Mesoscale Model Research Articles

Numerical simulations of the splitting tensile behaviour of needle-punched carbon/carbon composites

Carbon/carbon (C/C) composites are materials developed for applications requiring excellent mechanical and thermal properties at elevated temperatures. Needle-punching has been used to improve the delamination resistance of C/C composites; however, its effect on tensile behaviour has yet to be fully understood. This work studies the splitting tensile behaviour of needle-punched C/C composites numerically using experimentally validated finite element (FE) simulations with a damage initiation criterion. The splitting tensile test, comprising the diametral loading of flattened Brazilian discs (FBDs), was employed for the simulations and validation. A mesoscale model of the composite FBD specimen was built, in which each composite layer was explicitly modelled, including cohesive interfacial behaviour. The composite needle fibre bundles were modelled using truss elements. The FE simulations showed the crucial role that needled fibre bundles play in the C/C composite tensile performance by carrying tensile stresses perpendicular to the loading direction and reducing the levels of localised deformation in the central region of the specimen at the early stages of loading. Furthermore, the damage initiation criterion showed that when the needle fibre bundles are included in the FE simulation, the extension of damaged areas is less than in the simulations without needled fibre bundles. In addition, the numerical results showed that increasing the number of needled fibre bundles reduced the extension of the damage in the matrix.

Investigations of ice impact damage and residual performance of 3D angle-interlock woven composites based on in-situ sampling

This paper investigates the ice impact damage and residual performance of three-dimensional (3D) angle-interlock woven composites. Clear ice projectiles (free of microbubbles and ice cracks) are produced using the unidirectional freezing method, and subsequently launched against composite panels at velocities ranging from 85 m/s to 145 m/s. The impact process and full-field deflection are recorded through a 3D high-speed digital image correlation (DIC) system, and post-impact analysis is conducted via C-scan and microscope observations. Unlike hard body impacts, almost all external damage caused by ice impacts initially appears on the front face rather than the back face. Moreover, to better characterize the impact resistance, an improved compression after impact (CAI) method based on in-situ sampling is proposed, and contour maps of 65%, 80%, and 95% are constructed to illustrate the quantitative relationship between impact energy and residual performance. In particular, the area enclosed by the 80% contour map closely aligns with the damaged area detected by C-scan imaging, reflecting the limitation of C-scan in detecting tiny cracks. Finally, a multi-scale model has also been established for mutual verification with ice impact tests, where the meso-scale model predicts necessary input parameters for the macro-scale model. The current work can provide valuable guidance for developing experimental standards and damage assessment systems for 3D woven composites under hail threats.

Mechanical behaviour analysis of cemented tailings rock backfill materials: an insight from mesoscale modelling coupled with damage plasticity model

Mechanical behaviour analysis of cemented tailings rock backfill materials: an insight from mesoscale modelling coupled with damage plasticity model

Comparing the outputs of general circulation and mesoscale models in the flood forecasts of mountainous basins

Comparing the outputs of general circulation and mesoscale models in the flood forecasts of mountainous basins

A non-isothermal phase-field crystal model with lattice expansion: analysis and benchmarks

Abstract We introduce a non-isothermal phase-field crystal model including heat flux and thermal expansion of the crystal lattice. The fundamental thermodynamic relation between internal energy and entropy, as well as entropy production, is derived analytically and further verified by numerical benchmark simulations. Furthermore, we examine how the different model parameters control density and temperature evolution during dendritic solidification through extensive parameter studies. Finally, we extend our framework to the modeling of open systems considering external mass and heat fluxes. This work sets the ground for a comprehensive mesoscale model of non-isothermal solidification including thermal expansion within an entropy-producing framework, and provides a benchmark for further meso- to macroscopic modeling of solidification.

Experimental study on flexural properties of superelastic SMA fiber reinforced ECC under monotonic load

Abstract To enhance the bending performance and crack control of ECC materials, a novel hybrid fiber-reinforced cement composite (SMAF-ECC) was developed by incorporating superelastic SMA fibers into PVA-ECC. Four-point bending tests under monotonic loading were performed to systematically examine the effects of SMA fiber content, diameter, and shape on the mechanical properties of thin plates, including cracking deflection, cracking strength, ultimate deflection, and flexural strength. Additionally, the bending toughness of the plates was assessed using the ASTM C108 toughness index. Moreover, a mesoscale numerical analysis model for SMAF-ECC thin plates was developed. The results indicate that SMA fibers notably enhance the flexural performance of the plates. As the SMA fiber content increases, both the initial cracking strength and ultimate deflection initially increase before decreasing, with optimal performance occurring at a fiber content of 0.5%, which improves these properties by 20.9% to 44.5% compared to specimens without SMA fibers. The flexural strength of the specimens continues to increase with fiber content, reaching a maximum improvement of 36.1%. Among the various fiber shapes, flat-headed SMA fibers exhibit the best performance in improving both flexural strength and ultimate deflection, with enhancements of 7.2% and 25.9%, respectively, compared to specimens without SMA fibers. Furthermore, the inclusion of SMA fibers markedly improves the bending toughness of the thin plates. The reliability of the numerical model was validated through comparisons between the simulation and experimental results.

Compromised 2-start zigzag chromatin folding in immature mouse retina cells driven by irregularly spaced nucleosomes with short DNA linkers.

The formation of condensed heterochromatin is critical for establishing cell-specific transcriptional programs. To reveal structural transitions underlying heterochromatin formation in maturing mouse rod photoreceptors, we apply cryo-EM tomography, AI-assisted deep denoising, and molecular modeling. We find that chromatin isolated from immature retina cells contains many closely apposed nucleosomes with extremely short or absent nucleosome linkers, which are inconsistent with the typical two-start zigzag chromatin folding. In mature retina cells, the fraction of short-linker nucleosomes is much lower, supporting stronger chromatin compaction. By Cryo-EM-assisted nucleosome interaction capture we observe that chromatin in immature retina is enriched with i±1 interactions while chromatin in mature retina contains predominantly i±2 interactions typical of the two-start zigzag. By mesoscale modeling and computational simulation, we clarify that the unusually short linkers typical of immature retina are sufficient to inhibit the two-start zigzag and chromatin compaction by the interference of very short linkers with linker DNA stems. We propose that this short linker composition renders nucleosome arrays more open in immature retina and that, as the linker DNA length increases in mature retina, chromatin fibers become globally condensed via tight zigzag folding. This mechanism may be broadly utilized to introduce higher chromatin folding entropy for epigenomic plasticity.

Evaluation of Concrete Structural Cracking Behavior Induced by Early Drying Shrinkage.

In this paper, the early drying shrinkage coefficients of different hydraulic cement mortars are calibrated through laboratory experiments for moderate-heat Portland cement (MHPC) and low-heat Portland cement (LHPC). By developing an improved mesoscale modeling approach, a 3D highly detailed simulation of concrete was generated, which incorporates the phases of mortar, aggregates, and interfacial transition zone (ITZ). The simulation result is in good agreement with the concrete early drying shrinkage experiment, exhibiting an error of less than 4.99% after 28 days. Subsequently, the mesoscale model is employed to explain the influence of the ambient humidity, cement type, and aggregate volume ratio on the early drying shrinkage performance of concrete. The results show that the early drying shrinkage coefficient of the LHPC is approximately 82% of the MHPC. Additionally, the depth of ambient humidity influence is about 15 mm from the concrete surface after 28 days. The early drying shrinkage can be controlled by increasing ambient humidity via the LHPC or raising the aggregate volume ratio. The mass-loss rate of concrete decreases as the ambient humidity or aggregate volume ratio increases during the process of drying shrinkage. Furthermore, the results quantify the influence patterns of various factors on drying shrinkage, thereby facilitating their application in assessing the cracking time induced by early drying shrinkage in roller-compacted concrete (RCC) dams. This provides theoretical guidance for crack prevention in concrete structures and aids in developing strategies for the construction of crack-free dams.

Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting

ABSTRACTAn accurate prediction of wind power generation is crucial for optimizing the integration of wind energy into the power grid, ensuring energy reliability. This research focuses on enhancing the accuracy of wind power generation forecasts by combining data from mesoscale and reanalysis models with Machine Learning (ML) approaches. We utilized WRF forecast data alongside ERA5 reanalysis data to estimate wind power generation for a wind farm located at Valladolid, Spain. The study evaluated the performance of ML models based on WRF and ERA5 data individually, as well as a combined model using inputs from both datasets. The hybrid model combining WRF and ERA5 data with ML resulted in a 15% improvement in root mean square error (RMSE) and a 10% increase in compared with standalone models, providing a more reliable 1‐h forecast of wind power generation. Additionally, the availability of data over time was addressed: WRF provides the advantage of projecting data into the future, whereas ERA5 offers retrospective data.

Mesoscale modelling of starch digestion

An idealised mesoscale model of the enzymatic digestion of starch modelled as a polymer aggregate is used to study the effect of various enzyme properties, such as the enzyme efficiency, range and radius, on the rate at which monomers are released from the aggregate. Depending on the enzyme efficiency the process is found to be either reaction- or diffusion-limited. Additionally the digestion rate is found to be proportional to the volume around each bond that is accessible to the enzyme, which is determined by the range and radius of the enzyme. Simulations of uniformly mixed susceptible and resistant polymers reveal no significant effect on the digestion of the susceptible polymers due to the presence of the resistant polymers.

An Efficient PINN–Based Calibration Method for Mesoscale Peridynamic Concrete Models

Mesoscale models are crucial for the refined analysis of material damage behaviors. However, it remains a challenging task to calibrate a mesoscale model so as to accurately simulate the mechanical behaviors (MBs) of macroscale structural components. The models may be nonlinear, involve numerous material parameters (MPs), and be large‐scale. In addition, solutions to inverse problems may lack accuracy or be nonunique. A recent emerging method, physics‐informed neural network (PINN), combines deep learning with physical laws to solve complex problems and significantly reduce computational costs. This paper presents an effective PINN approach for mesoscale model calibration. The approach establishes a relationship between the MPs of a mesoscale model and the MBs of structural components using PINN, with constraints based on known physical relationships. Both forward PINN (MPs as inputs and MBs as outputs) and reverse PINN (swapping inputs and outputs) models are used. Calibration is achieved efficiently by combining the forward PINN model with an optimization algorithm or directly using the reverse PINN model. Validation is performed using a mesoscale concrete model in peridynamics (PDs). The relationship between the elastic modulus of bonds in PD and MBs of components is constrained by physical laws. The datasets are generated through OpenSees analysis. The PINN method demonstrates its effectiveness, particularly with the reverse model, which is both efficient and accurate.

ECCSrm: A novel meso-scale finite element model to simulate shrinkage of engineered cementitious composites (ECCs) incorporaring fiber characteristics

ECCSrm: A novel meso-scale finite element model to simulate shrinkage of engineered cementitious composites (ECCs) incorporaring fiber characteristics

Obtaining the longitudinal compressive response of unidirectional laminate composites from fiber misalignment micrographs through machine learning

The longitudinal compressive behavior of unidirectional composite laminates with fiber waviness is highly complex and plays a crucial role in determining final failure of composites. Evaluating this behavior, especially considering nonlinear failure mechanisms like kink-band formation, typically requires computationally expensive finite element analysis, which is impractical for large-scale quality inspection. To address computational challenges, this paper developed a new computational framework using Convolutional Neural Network (CNN) models, providing an ultra-efficient prediction of the entire stress–strain curve of composites with fiber waviness. The CNN models were trained on simulation data generated from an experimentally validated mesoscale finite element model. The microstructures of the composites with fiber waviness were taken from realistic micrographs, resulting in diverse stress–strain curves. The proposed CNN models showed high accuracy and efficiency for predicting the highly nonlinear stress–strain curves of the composites, which can be employed as a real-time evaluation method of the criticality of fiber waviness.

Mesoscale modelling of fibrin clots: the interplay between rheology and microstructure at the gel point.

This study presents a numerical model for incipient fibrin-clot formation that captures characteristic rheological and microstructural features of the clot at the gel point. Using a mesoscale-clustering framework, we evaluate the effect of gel concentration or gel volume fraction and branching on the fractal dimension, the gel time, and the viscoelastic properties of the clots. We show that variations in the gel concentration of our model can reproduce the effect of thrombin in the formation of fibrin clots. In particular, the model reproduces the fractal dimension's dependency on gel concentration and the trends in elasticity and gelation time with varying thrombin concentrations. This approach allows us to accurately recreate the gelation point of fibrin-thrombin gels, highlighting the intricate process of fibrin polymerization and gel network formation. This is critical for applications in the clinical and bioengineering fields where precise control over the gelation process is required.

Atomistic-informed phase field modeling of magnesium twin growth by disconnections

The nucleation and propagation of disconnections play an essential role during twin growth. Atomistic methods can reveal such small structural features on twin facets and model their motion, yet are limited by the simulation length and time scales. Alternatively, mesoscale modeling approaches (such as the phase field method) address these constraints of atomistic simulations and can maintain atomic-level accuracy when integrated with atomic-level information. In this work, a phase field model is used to simulate the disconnection-mediated twinning, informed by molecular dynamics (MD) simulations. This work considers the specific case of the growth of {101¯2} twin in magnesium. MD simulations are first conducted to obtain the orientation-dependent interface mobility and motion threshold, and to simulate twin embryo growth and collect facet velocities, which can be used for calibrating the continuum model. The phase field disconnections model, based on the principle of minimum dissipation potential, provides the theoretical framework. This model incorporates a nonconvex grain boundary energy, elasticity and shear coupling, and simulates disconnections as a natural emergence under the elastic driving force. The phase field model is further optimized by including the anisotropic interface mobility and motion threshold suggested by MD simulations. Results agree with MD simulations of twin embryo growth in the aspects of final twin thickness, twin shape, and twin size, as well as the kinetic behavior of twin boundaries and twin tips. The simulated twin microstructure is also consistent with experimental observations, demonstrating the fidelity of the model.

Fire-induced damage behaviour in corrosion-damaged concrete: Thermal-mechanical coupling phase field meso-scale modeling

Fire-induced damage behaviour in corrosion-damaged concrete: Thermal-mechanical coupling phase field meso-scale modeling

DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map.

We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data. The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown proteins. We implemented our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All supplemental materials are available at osf.io/5tx4q.

Summer Low Clouds off Southeast of Hokkaido Simulated in the Japan Meteorological Agency Meso-Scale Model under Different Synoptic Conditions

Summer Low Clouds off Southeast of Hokkaido Simulated in the Japan Meteorological Agency Meso-Scale Model under Different Synoptic Conditions

Revealing formation mechanism of end of process depression in laser powder bed fusion by multi-physics meso-scale simulation

ABSTRACT Unintended end-of-process depression (EOPD) commonly occurs in laser powder bed fusion (LPBF), leading to poor surface quality and lower fatigue strength, especially for many implants. In this study, a high-fidelity multi-physics meso-scale simulation model is developed to uncover the forming mechanism of this defect. A defect-process map of the EOPD phenomenon is obtained using this simulation model. It is found that the EOPD formation mechanisms are different under distinct regions of process parameters. At low scanning speeds in keyhole mode, the long-lasting recoil pressure and the large temperature gradient easily induce EOPD. While at high scanning speeds in keyhole mode, the shallow molten pool morphology and the large solidification rate allow the keyhole to evolve into an EOPD quickly. Nevertheless, in the conduction mode, the Marangoni effects along with a faster solidification rate induce EOPD. Finally, a ‘step’ variable power strategy is proposed to optimise the EOPD defects for the case with high volumetric energy density at low scanning speeds. This work provides a profound understanding and valuable insights into the quality control of LPBF fabrication.

Mesoscale Modeling for Predicting Effective Properties and Damage Behavior of Geopolymer Concrete.

Geopolymer concrete is a sustainable construction material and is considered as a promising alternative to traditional Portland cement concrete. However, there is still not much research on the effective properties and damage behavior of geopolymer concrete with consideration of its heterogeneous characteristics by means of mesoscale models combined with the regularized microplane damage model. Here, in this research, an easy and simpler approach for generating concrete mesoscale models and characterizing the angular characteristics of aggregate particles is presented. After the proposed mesoscale modeling was validated by numerical, experimental and theoretical models, it was employed further to predict the effective properties and damage behavior of geopolymer concrete. The obtained results show that the effective elastic modulus and compressive strength of geopolymer concrete were greatly affected by the volume fractions of aggregate, while no significant influence on Poisson's ratio was found. The evolution of damage and coalescence of cracks were affected by the volume fractions and spatial distribution of aggregate particles, which resulted in the different failure patterns in the mesoscale model of geopolymer concrete manufactured by different volume ratios of aggregate.