRF-ARW)

Abstract. The Weather Research and Forecasting (WRF) model offers a multitude of physics parameterizations to study and analyze the different atmospheric processes and dynamics that are observed in the Earth's atmosphere. However, the suitability of a WRF model setup is known to be highly sensitive to the type of weather phenomena and the type and combination of physics parameterizations. A multi-event sensitivity analysis is conducted to identify general trends and suitable WRF physics setups for three extreme weather events identified to be potentially harmful for the operation and maintenance of wind farms located in the Belgian offshore concession zone. The events considered are Storm Ciara on 10 February 2020, a low-pressure system on 24 December 2020, and a trough passage on 27 June 2020. A total of 12 WRF simulations per event are performed to study the effect of the update interval of lateral boundary conditions and different combinations of physics parameterizations (planetary boundary layer, PBL; cumulus; and microphysics). Specifically, the update interval of ERA5 lateral boundary conditions is varied between hourly and 3-hourly. Physics parameterizations are varied between three PBL schemes (Mellor–Yamada–Nakanishi–Niino, MYNN; scale-aware Shin-Hong; and scale-aware Zhang), four cumulus schemes (Kain–Fritsch, Grell–Dévényi, scale-aware Grell–Freitas, and multi-scale Kain–Fritsch), and three microphysics schemes (WRF Single-Moment five-class scheme, WSM5; Thompson; and Morrison). The simulated wind direction and wind speed are compared qualitatively and quantitatively to operational supervisory control and data acquisition (SCADA) data. Overall, a definitive best-case setup common to all three events is not identified in this study. For wind direction and wind speed, the best-case setups are identified to employ scale-aware PBL schemes. These are most often driven by hourly update intervals of lateral boundary conditions as opposed to 3-hourly update intervals, although it is only in the case of Storm Ciara that significant differences are observed. Scale-aware cumulus schemes are identified to produce better results when combined with scale-aware PBL schemes, specifically for Storm Ciara and the trough passage cases. However, for the low-pressure-system case this trend is not observed. No clear trend in utilizing higher-order microphysics parameterization considering the combinations of WRF setups in this study is found in all cases. Overall, the combination of PBL, cumulus, and microphysics schemes is found to be highly sensitive to the type of extreme weather event. Qualitatively, precipitation fields are found to be highly sensitive to model setup and the type of weather phenomena.

Characterization of toughness enhancement of aligned steel fibers in cement-based composites based on DDM method and meso-scale simulation

In this study, the toughness enhancement of aligned steel fibers in cement-based composites was mainly characterized. Three-point bending fracture tests with different fiber orientations and fiber volume contents were performed; additionally, crack propagation processes of random and aligned steel fiber reinforced cement-based composites (SFRCs and ASFRCs, respectively) were investigated using the discontinuous deformation measurement (DDM) method. The evolution of the fiber stress and resultant fiber force F on the fracture surface was analyzed through a meso-scale simulation analysis. Results of the DDM method, reveal that compared with the SFRC, smaller strain concentration regions are observed for the ASFRC under the same load. A higher ultimate load and use efficiency of steel fibers are obtained for the ASFRC. Moreover, a higher maximum value of F is presented for the ASFRC; the same tendency is exhibited for F at the initial loading stage. Both these trends indicate that the aligned steel fibers improve the crack-resistance ability and result in a better toughness enhancement effect for the ASFRC than that of the SFRC.

Metal Additive Manufacturing Process Design based on Physics Constrained Neural Networks and Multi-Objective Bayesian Optimization

The high-dimensionality of the process-structure-property (P-S-P) relationships for metal additive manufacturing (AM) is the major research challenge for process optimization to produce defect-free, structurally sound, and reliable AM parts. The goal of this work is to investigate the feasibility of a hybrid physics-based data-driven process design framework to establish reliable surrogates of process-structure relationships for metal AM process optimization. The process design framework includes a mesoscale multiphysics simulation model to predict microstructure evolution, a physics-constrained neural network to construct the surrogate of the process-structure relationship, and Bayesian optimization for process design. The proposed framework is demonstrated by optimizing the initial temperature and cooling rate for the single dendritic growth of Ti-6Al-4V alloy during rapid solidification in metal AM so that the desired dendritic area and microsegregation level can be achieved. The multi-objective optimization problem is solved with single-objective Bayesian optimization by aggregation. The effects of the aggregation weights on the optimization results are investigated with sensitivity analysis. The results show that the weights of individual objective functions determined from design preferences can help the designer to produce desired microstructure. The Pareto front and processing map for dendritic growth are constructed with the multi-objective Bayesian optimization method. The proposed process design framework is generic and can be potentially applied in materials design and digital twins of metal AM.

Meso-scale numerical simulation of the effect of aggregate strength on damage and fracture of high-strength concrete under dynamic tensile loading

The dynamic mechanical behavior of high-strength concrete is sensitive to aggregate properties, thus an accurate description of the effect of aggregate strength on the dynamic tensile mechanical properties of high-strength concrete is essential to evaluate the structural stability of concrete. In this paper, a concrete meso-scale modeling framework was proposed based on discrete element method (DEM). The meso-scale model considered the three-dimensional meso-structure of concrete (coarse aggregate, mortar and interfacial transition zone), and the crushable aggregate model with realistic morphological characteristics was generated by the “clump-cluster” method. Based on this, the effects of aggregate strength on dynamic tensile properties and damage behavior of high strength concrete were quantitatively studied by splitting tensile test. The simulation results reveal the influence of aggregate strength and strain rate on the tensile properties of concrete at both macroscopic and mesoscopic levels, including dynamic deformation behavior, microcracks propagation mode and the proportion of different kinds of microcracks at different loading stages. The results show that the aggregate strength plays an important role in the dynamic tensile properties of concrete. The failure behavior of concrete and the damage degree of different components under dynamic tensile loading are influenced by the ratio of aggregate strength to mortar strength.

Mesoscopic simulations of active nematics.

Coarse-grained, mesoscale simulations are invaluable for studying soft condensed matter because of their ability to model systems in which a background solvent plays a substantial role but is not the primary interest. Such methods generally model passive solvents; however, far-from-equilibrium systems may also be composed of complex solutes suspended in an active fluid. Yet, few coarse-grained simulation methods exist to model an active medium. We introduce an algorithm to simulate active nematics, which builds on multiparticle collision dynamics (MPCD) for passive fluctuating nematohydrodynamics by introducing dipolar activity in the local collision operator. Active nematic MPCD (AN-MPCD) simulations not only exhibit the key characteristics of active nematic turbulence but, as a particle-based algorithm, also reproduce crucial attributes of active particle models. Thus, mesoscopic AN-MPCD is an approach that bridges microscopic and continuum descriptions, allowing simulations of composite active-passive systems.

Characterization and Multiscale Modeling of the Mechanical Properties for FDM-Printed Copper-Reinforced PLA Composites.

Additive manufacturing is an emerging technology and provides high design flexibility to customers. Fused deposition modeling (FDM) is an economical and promising additive manufacturing method. Due to its many advantages, FDM received great attention in recent years, and comprehensive studies are being undertaken to investigate the properties of FDM-printed polymers and polymer composites. As a result of the manufacturing technology employed in FDM, inner structures are changed with different process parameters, and thus, anisotropic properties are observed. Moreover, composite filaments such as particle- or fiber-reinforced polymers already have anisotropy before FDM printing. In this study, we investigate the effect of different process parameters, namely layer thickness and raster width on FDM-printed copper-reinforced poly(lactic acid) (PLA). Mechanical characterizations with a high-resolution camera are carried out for analyzing the deformation behaviors. Optical microscopy characterizations are performed to observe the mesostructural changes with various process parameters. Scanning electron microscopy (SEM) and an energy-dispersive X-ray spectroscopy (EDS) analysis are conducted for investigating the microstructure, specifically, copper particles in the PLA matrix. A 2D digital image correlation code with a machine learning algorithm is applied to the optical characterization and SEM-EDS images. In this way, micro- and mesostructural features, as well as the porosity ratios of the specimens are investigated. We prepare the multiscale homogenization by finite element method (FEM) simulations to capture the material’s response, both on a microscale and a mesoscale. We determined that the mesostructure and, thereby, the mechanical properties are significantly changed with the aforementioned process parameters. A lower layer thickness and a greater raster width led to a higher elasticity modulus and ultimate tensile strength (UTS). The optical microscopy analysis verified this statement: Decreasing the layer thickness and increasing the raster width result in larger contact lines between adjacent layers and, hence, lower porosity on the mesoscale. Realistic CAD images were prepared regarding the mesostructural differences and porosity ratios. Ultimately, all these changes are accurately modeled with mesoscale and multiscale simulations. The simulation results are validated by laboratory experiments.

Dynamics of the Atlantic Marine Intertropical Convergence Zone

AbstractA generalized ω‐equation is used to identify the contributions from different processes that force upward motions in the Atlantic Marine ITCZ (AMI) from a numerical mesoscale simulation of June 2010. This ω‐equation separates the diabatic heating contributions, which lie at the core of the Weak Temperature Gradient (WTG) framework, from the dynamical terms. Three layers of atmosphere are found with different balance. In the Marine Atmospheric Boundary‐Layer (MABL), the upward motions in the AMI are induced by the frontogenesis and buoyancy components, which are regulated by the ageostrophic adjustment due to the presence of thermal‐wind imbalance. The balance of these three processes well captures the variability of the vertical velocity and the associated precipitation, meaning that boundary‐layer processes play a central role in the AMI dynamics. In the layer [600–2,000 m], a zone of strong vertical wind‐shear just above the MABL, the upward motions are induced by the ageostrophic adjustment and radiative components, which are counteracted by evaporation of convective precipitation. Above 2,000 m the ascending motions are driven by the deep convection heating, as expected by the WTG framework, and more surprisingly by the ageostrophic adjustment term within the Tropical Easterly Jet. Thanks to the use of the ω‐equation, these results extend the current WTG framework to the boundary layer, where it is not expected to hold. In the free troposphere, the WTG framework only accounts for half of the AMI ascent, the other half being forced by the dynamical terms.

Dynamics of gap winds in the Great Rift Valley, Ethiopia: emphasis on strong winds at Lake Abaya

Abstract. Lake Abaya, located in the Great Rift Valley (GRV) in Ethiopia, is affected by regularly occurring strong winds that cause water waves, which in turn affect the lake's ecology and food web. The driving forces for these winds, however, are yet unexplained. Hence, the main goal of this study is to provide a physical explanation for the formation of these strong winds in the GRV and especially at Lake Abaya. To this aim, two case studies were performed based on measurements, ERA5 reanalysis data and mesoscale numerical simulations conducted with the Weather Research and Forecasting (WRF) model. The simulations revealed that in both cases a gap flow downstream of the narrowest and highest part of the GRV (i.e. the pass) led to high wind speeds of up to 25 m s−1. Two types of gap flow were identified: a north-eastern gap flow and a south-western gap flow. The wind directions are in line with the orientation of the valley axis and depend on the air mass distribution north and south of the valley and the resulting along-valley pressure gradient. The air mass distribution was determined by the position of the Intertropical Convergence Zone relative to the GRV. The colder air mass was upstream of the GRV in both case studies. During the day, the convective boundary layer in the warmer air mass on the downstream side heated up more strongly and quickly than in the colder air mass. The most suitable variable describing the timing of the gap flow was found to be the pressure gradient at pass height, which corresponds roughly to the 800 hPa pressure level. In both cases the gap flow exhibited a strong daily cycle, which illustrates the importance of the thermal forcing due to differential heating over complex terrain in addition to the large-scale forcing due to air mass differences. The start, strength, and the duration of the gap winds within the valley depended on location. For both cases, the strongest winds occurred after sunset and in the ongoing night downstream of the gap and on the corresponding lee slope. The ERA5 reanalysis captures both events qualitatively well but with weaker wind speeds than in the mesoscale numerical simulations. Hence, ERA5 is suitable for a future climatological analysis of these gap flows.

Predictive Mesoscale Simulation of Flow-Induced Blend Morphology, Interfacial Relaxation, and Linear Viscoelasticity of Polymer–Elastomer Blends

Mesoscale simulations of two (cross-linking vs non-cross-linking) representative polymer–elastomer (Nylon 6/ACM) blends are performed without introducing freely adjustable parameters. The simulation allows for a direct investigation into the interplay between flow history (modeled herein by oscillatory shear flow), blend morphology, and viscoelasticity of the polymer composite. Detailed spectral analysis is utilized to resolve the stress relaxations in the bulk and interface regions, respectively, in an effort to gain insight into the origins of blend elasticity. The results reveal a notable impact of flow history on the blend morphology, and the substantially altered storage modulus response in the terminal regime agrees well with early experimental observations on a broad variety of immiscible polymer blends. The interfacial relaxation, in particular, is found to formally commence when the stress relaxations of individual polymer components in the bulk phase are nearly complete, helping to release the remaining stress that was previously dictated by repulsion-driven tube dilations─instead of chain reptation─in the interface region. Importantly, the last feature suggests that the bulk and interfacial relaxations of a polymer blend cannot be fully decoupled. Cross-linking between the elastomers (ACM) adds further complexity to the features of storage modulus response while partly offsetting the flow-induced phase separation. The generality of the simulation protocols described herein along with the affordable computational expense is expected to greatly facilitate future processing designs on polymer–polymer and polymer–elastomer blends.

Mesoscale Machine Learning Analytics for Electrode Property Estimation

The development of next-generation batteries with high areal and volumetric energy density requires the use of high active material mass loading electrodes. This typically reduces the power density, but the push for rapid charging has propelled innovation in microstructure design for improved transport and electrochemical conversion efficiency. This requires accurate effective electrode property estimation, such as tortuosity, electronic conductivity, and interfacial area. Obtaining this information solely from experiments and 3D mesoscale simulations is time-consuming while empirical relations are limited to simplified microstructure geometry. In this work, we propose an alternate route for rapid characterization of electrode microstructural effective properties using machine learning (ML). Using the Li-ion battery graphite anode electrode as an exemplar system, we generate a comprehensive data set of ∼17 000 electrode microstructures. These consist of various shapes, sizes, orientations, and chemical compositions, and characterize their effective properties using 3D mesoscale simulations. A low dimensional representation of each microstructure is achieved by calculating a set of comprehensive physical descriptors and eliminating redundant features. The mesoscale ML analytics based on porous electrode microstructural characteristics achieves prediction accuracy of more than 90% for effective property estimation.

Impact of urban heat island on cooling energy demand for residential building in Montreal using meteorological simulations and weather station observations

Urban heat island (UHI) and the increased frequency of heatwaves due to climate change reduce thermal comfort inside buildings leading to increased use of air conditioning systems. In this research, the impact of the actual local climate on the cooling energy demand of residential buildings in Montreal is studied. Building energy simulations (BES) are conducted for microclimates at eight locations in the Montreal area and compared with a reference weather data. The climate data for four locations are provided by weather stations. The other climate data are simulated with a detailed weather research and forecasting (WRF) model at 250 m resolution with detailed land-use data over a period of four months during the summer of 2020. The air temperatures from meteorological mesoscale simulations are validated with available weather station observations. The BES results show an increase in the cooling energy demand due to higher air temperatures at urban locations compared to the rural periphery. The cooling energy demand varies significantly over the eight locations although being within a radius of 20 km. The mean variation in cooling energy demand between the locations amounts to 14 % compared to the average cooling demand. Using the reference climate data provided in BES, the cooling energy demand is significantly underestimated by 25 % to 34 % on average. Increasing the thermostat cooling setpoint with 1 °C results in a reduction in mean cooling energy demand of 4.5 kWh/m2, or 11.7 %. A linear trend is found between the cooling energy demand from BES and cooling degree hours (CDH) indicating CDH can be used as a first indication for building energy demand.

Computing diffraction patterns of microstructures from phase-field simulations

The diffraction pattern of a material contains information not only on the crystal structures of its constituting phases, but also on its mesoscale spatial distributions of phases, grains, and ferroelastic, ferroelectric, and ferromagnetic domains. While diffraction patterns from experiments such as X-ray diffraction are presented in the reciprocal or Fourier space, mesoscale microstructure models such as the phase-field method naturally produce real-space images of spatial distribution of chemical composition, structural, and ferroic domains. Although one could rather readily compute the Fourier amplitudes of chemical and structural domain distributions generated by mesoscale simulations, they only contain information about the length scale and alignment of the real-space chemical and structure domains. Therefore, a direct comparison between diffraction experiments and mesoscale microstructure simulations is not possible. Here, we develop a theoretical approach to directly compute the crystal diffraction patterns of microstructures predicted by phase-field simulations. In particular, we consider five representative examples of microstructure patterns involving purely compositional domains, a single pair of tetragonal twin structures, multiple twin variants in a hexagonal system, ferroelectric polar vortices, and polycrystalline grains. The results are compared with previous experimental observations as well as X-ray diffraction experiments performed in the present study. The theoretical framework allows one to directly connect material microstructures and diffraction patterns predicted from phase-field simulations and the corresponding diffraction patterns from experiments, and thus providing guidance to experimental diffraction characterization and interpretation of microstructures.

Interaction of the Sea Breeze with the Urban Area of Rome: WRF Mesoscale and WRF Large-Eddy Simulations Compared to Ground-Based Observations

The Weather Research and Forecast (WRF) model is used to simulate atmospheric circulation during the summer season in a coastal region of central Italy, including the city of Rome. The time series of surface air temperature, wind speed, and direction are compared with in situ observations in urban Rome and its rural surroundings. Moreover, the vertical wind profiles are compared to sodar urban measurements. To improve the WRF model’s ability to reproduce the local circulation, and the onset and propagation of the sea breeze, several simulations are carried out modifying the land use and the thermal and physical properties of the surfaces. Based on the results of the correlation coefficient and the RMSE, the heat capacity and albedo are the parameters mostly influencing the daily temperature cycle. Particularly, the temperature in the urban area is reproduced more realistically when the heat capacity is increased. Hence, the best simulations are used to initialize a large-eddy simulation at high spatial resolution to analyze the interaction between the sea breeze and the urban heat island and to investigate the interaction of the sea breeze front with orography and surface roughness. As confirmed by observations collected by in situ weather stations in the surroundings of Rome, the front, entering the city, splits into three branches: (i) a west component in the western flank of the city, closer to the sea; (ii) a north-west component in the northern, inland side, and (iii) a south-west component in the south area of the city.

On the Contributions of the Northeast Cold Vortex to the Formation and Evolution of Backflow Blizzard

Blizzard is a severe weather-related disaster with significant environmental, economic, and social impacts. What is worse, the blizzard is increasingly frequent along with climate change. To enhance resilience, it is important to accurately estimate the blizzards. While some studies have reported the blizzard formation and characteristics, limited studies have not well presented the backflow blizzard associated with the cold vortex in Northeast China. In order to overcome this research gap, this study aims to analyze the characteristics of a backflow blizzard in Liaoning, China, and reveal the reasons behind the spatial heterogeneity of snowstorm intensity and duration. With observation data and mesoscale numerical simulation, this study discovered that the northeast cold vortex was the combined results of airflows from the Sea of Japan transported by the easterly airflow, the East China Sea, and the Yellow Sea transported by the southeast airflow, and the low-level southwest airflow, and the vortex was an important driver to the blizzard. Results further indicate that the interactions of airflow movement, water vapor variation, and frontogenesis occurrence at different layers caused the differences in snowfall intensity, duration, and volume at Zhangwu and Huanren stations. In particular, at Zhangwu station, there was an interaction of warm-wet and cold-dry airflows, but low-layer water vapor content was small, under which background only the cold air pushed the warm air as the southwest wind did not increase significantly, leading warm-humid air to be forced to climb and resulting in frontogenesis at 700–925 hPa. Therefore, the situation of no frontogenesis and poor humidity conditions in the low-level cold air contributed to the weak and a short-duration of snowfall at Zhangwu Station. The water vapor content of warm-wet and cold-wet airflows over Huanren station was better than that at Zhangwu so that during cold and warm air interaction, there was frontogenesis at 500–925 hPa. Near the top of the inverted trough in the ground layer, there was a convergence of the southeasterly wind and the northeasterly winds, resulting in the occurrence of horizontal frontogenesis throughout the layer of 500 hPa and forming an upward movement column. Moreover, a deep near-saturated water vapor layer formed below 600 hPa due to the upward movement column. Overall, the abundant water vapor combined with the deep dynamic uplift led to the heavy and long duration of snowfall at the Huanren station. Overall, this study is an important reference for understanding the backflow blizzard and its mechanism. Moreover, it is conducive to the accurate estimation of backflow blizzards in Northeast China and eastern China, the eastern coast of the Korean peninsula, and other similar areas where on the east part of the continent is the ocean.

Random Generation of Lattice Structures with Short-Range Order

Thermodynamically driven short-range order (SRO) is known to exist in multi-component alloys and can influence their mechanical properties. Atomistic and mesoscale simulations are frequently used to study the effects of SRO, but introducing SRO into the lattice usually requires costly Monte Carlo simulations. Here, we develop a novel method and open-source code to create lattices with specified SRO parameters that can be used as inputs to simulations to study the interaction of SRO with dislocations or other deformation mechanisms. The method, order through informed swapping (OTIS), uses a statistical smart swapping procedure to create lattices with known SRO parameters from an initially random lattice. We illustrate the use of OTIS on ternary and quinary equimolar alloys to create both BCC and FCC lattices with hundreds of thousands of atoms. The computation time and size scaling are discussed. As an example application of the method, the generated SRO lattices are incorporated into a phase-field dislocation dynamics to study dislocation loop growth.

Gecko Adhesion on Flat and Rough Surfaces: Simulations with a Multi‐Scale Molecular Model

A multiscale modeling approach is used to develop a particle-based mesoscale gecko spatula model that is able to link atomistic simulations and mesoscale (0.44 µm) simulations. It is used to study the detachment of spatulae from flat as well as nanostructured surfaces. The spatula model is based on microscopical information about spatulae structure and on atomistic molecular simulation results. Target properties for the coarse-graining result from a united-atom model of gecko keratin in periodic boundary conditions (PBC), previously developed by the authors. Pull-off forces necessary to detach gecko keratin under 2D PBC parallel to the surface are previously overestimated when only a small region of a spatula is examined. It is shown here that this is due to the restricted geometry (i.e., missing peel-off mode) and not model parameters. The spatula model peels off when pulled away from a surface, both in the molecular picture of the pull-off process and in the force-extension curve of non-equilibrium simulations mimicking single-spatula detachment studied with atomic force microscopy equipment. The force field and spatula model can reproduce experimental pull-off forces. Inspired by experimental results, the underlying mechanism that causes pull-off forces to be at a minimum on surfaces of varying roughnesses is also investigated. A clear sigmoidal increase in the pull-off force of spatulae with surface roughness shows that adhesion is determined by the ratio between spatula pad area and the area between surface peaks. Experiments showed a correlation with root-mean-square roughness of the surface, but the results of this work indicate that this is not a causality but depends on the areaaccessible.