Micro-scale Model Research Articles

A discrete event approach to micro-scale traffic modeling in urban environment

In this work, we present a new approach based on a discrete event formalism to model and simulate micro-scale urban traffic systems. The formalism is a coupling between the P-DEVS (Parallel-Discrete Event System Specification) formalism and UML (Unified Modeling Language) state machines. A system is represented by a set of coupled components. Each component supports the dynamics and logic of a system element. The models presented include the streets, intersections and traffic signs, all of which can be synchronized together through specific mechanisms. These models can be applied to real-world OpenStreetMap networks. A discrete event-driven adaptation of the simplified Gipps car-following model is introduced, and subsequently compared to its discrete time counterpart. The results show that our discrete event model follows dynamics which are similar to those of a discrete time model with a low update time step of 0.1s, despite not taking certain non-linearities of the latter into account. In terms of vehicle state changes and computation time, our approach outperforms the discrete time one with an update time step of 1s, both on a simple case study and on a real network.

Numerical investigation and experimental validation of aluminized propellant combustion under high pressures: Critical effects of heat feedback

Aluminum is a common metal fuel in propellants, but its effects on the burning rate and the underlying mechanism are still unclear. Numerical calculations and temperature measurement experiments were carried out within the pressure range of 0.1–9 MPa, and the effects of the pressure and the ammonium perchlorate (AP) particle size on the combustion characteristics of aluminized propellants were studied. First, based on a two-dimensional micro-scale combustion model of an AP/hydroxyl‑terminated polybutadiene (HTPB) propellant, by considering the “heat sink” effect of aluminum on the burning surface and the chemical reaction of aluminum in the gas phase, a numerical calculation model of heat flux coupled with the steady-state combustion of an aluminized propellant was established. The flame structure of an aluminized propellant obtained by this model conformed to the multi-flame structure and could correctly reflect the influence of the AP particle size and pressure on the burning rate and other combustion characteristics. Subsequently, the influence of radiation heat feedback was further introduced into the model, which greatly improved its accuracy in predicting the burning rate at low pressures of 1–3 MPa. More importantly, the temperature distributions of the propellants were measured using embedded tungsten–rhenium thermocouples, and the flame temperature was obtained by further processing the temperature measurement data according to the thermocouple response characteristics. The temperature measurement results showed that with the increase in the pressure and the decrease in the size of the AP particles, the temperature gradient increased significantly, the flame temperature increased slightly, and the burning rate of the propellant increased. By analyzing how the temperature gradient and the burning rate changed with pressure, it was found that the conduction heat feedback was important to determine the burning rate.

Upscaling coarse-grained simulation study for hydrated cement paste from mesoscale to microscale

In this paper, upscaled mesoscale and microscale models are introduced for hydrated cementitious materials by employing the discrete element method and coarse-grained (CG) simulation. At the mesoscale, the hydration process is simulated using the grand canonical Monte Carlo (GCMC) process. Subsequently, an exploration of dynamic, structural, and mechanical properties ensues through mean square displacement (MSD), radial distribution function (RDF), pore systems analysis, and uniaxial tension tests are conducted based on the constructed CG C–S–H gel model. The structural and mechanical performance of this model affirms its consistency with previous experimental investigations. On the other hand, the microscale model is established based on an existing microstructure model of cement paste. Mechanical properties and failure mechanism analyses, influenced significantly by the inner microstructure and components (specifically the degree of hydration with a fixed water-to-cement ratio), are investigated using the uniaxial tension test. Additionally, the microscale simulation serves to validate the feasibility of modeling micro-scale cementitious materials with the discrete CG-MD method.

Modeling NO2 concentrations in real urban areas using computational fluid dynamics: A comparative analysis of methods to assess NO2 concentrations from NOx dispersion results

Major cities worldwide constantly deal with health hazards caused by air pollution. Modeling this pollution on an urban scale is essential for assessing the impact of local policies and promoting sustainable urban development. However, there are practical difficulties when using microscale modeling in applied context, and particularly for nitrogen dioxide modeling (NO2). In this study, a Computational Fluid Dynamics (CFD) model was employed to assess monthly NO2 concentrations in Antwerp, Belgium, and the results were compared to a one-month measurement campaign using 73 passive samplers. The result showed that using CFD with conventional assumption – such as neutral atmospheric stability consideration and using a turbulent Schmidt number (Sct) set to 0.7 – yield satisfying results according to air quality model acceptance criteria. Optimal outcomes were achieved by considering NO2 background concentration instead of NOx and employing Bachlin et al.’s empirical function to convert modeled NOx concentrations to NO2, dismissing the need for straightforward chemical mechanisms – such as photostationary steady-state equilibrium (PSS) –, or more expensive models in terms of computing resources. This approach yielded an overall error of less than 15 % and a correlation coefficient R of 0.78, affirming its effectiveness in modeling NO2 air quality in applied context.

Review on Design, Characterization, and Prediction of Performance for Asphalt Materials and Asphalt Pavement Using Multi-Scale Numerical Simulation.

Asphalt pavement, which is mainly made up of the asphalt mixture, exhibits complicated mechanical behaviors under the combined effects of moving vehicle loads and external service environments. Multi-scale numerical simulation can well characterize behaviors of asphalt materials and asphalt pavement, and the essential research progress is systematically summarized from an entire view. This paper reviews extensive research works concerning aspects of the design, characterization, and prediction of performance for asphalt materials and asphalt pavement based on multi-scale numerical simulation. Firstly, full-scale performance modeling on asphalt pavement is discussed from aspects of structural dynamic response, structural and material evaluation, and wheel-pavement interaction. The correlation between asphalt material properties and pavement performance is also analyzed, and so is the hydroplaning phenomenon. Macro- and mesoscale simulations on the mechanical property characterization of the asphalt mixture and its components are then investigated, while virtual proportion design for the asphalt mixture is introduced. Features of two-dimensional and three-dimensional microscale modeling on the asphalt mixture are summarized, followed by molecular dynamics simulation on asphalt binders, aggregates, and their interface, while nanoscale behavior modeling on asphalt binders is presented. Finally, aspects that need more attention concerning this study's topic are discussed, and several suggestions for future investigations are also presented.

Microscale and mesoscale modeling for compressive failure in unidirectional carbon fiber reinforced polymer composites with random distribution of fiber misalignments

AbstractCompressive strength and failure mode of carbon fiber reinforced polymer (CFRP) composites are affected by the severity of fiber misalignment. In this paper, numerical models based on computational micromechanics are proposed to investigate the correlation between fiber misalignment and compression failure. A single‐fiber micromodel including fiber, matrix, and interface phases is first developed to capture the influence of misalignment severity on the material failure mode. The failure longitudinal stress distribution in the fiber predicted by the micromodel indicates that the failure mode depending on the initial misalignment can be identified as uniform compression failure, compression‐bending combination collapse, and fiber buckle. A mesoscale 2D model with multiple fibers is then constructed to investigate the kink band formation. This model takes into account the distribution of fiber misalignments to capture the key geometric features of fiber misalignment that influent the compression failure in realistic composite materials at the structural scale. The results show that fiber misalignment angle amplitude, misalignment dispersion (characterized by the standard deviation), and misalignment wavelength are the key parameters of fiber misalignment, which have significant effects on compression strength and kink band geometry.Highlights Single‐fiber model to capture the effect of misalignment severity on failure mode. Multi‐fiber model with randomly distributed fiber misalignments. Failure mode depending on the severity of initial misalignment was identified. Key parameters of misalignment on compression strength and kink band geometry.

A study on the tensile fracture behavior of polypropylene fiber reinforced concrete based on a microscale model

Polypropylene fibers are distributed in concrete with varying shapes and quantities, which is not conducive to microscopic modeling and analysis. This study aims to establish an effective and efficient crack calculation model suitable for polypropylene fiber-reinforced concrete (PFRC) to reveal the reinforcement mechanism of polypropylene fiber-reinforced concrete. Firstly, from a microscale perspective, PFRC was regarded as a three-phase heterogeneous material comprising aggregate, mortar (with embedded polypropylene fibers), and interfacial transition zone (ITZ). Three-point bending and splitting tensile tests were conducted on mortar-aggregate composite and mortar specimens with the fiber volume fraction as a variable. Furthermore, Peak stress ratio and fracture energy ratio were introduced to characterize the modification effect of polypropylene fibers. The influence of polypropylene fibers on the mechanical properties of mortar and ITZ was investigated, and the modification mechanism of polypropylene fibers on concrete was analyzed from a microscale perspective. Finally, a comprehensive microscale fiber-reinforced concrete cracking finite element model was established based on the results of microscale experiments using two-dimensional image recognition and three-dimensional numerical simulation methods. The research results show that PP fibers can significantly enhance the mechanical properties of mortar, with flexural and tensile strength increased by 9.6–15.0% and 9.5–21.9%, and the maximum fracture energy increased by 88.7%. The hydrophobicity of PP fibers limits the elimination of bubbles, increases the porosity of ITZ, reduces the bonding strength of ITZ, and after maximum weakening, the flexural strength, tensile strength, and fracture energy of ITZ are only 74.3%, 79.1%, and 37.6% of those without fiber doping. The microanalysis model established based on this can effectively describe the relevant concrete tensile fracture process indicators. The ratio of the stress peak calculated by the 3D model to the test is 0.997, while the ratio of the stress peak calculated by the 2D model to the test is only 0.706. The simulation effect of the 3D model is closer to reality. The influence of ITZ on the peak stress of concrete is more significant than that of mortar, and improving the weakening effect of PP fibers on the bonding strength of ITZ is the key to improving the macroscopic mechanical properties of PFRC.

An Initial Case Study of the Computational Modeling of Cord–Rubber Segments

This paper presents an initial study of the computational modeling for determining the stress–strain behavior of the cord–rubber segment, tailored to be used as a flexible member in the construction of torque transfer coupling in railway vehicles. The presented computational models use the multibody simulation approach for the assessment of segment deformation under diverse loading characteristics. The results are then used in the finite element computational models of the cord–rubber segment. The computational model is validated through tension tests of cord–rubber segments. The homogenized and microstructural models show their ability to simulate the overall stiffness of the segment relatively accurately in tension, but they are limited in not being able to include the influence of the segment’s manufacturing technology. The micro-scale computational model then incorporates realistic representations of cord bundles, considering various cross-sectional shapes, whereas a very important factor significantly influencing the correlation of the results of the technical experiment and simulation is the consideration of the manufacturing technology in the process of building the computational model. The results show that the computational modeling approach in this work can be used for the determination of the optimal manufacturing technology conditions with regard to the stress–strain behavior of the given segment.

Mesoscale modelling for wind resource evaluation purposes. A test case in complex terrain

The aim of this work was the preliminary wind resource assessment of a complex terrain site, located in Bulgaria, using a virtual wind data series, taken from mesoscale modelling Weather Research and Forecasting system (WRF). The meso-micro scale coupling was carried out applying the virtual wind data series to the point where once a local wind measurement was conducted. This way, the comparison between the two data sets, virtual and local measurement, could be done. Using the mesoscale virtual information the characterization of the wind was done for the place of study. The microscale model Wind Atlas Analysis and Application Program (WASP) was used to make the wind resource mapping and the calculation of the estimated wind resource and annual production of energy, taking into account the defined configuration for the wind farm and the turbine model chosen. The obtained differences, in terms of the magnitude of the estimated resource and its own dominant orientation indicates that the application of mesoscale models in very complex topography terrains should be done with extreme care.

Coupling scales in process‐based soil organic carbon modeling including dynamic aggregation

AbstractBackgroundCarbon storage and turnover in soils depend on the interplay of soil architecture, microbial activities, and soil organic matter (SOM) dynamics. For a fundamental understanding of the mechanisms that drive these processes, not only the exploitation of advanced experimental techniques down to the nanoscale is necessary but also spatially explicit and dynamic image‐based modeling at the pore scale.AimWe present a modeling approach that is capable of transferring microscale information into macroscale simulations at the profile scale. This enables the prediction of future developments of carbon fluxes and the impact of changes in the environmental conditions linking scales.MethodWe consider a mathematical model for CO2 transport across soil profiles (macroscale), which is informed by a pore‐scale (microscale) model for C turnover. It allows for the dynamic, self‐organized re‐arrangement of solid building units, aggregates and particulate organic matter (POM) based on surface interactions, realized by a cellular automaton method, and explicitly takes spatial effects on POM turnover such as occlusion into account. We further include the macroscopic environmental conditions water saturation, POM content, and oxygen concentration.ResultsThe coupled simulations of macroscopic transport and pore‐scale carbon and aggregate turnover reveal the complex, nonlinear interplay of the underlying processes. Limitations by diffusive transport, oxygen availability, texture‐dependent occlusion and turnover of OM drive CO2 production and carbon storage.ConclusionsThis emphasizes the need for such micro–macro models exchanging information on different scales to investigate and quantify the effects of structural changes, variations in environmental conditions, or degradation processes on carbon turnover.

Evaluation of multiscale mechanisms of ultrasound-assisted extraction from porous plant materials: Experiment and modeling on this intensified process

Ultrasound-assisted extraction (UAE) is an intensified mass transfer process, which can utilize natural resources effectively, but still lacks detailed mechanisms for multiscale effects. This study investigates the mass transfer mechanisms of UAE combined with material’s pore structure at multiscale. Porous material was prepared by roasting green coffee beans (GCB) at 120 °C (RCB120) and 180 °C (RCB180), and their UAE efficiency for phytochemicals (caffeine, trigonelline, chlorogenic acid, caffeic acid) were evaluated by experiment and modeling. Besides, the physicochemical properties, mass transfer kinetics, and multi-physical field simulation were studied. Results indicated that positive synergy effects on extraction existed between ultrasound and material’s pore structure. Higher mass transfer coefficients of UAE (GCB 0.16 min−1, RCB120 0.38 min−1, RCB180 0.46 min−1) was achieved with higher total porosity (4.47 %, 9.17 %, 13.52 %) and connected porosity (0 %, 3.79 %, 5.98 %). Moreover, simulation results revealed that micro acoustic streaming and pressure difference around particles were the main driving force for enhancing mass transfer, and the velocity (0.29–0.36 m/s) increased with power density (0.64–1.01 W/mL). The microscale model proved that increased yield from UAE-RCB was attributed to internal convection diffusion within particles. This study exploited a novel benefit of ultrasound on extraction and inspired its future application in non-thermal food processing.

Prediction and adjustment of elastic properties in three‐dimensional orthogonal woven composites through dual‐scale modeling

AbstractThree‐dimensional (3D) orthogonal woven composites can overcome the drawback of weak interlaminar strengths in traditional laminated composites and exhibit excellent resistance to out‐of‐plane impact, however, the significant difference in properties in the in‐plane orthogonal direction will limit the potential range of application thereof. Predicting elastic properties and exploring methods of adjustment are therefore of paramount importance. Using computed tomography (CT) scanning to obtain detailed parameter models, a corresponding dual‐scale model of microscopic representative volume element (RVE) and mesoscopic RVE was established, and the accuracy of the model was experimentally validated, which can predict the elastic properties of materials. In addition, the influences of the yarn fiber volume fraction and weaving distance on elastic properties were evaluated using the elastic modulus visualization method, and a formula integrating these two factors is provided for adjustment of the in‐plane orthogonal Young's modulus.Highlights Experimentally validated microscale and mesoscale RVE models were established. The influences of factors on elastic properties were visualized. The relationship between Young's modulus and influencing factors was derived. A reliable strategy for adjusting the in‐plane Young's modulus was proposed.

Analytical simulation and experimental validation of viscoplastic bending response of textile-reinforced composites for CubeSats

This study introduces an innovative approach for analyzing bending deformation and strength in textile-reinforced laminated composites, which is crucial for CubeSat structures. Our research develops a dual-scale modelling framework: a microscale model capturing the detailed viscoelastic-viscoplastic behaviour of fibres and matrices and a mesoscale model that integrates this with textile geometry, advanced shear deformation theories, and distributed damage effects. Extensive laboratory experiments validate our model, confirming its accuracy in predicting the composite behaviour under varied conditions. This work notably enhances the understanding and prediction of textile-reinforced composites, offering significant implications for CubeSat structural design and performance.

Unlocking Efficiency in Radio-Frequency Heating: Eigenfrequency Analysis for Resonance Identification and Propagation Enhancement in Nigerian Tar Sands.

Nigerian bituminous tar sands are among the world's largest deposits of bitumen and heavy oil. They are estimated to contain 38-40 billion barrels of heavy oil and bitumen, spanning approximately 120 km in length and 4-6 km in breadth. With global commitments to net zero emissions and various energy transition plans, improvements in the recovery methods for heavy oil and bitumen are being sought. To address this, renewable energy electrothermal enhanced oil recovery is considered an eco-friendly alternative. In our study, we introduce a novel Reservoir-Waveguide-Debye model. This model explores the enhancement of penetration for radio-frequency electromagnetic (EM) waves, which can be generated from renewable energy sources. These waves facilitate the viscosity reduction of heavy oil and bitumen. Through a comprehensive 2D numerical simulation employing the bulk properties of bituminous tar sands, we assess the propagation of EM fields within porous media. We utilize the industrial heating radio-frequency bandwidth of 1-60 MHz to conduct frequency domain investigations. Our analysis delves into propagation modes using eigenfrequency analysis, pinpointing the EM resonance of the tar sands. Furthermore, we investigate the impact of mesh refinement on the EM eigenfrequencies of porous media at both the microscale (400 μm) and macroscale (100 m in radial distance). Our results demonstrate the occurrence of resonance phenomena at complex eigenfrequencies around 27.12 and 54.24 MHz in both the microscale and macroscale models of the bituminous sands. This breakthrough research offers promising insights into harnessing renewable energy-driven EM waves for efficient thermal recovery processes in the Nigerian bituminous tar sands, thus fostering sustainable and eco-friendly energy solutions.

Simulation of fiber-induced melt pressure fluctuations within large scale polymer composite deposition beads

The process-structure-property relationship in Large Area Additive Manufacturing (LAAM) technology is an ongoing area of research as the inherent microstructural properties (chiefly fibers and voids) affect the performance of printed parts. Unfortunately, we currently lack adequate understanding of micro void nucleation and evolution during the LAAM and fused deposition modelling (FDM) processes. Modeling of the polymer melt flow during the extrusion process is important in understanding the underlying microstructural formation and associated properties of the print, that determines the part performance in service conditions. In this paper we compute fiber-induced local pressure fluctuations which may promote void formation in the bead’s microstructure. On a macro-scale, we determine flow fields of a purely viscous, Newtonian planar polymer deposition flow through a LAAM nozzle which are utilized on a micro-scale model where we simulate the evolution of a single ellipsoidal fiber along streamlines obtained from the macro-model. On the micro-scale, we determine instantaneous values of the translational and rotational velocities of the rigid ellipsoidal fiber that satisfies a balance of hydrodynamic forces and couples on the fiber’s surface based on a Newton Raphson algorithm and we track the fiber’s motion along the flow path via an explicit numerical iterative algorithm. Model verification is achieved by benchmarking results with solutions from well-known Jeffery’s equation of motion of a particle in homogeneous simple shear flow. We account for rotary diffusivity due to short-range fiber-fiber interaction in the FEA simulation by determining an effective fluid domain size representative of the interaction coefficient of the melt flow through a correlation analysis that yields an equivalent steady state orientation based on the Advani-Tucker equation. We also consider different possible motions of the fiber along individual LAAM flow paths from a given set of random initial fiber conditions to determine pressure bounds on the fiber surface along each streamline. For improved computational efficiency, calculations are carried out with respect to the fiber’s local coordinate axes to overcome the rigor of adaptive remeshing during the quasi-transient analysis. Results show low pressure extremes near the fiber’s surface which varies across the printed bead as well as through its thickness. Discussion is provided to gain insight into the effect of low-pressure extremes on micro void formation, particularly at the nozzle exit and during die swell/expansion.

Uncovering the decomposition mechanism of nitrate ester plasticized polyether (NEPE): a neural network potential simulation.

Nitrate ester plasticized polyether (NEPE) propellants have attracted widespread attention due to their high energy density and excellent low-temperature mechanical properties. However, little is known about the thermal decomposition process of the NEPE propellant, particularly lacking microscale models and interaction mechanisms. This work aims to establish a high-precision and efficient neural network potential (NNP) model covering the NEPE matrix, describing its mechanical behavior and detailed thermal decomposition mechanisms. The model accuracy, including atomic energies and forces, was validated through density functional theory (DFT) results, and the NEPE propellant decomposition model was verified via molecular dynamics (MD) simulations with DFT precision. The results demonstrate that the NNP model accurately predicts the energies and forces of the NEPE matrix for single and mixed systems at the DFT-level precision, and reproduces the mechanical properties consistent with DFT calculations. Meanwhile, the thermal decomposition order of the NEPE matrix predicted by NNP is consistent with the experimental results, accurately capturing complex physical phenomena and detailed decomposition processes among components. It is also revealed that the addition of a binder can improve the stability of the propellant and extend its energy release time. This study applies innovative machine learning algorithms to develop an NNP computational model for the NEPE matrix with DFT precision, which is crucial for practical propellant formulation design.

Параллельная реализация микромасштабной модели турбулентного движения воздуха и переноса примеси с использованием технологии OpenMP

The work presents the developed microscale model of turbulent air movement and the transfer of passive gaseous pollutant in street canyons and city blocks. To parallelize this model, using the example of solving one transport equation, various parallel programming technologies were considered: MPI, OpenMP, OpenACC and CUDA. For each parallel implementation, the acceleration, efficiency and labor intensity of using the technology in question were assessed. Based on the results of computational experiments, the most suitable parallel programming technology – OpenMP for parallelizing the entire model was chosen. The results of using OpenMP in parallel implementation of a microscale model of turbulent air movement and pollutant transfer are presented.

Coaching Model To Build Entrepreneurship Among Youth Unemployment

Partners often try to start building productive business, but empowerment still weak due to limited knowledge, skills, health insurance capital, technology and markets. The purpose of this program is to create a new young entrepreneurs reliable, independent and competitive among the youth unemployment. The method that used to solve the problems of these partners through the facilitation of productive economic activity based micro-scale engineering model of coaching in the form of technological and social skills through training with a participatory approach, stimulus funding and mentoring (supervision, monitoring and evaluation). Result of the evaluation of the program show that the impact of the program on the social aspects of both partners is the entrepreneurial spirit is getting stronger which can be measured in absolute numbers with Likert scale indicates the value of a score of 3.5. so it can be concluded that both partners have an entrepreneurial spirit by 75%. The impact of technological engineering shows that partners have been able to increase the procurement of oyster mushroom baglog even by purchasing, increasing turnover and expanding their marketing reach and have even diversified their business in the form of selling oyster mushroom satay. As for the impact of other social engineering is both partners have started to master the techniques of business management reasonably well and being able to access market information, read the market opportunities, technology innovations through the internet facility and has begun marketing management including control of simple bookkeeping

CFD microscale modelling of flow behavior in different parts of a rotating packed bed

Process intensification (PI) is playing a key role in alleviating the challenge of reducing carbon footprint of many chemical processes and bringing down their development costs. Over the years, many PI technologies have been investigated with rotating packed bed (RPB) technology receiving much of the attention for its potential of significant intensification in terms of capital expenditure, operating costs, and hardware size. In this study, microscale CFD simulations of a rotating packed bed were conducted, and the results were validated with experimental data. The results show the strong relation between the reverse flow at the packing outer periphery and the gas maldistribution factor. The latter is mainly caused by the accelerating flow in the outer cavity. Inside the wire mesh packing, the gas flow is found to be almost fully uniform for nearly half of the total packing depth. Also, turbulent kinetic energy (TKE) levels at the packing outer edge are strongly linked to the slip tangential velocity component, while at its inner edge, they depend mainly on the radial packing velocity. The so-called gas end effect zone is detected by observing the TKE profiles near the packing outer edge. The latter accounts for less than 10% of the total packing depth. The validity of the widely used porous media model in RPBs’ packing for both radial and tangential directions is confirmed by the obtained results, but this excludes the packing inner and outer edges. In the inner cavity region, gas exhibits two distinctive behaviors and transits from free vortex flow to swirling flow as the flow becomes close to the vortex core. As a result of this transition, the increase in shear stress accelerates the decrease in the gas tangential velocity in the vortex core and help speed up the favorable pressure gradient and flow establishment beyond the vortex core.

Microscale modeling of the ductile fracture behavior of thin stainless steel sheets

This study aims to model the fracture behaviour of thin stainless steel sheets in the microscale, which are widely used in the manufacturing of thin-walled structures such as bipolar plates, while considering the effects of geometry and grain size. To achieve this, 304 austenitic stainless steel with two different thicknesses is heat-treated to obtain samples with distinctive grain sizes. Uniaxial tensile tests and cup drawing tests are performed on the resulting samples, and the fracture strains are measured using a digital image correlation system. The morphology of fracture surfaces is also analysed to understand fracture mechanisms in the microscale. A new ductile fracture model based on the normalized Cockcroft–Latham criterion is developed to take the size effect into account, which is then applied in finite element analysis to predict damage evolution and fracture initiation during the tests. The results reveal a significant reduction in the fracture strain with decreasing sheet thickness and increasing grain size. Furthermore, the fracture mode changed from tensile fracture of a polycrystalline metal to shear fracture of a single-crystal metal as the number of grains across the thickness decreased. It is confirmed that the proposed model accurately replicates the decrease of the fracture strain as the plastic deformation scaled down to the microscale and successfully predicts the displacement at the onset of fracture under different loading conditions. Based on these results, it can be concluded that the proposed model has great potential for predicting fracture in microforming processes.