Mesoscale Simulations Research Articles

Incorporating an urban canopy model with artificial water dissipation into weather research and forecasting: A case study for Beijing

AbstractUrban artificial water dissipation is a concomitant process of human water use in built‐up areas that can absorb heat through evapotranspiration, reduce air temperature, transfer surface water to the atmosphere, and participate in the urban water cycle. In the context of increased urbanization, the impact of artificial water dissipation on urban climates cannot be ignored. In this study, calculation models for artificial water dissipation from different underlying surface types (buildings, hardened ground, soil, and vegetation) were introduced into an urban canopy model and coupled with the weather research and forecasting (WRF) model for mesoscale weather simulations of the Beijing area. Observational datasets of temperature and humidity from automatic weather stations in Beijing were used for validation. Results showed that the coupled model could reproduce the temperature and humidity of urban weather stations in Beijing more accurately compared to simulations that did not employ an urban canopy model or that adopted a traditional urban canopy model. In the Beijing urban area, the latent heat flux of NON, urban canopy model, and artificial water dissipation into the UCM are approximately 0, 35, and 65 W/m2, respectively, and the anthropogenic latent heat is about 30 W/m2. Incorporating urban artificial water dissipation into the urban canopy model reduced the urban air temperature by 0.2°C and increased the specific humidity by 0.6 g/kg, alleviating urban heat island effects and increasing humidity. These findings indicate that artificial urban water dissipation plays an important role in urban weather and climate that should be considered by urban canopy models. In the future, this model can be coupled into WRF for more accurate mesoscale weather simulations.

Size effect of axially-loaded CFRP ring-confined circular concrete columns: 3D mesoscale simulation

Most of the available experimental research on Fiber reinforced polymer (FRP) confined concrete columns focus on the fully-confined and small-sized ones, while there are few studies on the axial compression behavior and size effect of FRP ring-confined concrete columns. In this study, a three-dimensional mesoscopic numerical model of Carbon Fiber Reinforced Polymer (CFRP) ring-confined concrete in a circular column was established, taking into account the heterogeneity of concrete and the interaction between CFRP and concrete. Based on the meso-scale simulation method, the failure of a total of 36 columns with different structural sizes (the maximum cross-sectional width is 1000 mm) were simulated, and the influences of FRP volumetric ratio and vertical confinement effectiveness coefficient on axial compression behavior and size effect was explored. Moreover, considering the influence of FRP volumetric ratio and vertical confinement effectiveness coefficient, a semi-empirical and semi-theoretical formula for describing the size effect on compressive strength was established by modifying Bažant’s size effect law for concrete materials, and it is suitable for fully-confined and partially-confined concrete columns. The accuracy and applicability of the proposed formula was verified by comparing with the test results and the predicted results.

3D meso-scale simulation of chloride ion transportation in cracked concrete considering aggregate morphology

Many experimental and numerical studies have been conducted to explain chloride ion penetration in concrete including aggregates, ITZ and mortar. To my best knowledge, there are few devotion on the relationship between three-dimensional real cracks and chloride ion transportation. Based on spherical harmonic theory, reconstruct the particle generated from smoothing Voronoi polyhedron cell by Loop subdivision to simulate the morphology of real aggregate, and this process performs relatively good efficiency in generating sets of concrete model with different features (mainly aggregate volume fraction, fractal dimension). Consequently, a novel method of representing crack characteristics based on spherical harmonic theory is proposed for analysis, construct a bridge between aggregate morphology and crack characteristics. In view of chloride penetration depth, crack changes the profiles significantly, which accounts for the phenomenon that the existence of crack makes statistical results of chloride concentration on different cross-sections of models fit well with bimodal Gaussian distribution. Volume fraction and morphology of aggregates are mainly correlated to decline rate of gap between chloride concentration of region near crack and the remaining. Both are meaningful for further predict the time to corrosion initiation in reinforced concrete structures in marine environment.

Predicting Mechanical Constitutive Laws of Elastomers with Mesoscale Simulations

Predicting Mechanical Constitutive Laws of Elastomers with Mesoscale Simulations

Self-Assembly Behaviors and Flow Properties of Amphiphiles by Mesoscale Simulations with Hydrodynamic Interactions

Self-Assembly Behaviors and Flow Properties of Amphiphiles by Mesoscale Simulations with Hydrodynamic Interactions

Data-driven prognostic model for temperature field in additive manufacturing based on the high-fidelity thermal-fluid flow simulation

The process-structure–property relationship for additive manufacturing (AM) is typically derived starting from the temperature profile which can be achieved by the meso-scale thermal-fluid flow simulation with huge computational cost. We propose a data-driven prognostic approach with specialized physical constraints to rapidly predict the temperature profiles. The dataset is constructed from the physics-based thermal-fluid flow simulation results under different manufacturing parameters. The temperature field around the molten pool region is statistically characterized by the function parameters of the individual isotherms, which are essentially the output of the data-driven model based on the input manufacturing parameters, while the temperature field is reconstructed using the interpolation approach based on the predicted isotherms. The data-driven predicted temperature profiles are validated against those from the thermal-fluid flow simulations, and then further applied in the thermal stress and grain growth simulations, of which the results are compared with those using the temperature profile directly from thermal-fluid flow simulations. The results demonstrate that our data-driven approach is highly feasible in predicting the geometry features of the isotherms and temperature profiles around the molten pool regions.

Rhamnolipid Biosurfactants for Oil Recovery: Salt Effects on the Structural Properties Investigated by Mesoscale Simulations.

Rhamnolipids (RLs) are biosurfactants produced by Pseudomonas. The biodegradability and the variety of their functionality make them suitable for environmental remediation and oil recovery. We use dissipative particle dynamics simulations to investigate the aggregation behaviors of ionic RL congeners with nonane in various operating conditions. Under zero-salinity conditions, all RL congeners studied here form small ellipsoidal clusters with detectable free surfactants. When salt ions are present, the electrostatic repulsion between the ionized heads is overcome, resulting in the formation of larger aggregates of unique structures. RLs with C10-alkyl tails tend to form elongated wormlike micelles, while RLs with C16-alkyl tails tend to form clusters in spherical symmetry, including vesicles. Di-rhamnolipids (dRLs) require stronger solvation than monorhamnolipids (mRLs) to form clusters, and the resulting size of micelles is decreased. The morphology of the mixed dRL/mRL/oil systems is controlled based on the type of the congeners and the oil contents. In addition, the divalent calcium ions are found to be influential to the structure of the micelles through different mechanisms. For 5 wt % salinity, the ionic RLs can form oil-swollen micelles up to a 1:1 surfactant-to-oil ratio, suggesting that ionic RLs are superb to act as cleaning agents for petroleum hydrocarbons in the marine area. These key findings may guide the design for RL-based washing techniques in enhanced oil recovery.

Effect of void positioning on the detonation sensitivity of a heterogeneous energetic material

Although it is well-established that voids profoundly influence the initiation and reaction behaviors of heterogeneous energetic materials such as polymer-bonded explosives (PBX) and propellants, there has been little study of how void location in different constituents in the microstructures of such materials affect the macroscale behavior. Here, we use three-dimensional (3D) mesoscale simulations to study how void placement within the reactive grains vs the polymer binder influences the shock-to-detonation transition in a polymer-bonded explosive. The material studied here has a microstructure comprised of 75% PETN (pentaerythritol tetranitrate) grains and 25% hydroxyl-terminated polybutadiene polymer binder by volume. Porosities up to 10% in the form of spherical voids distributed in both the grains and polymer are considered. An Arrhenius reactive burn relation is used to model the chemical kinetics of the PETN grains under shock loading, thereby resolving the heterogeneous detonation behavior of the PBX. The influence of void location on the shock initiation sensitivity of the material is quantitatively ranked by comparing the predicted run distance to detonation (RDD) for each sample. The analysis includes inherent quantification of uncertainties arising from the stochastic variations in the microstructure morphologies and void distributions by using statistically equivalent microstructure sample sets, leading to probabilistic formulations for the RDD as a function of shock pressure. The calculations reveal that the location of voids in the composite microstructure significantly affects the RDD. Specifically, voids exclusively within the grains cause the PBX to be more sensitive (having shorter RDD) than voids in the polymer binder. Unique probabilistic relationships are derived to map the probability of observing RDD for each void location material case, allowing for prediction of initiation behavior anywhere in the shock pressure–RDD space. These findings agree with trends reported in the literature.

Meso-scale simulation of energetic materials. I. A method for generating synthetic microstructures using deep feature representations

The response of a wide class of heterogeneous energetic materials (HEs) to loads is determined by dynamics at the meso-scale, i.e., by physicochemical processes in their underlying microstructure. Structure–property–performance (S–P–P) linkages for such materials can be developed in a multi-scale framework, connecting the physics and thermophysical properties at the meso-scale to response at the macro-scale. Due to the inherent stochasticity of the microstructure, ensembles of microstructures are required to conduct meso-scale simulations to establish S–P–P linkages. Here, a deep neural network-based method called deep feature representation is applied to generate a range of material microstructures from heterogeneous energetic materials to metal foams and metallic mixtures. The method allows for the generation of stochastic microstructures using a single real microstructure as the input and is not limited to low packing density or topological complexity of solids. In its application to pressed energetic materials, we show that qualitative and quantitative features of real (i.e., imaged) microstructures are captured in the synthetic microstructures. Therefore, a stochastic ensemble of synthetic microstructures can be created for use in reactive meso-scale simulations to relate the microstructures of HEs to their performance. While the focus is on pressed HE microstructures, we also show that the method is general and useful for generating microstructures for in silico experiments for a wide range of composite/multiphase materials, which can be used to establish S–P–P linkages.

Meso-scale simulation of energetic materials. II. Establishing structure–property linkages using synthetic microstructures

Meso-scale simulations of pressed energetic materials are performed using synthetic microstructures generated using deep feature representation, a deep convolutional neural network-based approach. Synthetic microstructures are shown to mimic real microstructures in the statistical representation of global and local features of micro-morphology for three different classes of pressed HMX with distinctive micro-structural characteristics. Direct numerical simulations of shock-loaded synthetic microstructures are performed to calculate the meso-scale reaction rates. For all three classes, the synthetic microstructures capture the effect of morphological uncertainties of real microstructures on the response to shock loading. The calculated reaction rates for different classes also compare well with those of the corresponding real microstructures. Thus, the article demonstrates that machine-generated ensembles of synthetic microstructures can be employed to derive structure–property–performance linkages of a wide class of real pressed energetic materials. The ability to manipulate the synthetic microstructures using deep learning-based approaches then provides an opportunity for material designers to develop and manufacture pressed energetic materials that can yield targeted performance.

Quantitative evaluation of steel corrosion induced deterioration in rubber concrete by integrating ultrasonic testing, machine learning and mesoscale simulation

Chloride-induced steel corrosion seriously affects the durability of reinforced concrete structures. Rubber concrete, an environmentally friendly construction material in which waste rubber is recycled as a concrete component, has demonstrated superior resistance to chloride-induced steel corrosion and the subsequent concrete deterioration. However, quantitative evaluation of the degree of deterioration in rubber concrete based on nondestructive detection is challenging due to the complexity of the material. In this paper, reinforced concrete specimens with rubber contents of 0, 10% and 20% are subjected to the electrochemically accelerated corrosion experiments and monitored by ultrasonic testing. Six machine learning models are trained by the data derived from the ultrasonic testing to predict the corrosion degree based on ultrasonic traits. The results show that the machine learning models except for the linear model can accurately and robustly predict the corrosion degree under the interference of outlier amplitude and size of training set. Furthermore, the corrosion-induced deterioration process is computed by mesoscale simulation based on the corrosion degree, so that the damages of specimens with different rubber contents are quantitatively evaluated. The experimental and computational studies prove that the addition of rubber into concrete effectively retards the corrosion of steel and the deterioration of concrete.

Size effect on compressive behaviour of stirrup-confined concrete columns

Numerous studies have indicated the existence of a size effect on the axial compressive behaviour of stirrup-confined concrete columns. However, most of these studies have focused on nominal compressive strength. Investigations of the size effect on the axial strain (at peak load) and of the descending branch are limited. In this study, the size effect behaviour of square stirrup-confined concrete columns under axial compression was explored, using a three-dimensional mesoscale simulation method. Based on the numerical and available experimental results, the influence of specimen size on peak axial stress (i.e. compressive strength), corresponding strain and softening rate was explored. Moreover, quantitative relationships between specimen size and peak axial stress, corresponding strain and softening rate for circular and square stirrup-confined concrete columns were derived. Finally, considering the size effect on the peak axial stress, corresponding strain and softening rate, a novel stress–strain model describing the axial compression behaviour of stirrup-confined concrete was developed. The proposed model was verified by comparison with the available experimental results and existing models.

Wind field numerical simulation in forested regions of complex terrain: A mesoscale study using WRF

The increasing human activity in forested regions requires a better understanding of the wind conditions. This paper aims to improve the wind simulation in forested regions of complex terrain, and thereby to investigate the effect of forest on wind characteristics. Considering the complexity of the surface, simulations were carried out covering different parameterization schemes sets using Weather Research and Forecasting model (WRF). The results indicate that Noah-MP scheme and YSU scheme are recommended for a mesoscale wind simulation in forested regions of complex terrain. Moreover, due to the complexities of land surface with seasonally-varied roughness length (RL) and leaf area index (LAI), localized LAIs and RLs would significantly improve wind field simulation. Comparisons demonstrated that the modified RLs, which can reshape the wind speed distribution, have greater impact than LAI on the wind field simulation in the near-surface level. The findings can be further exploited in the wind damage assessment for the infrastructures, as well as wind power assessment in forested regions.

Multi-Physics Mesoscale Substructure Analysis on Stress Wave Measurement within CFST-PZT Coupling Models for Interface Debonding Detection.

In recent years, the development of interface debonding defect detection methods for concrete-filled steel tubes (CFSTs) using stress wave measurement with piezoelectric-lead-zirconate-titanate (PZT) actuator and sensor has received significant attention. Because the concrete core in CFSTs is a heterogeneous material with randomness at the mesoscale, the size, position and distribution of aggregates unavoidably affect the stress wave propagation and the PZT sensor response. In this study, to efficiently investigate the influence of the mesoscale structure of the concrete core of CFSTs on the response of embedded PZT sensors, a multi-physics substructure model of CFST members coupled with a PZT actuator and a PZT sensor, where a single circular aggregate with different size and position and randomly distributed circular aggregates are considered, are established first. Then, multi-physics simulations on the effect of the local mesoscale structure of the concrete core on the response of the embedded PZT sensor excited by both a sinusoidal signal and sweep frequency signal are carried out. Moreover, corresponding multi-physics and mesoscale simulations on the embedded PZT sensor response of substructures with different interface debonding defects are also carried out for comparison. The amplitude and the wavelet packet energy of the embedded PZT sensor response of each mesoscale substructure are employed to distinguish the influence of the concrete core mesoscale structure and interface debonding defect on sensor measurement. The findings from the results with the multi-physics coupling substructure models are compared with those of the full CFST-PZT coupling models and the tested members of the previous studies to verify the rationality of the embedded PZT sensors measurement of the established substructure models. Results from this study show that the effect of interface debonding defect on the amplitude and the wavelet packet energy of the embedded PZT sensor measurement of the CFST members is dominant compared with the mesoscale heterogeneity and randomness of the concrete core.

Atomistic simulation toward real-scale microprocessor circuits

A highly efficient and novel atomistic simulation framework is first established for the thermal and mechanical behaviors of a whole microprocessor chip or its constituent functional modules, important for the performance and reliability of high-end microprocessor circuits. The largest simulated module contains about 55.3 thousand nano-transistors with around 107 billion atoms. Traditionally, the macroscopic continuous methods are difficult to treat nanoscale factors such as doping, thin dielectric layer, surface and interface in the nano-transistor devices, while the microscopic quantum mechanics method can only calculate one or several nano-transistors. This proposed simulation method realizes the integrated treatment of the above nanoscale factors and complex gate layout by coupling multiple interatomic potential models for different materials and designing efficient parallel algorithms, and bridges the mesoscale simulation gap between the aforementioned macroscopic and microscopic methods. The development provides the first atomic-scale simulation framework for predicting and modulating the thermal behavior of a microprocessor circuit or its functional module, which paves an exciting way to the atomic-resolution design of novel high-performance microprocessor chips in the post-Moore era.

Fatigue life estimating for chloride attacked RC beams using the S-N curve combined with mesoscale simulation of chloride ingress

Chloride ingress governs the rebar corrosion, significantly influencing the fatigue life of the RC structures. The S-N curve considering the chloride ingress is proposed based on the fatigue tests of nineteen beams. The mesoscale numerical method is applied to simulate the chloride ingress in fatigue-loaded concrete. The S-N curve combined with the mesoscale simulation of chloride ingress is proved to be reasonable for the fatigue life estimating. Additionally, it is found that water-to-cement ratio of ≤0.4 and aggregate content of ≥50% are suggested to improve the fatigue life, and stress level is better to consider for the durability design.

Error Features in Predicting Typhoon Winds: A Case Study Comparing Simulated and Measured Data

Simulating a typhoon’s wind field via mesoscale models is important in terms of providing not only the guidelines for urban planning and onshore/offshore constructions, but also the provision of insight into the dynamics and thermodynamics of tropical cyclone systems. Therefore, the errors that are contained in simulation results were investigated in the present study, in association with large-scale meteorological patterns and localized wind conditions in the typhoon boundary layer. In detail, the full-set three-dimensional simulations of three typhoon cases were carried out in order to provide the typhoon wind fields that were required to compare with the observations that were obtained through land weather stations and offshore buoys. Although the reliability of typhoon simulations has been thoroughly investigated, the previous works mostly concentrated on the configurations and dynamic core of the model. The present study reveals, however, the influences of the characteristics of the specific weather system on the simulation’s results, which provides the foundation for the proposition of empirical corrections to improve the mesoscale simulation results of typhoon wind fields without updating the model’s algorithm.

Mesoscale study on explosion-induced formation and thermochemical response of PTFE/Al granular jet

The dynamic formation, shock-induced inhomogeneous temperature rise and corresponding chemical reaction behaviors of PTFE/Al reactive liner shaped charge jet (RLSCJ) are investigated by the combination of mesoscale simulation, reaction kinetics and chemical energy release test. A two-dimensional granular model is developed with the randomly normal distribution of aluminum particle sizes and the particle delivery program. Then, the granular model is employed to study the shock-induced thermal behavior during the formation and extension processes of RLSCJ, as well as the temperature history curves of aluminum particles. The simulation results visualize the motion and temperature responses of the RLSCJ at the grain level, and further indicate that the aluminum particles are more likely to gather in the last two-thirds of the jet along its axis. Further analysis shows that the shock, collision, friction and deformation behaviors are all responsible for the steep temperature rise of the reactive jet. In addition, a shock-induced chemical reaction extent model of RLSCJ is built based on the combination of the Arrhenius model and the Avrami-Erofeev kinetic model, by which the chemical reaction growth behavior during the formation and extension stages is described quantitatively. The model indicates the reaction extent highly corresponds to the aluminum particle temperature history at the formation and extension stages. At last, a manometry chamber and the corresponding energy release model are used together to study the macroscopic chemical energy release characteristics of RLSCJ, by which the reaction extent model is verified.

Meso-scale numerical simulation and experimental verification of single grain grinding TiC–Fe composites

Grinding process of particle reinforced metal matrix composites (PRMMCs) is extremely challenging due to the addition of hard particles. To reveal the material removal mechanism, a meso-scale numerical model for single grain grinding of TiC–Fe composites was established, considering the random particle distribution model and cohesive zone model. The simulated grinding forces and machined surface topography based on the proposed model show a similar trend and were consistent with the experimental results. The transition stage of ductile-brittle removal of TiC–Fe composites was explored. Results indicated that the grinding forces increase abruptly due to the obstruction of high-strength TiC particles and fluctuate periodically in the Fe matrix. The grinding forces decrease with the elevated grinding speed. Additionally, the material removal processes at different stages were studied and the removal modes mainly include: matrix plastic removal, particles wear, particles debonding, particles fracture, particles pull out. This paper provides significant guidance for the digitization and information construction of PRMMCs machining in enterprises and increases the economic benefits for the manufacturing industry.

Modeling of the mechanical properties of fused deposition modeling (FDM) printed fiber reinforced thermoplastic composites by asymptotic homogenization

Fused deposition modeling (FDM) is a low-cost additive manufacturing method with moderate tolerances and high design flexibility. Ample studies are being undertaken for modeling the mechanical properties of FDM by using the Finite Element Method (FEM). The process technique of FDM results in anisotropic inner structures that are affected by the chosen manufacturing parameters. Moreover, composite filaments, such as fiber-reinforced polymers, have anisotropy even in filament form before FDM printing. These anisotropic effects are needed to be examined and incorporated for an adequate model. In order to speed up the design stage, we aim to prepare a practical method for simulating the mechanical properties of FDM-printed fiber-reinforced polymer composites. In this work, we computed the homogenized material properties for various fiber lengths, fiber volume percentages, and fiber orientations by asymptotic homogenization at the microscale. Then, mesoscale simulations are carried out through FEM simulations by incorporating the influences of process parameters. In this way, we demonstrate the effect of various micro- and mesostructural features on the homogenized properties step by step.