Scale Simulations Research Articles

A viscoplastic constitutive model for plastic silts and clays for static slope stability applications

A viscoplastic model for representing plastic silts and clays in geotechnical static slope stability applications is presented. The PM4SiltR model builds on the stress ratio-controlled, critical state-based, bounding surface plasticity model PM4Silt and is coded as a dynamic link library for use in the finite difference program FLAC 8.1. PM4SiltR incorporates strain rate-dependent shear strength, stress relaxation, and creep using a consistency approach combined with an internal strain rate and auto-decay process. The model does not include a cap, and as such cannot simulate strain rate-dependent consolidation under increasing overburden stress. Six parameters control the viscous response for PM4SiltR while the parameters controlling the nonviscous components of the response are the same as for PM4Silt. Single element simulations are presented to illustrate the influence of viscoplasticity on the constitutive response in direct simple shear loading and undrained creep. Single element responses are shown to be consistent with observed experimental results. Simulations of a hypothetical tailings dam constructed using the upstream method are performed to illustrate use of PM4SiltR at field scale. Results of field scale simulations show PM4SiltR can model undrained creep and progressive failure leading to delayed slope instability after relatively minor changes in loading conditions at field scale.

Bottom-to-top modeling of epoxy resins: From atomic models to mesoscale fracture mechanisms.

We outline a coarse-grained model of epoxy resins (bisphenol-F-diglycidyl-ether/3,5-diethyltoluene-2,4-diamine) to describe elastic and plastic deformation, cavitation, and fracture at the μm scale. For this, molecular scale simulation data collected from quantum and molecular mechanics studies are coarsened into an effective interaction potential featuring a single type of beads that mimic 100nm scale building blocks of the material. Our model allows bridging the time-length scale problem toward experimental tensile testing, thus effectively reproducing the deformation and fracture characteristics observed for strain rates of 10-1 to 10-5s-1. This paves the way to analyzing viscoelastic deformation, plastic behavior, and yielding characteristics by means of "post-atomistic" simulation models that retain the molecular mechanics of the underlying epoxy resin at length scales of 0.1-10 µm.

Simulation and performance analysis of quantum error correction with a rotated surface code under a realistic noise model

The demonstration of quantum error correction (QEC) is one of the most important milestones in the realization of fully-fledged quantum computers. Toward this, QEC experiments using the surface codes have recently been actively conducted. However, it has not yet been realized to protect logical quantum information beyond the physical coherence time. In this work, we performed a full simulation of QEC for the rotated surface codes with a code distance 5, which employs 49 qubits and is within reach of the current state-of-the-art quantum computers. In particular, we evaluate the logical error probability in a realistic noise model that incorporates not only stochastic Pauli errors but also coherent errors due to a systematic control error or unintended interactions. While a straightforward simulation of 49 qubits is not tractable within a reasonable computational time, we reduced the number of qubits required to 26 qubits by delaying the syndrome measurement in simulation. This and a fast quantum computer simulator, , implemented on GPU allows us to simulate full QEC with an arbitrary local noise within reasonable simulation time. Based on the numerical results, we also construct and verify an effective model to incorporate the effect of the coherent error into a stochastic noise model. This allows us to understand what the effect coherent error has on the logical error probability on a large scale without full simulation based on the detailed full simulation of a small scale. The present simulation framework and effective model, which can handle arbitrary local noise, will play a vital role in clarifying the physical parameters that future experimental QEC should target. Published by the American Physical Society 2024

Computational Exploration of Berberis lycium Royle: A Hidden Treasure Trove for Antiviral Development.

Viral infections and associated illnesses account for approximately 3.5 million global fatalities and public health problems. Medicinal plants, with their wide therapeutic range and minimal side effects, have gained limelight particularly in response to growing concerns about drug resistance and sluggish development of antiviral drugs. This study computationally assessed 11 chemical compounds from Berberis lycium along with two antiviral drugs to inhibit SARS CoV 2 (coronavirus disease 2019 [COVID-19]) RNA-dependent RNA polymerase (RdRP), influenza virus RdRP, and two crucial dengue virus (DENV) enzymes (NS2B/NS3 protease and NS5 polymerase). Berberine and oxyberberine passed all pharmacokinetics analysis filters including Lipinski rule, blood-brain barrier permeant, and cytochrome suppression and demonstrated drug-likeness, bioavailability, and a non-toxic profile. Docking of phytochemicals from B lycium returned promising results with selected viral proteins, ie, DENV NS2BNS3 (punjabine -10.9 kcal/mol), DENV NS5 (punjabine -10.4 kcal/mol), COVID-19 RdRP (oxyacanthine -9.5 kcal/mol), and influenza RdRP (punjabine -10.4 kcal/mol). The optimal pharmacokinetics of berberine exhibited good binding energies with NS2BNS3 (-8.0 kcal/mol), NS5 (-8.3 kcal/mol), COVID RdRP (-7.7 kcal/mol), and influenza RdRP (-8.3 kcal/mol), while molecular dynamics simulation of a 50-ns time scale by GROMACS software package provided insights into the flexibility and stability of the complexes. A hidden treasure trove for antiviral research, berberine, berbamine, berbamunine, oxyberberine, oxyacanthine, baluchistanamine, and sindamine has showed encouraging findings as possible lead compounds. Pharmacological analyses provide credence for the proposed study; nevertheless, as the antiviral mechanisms of action of these phytochemicals are not well understood, additional research and clinical trials are required to demonstrate both their efficacy and toxicity through in vitro and in vivo studies.

Crumbling crystals: on the dissolution mechanism of NaCl in water.

Dissolution of ionic salts in water is ubiquitous, particularly for NaCl. However, an atomistic scale understanding of the process remains elusive. Simulations lend themselves conveniently to studying dissolution since they provide the spatio-temporal resolution that can be difficult to obtain experimentally. Nevertheless, the complexity of various inter- and intra-molecular interactions require careful treatment and long time scale simulations, both of which are typically hindered by computational expense. Here, we use advances in machine learning potential methodology to resolve at an ab initio level of theory the dissolution mechanism of NaCl in water. The picture that emerges is that of a steady ion-wise unwrapping of the crystal preceding its rapid disintegration, reminiscent of crumbling. The onset of crumbling can be explained by a strong increase in the ratio of the surface area to volume of the crystal. Overall, dissolution comprises a series of highly dynamical microscopic sub-processes, resulting in an inherently stochastic mechanism. These atomistic level insights contribute to the general understanding of dissolution mechanisms in other crystals, and the methodology is primed for more complex systems of recent interest such as water/salt interfaces under flow and salt crystals under confinement.

Salome_cfd: An HPC framework for thermalhydraulics and multiphysics powered by the open-source Computational Fluid Dynamics Toolbox code_saturne

EDF R&D lab Chatou, Fluid Mechanics Energy and Environment department (MFEE), has been developping an HPC oriented Computational Fluid Dynamics toolbox since the late 1990s. This toolbox is based on the open-source Computational Fluid Dynamics (CFD) and multiphysics solver code_saturne. It has a proprietary extension, neptune_cfd, which is a Computational Multiphase Fluid Dynamics solver dealing with multi-phase flows. After a brief overview of code_saturne and neptune_cfd architecture and HPC capabilites, the present paper will focus on the effort made at EDF R&D to harness HPC clusters for thermalhydraulics at an industrial scale. This is made available by codes built from the start with massively-parallel architectures in mind, thus leading to highly scalable solvers which are able to handle tens of billions of cells. User-friendly access and interoperability are pivotal for engineers to take advantage of these HPC capabilities. Among common applications requiring HPC resources, a focus is first made on frontier scale simulations, then on parametric studies and uncertainty quantifications experiments, where a twolevel parallelism is needed : a parallel Design of Experiment evaluation drives computations themselves (massively) parallel.

Step-induced double-row pattern of interfacial water on rutile TiO2(110) under electrochemical conditions.

Metal oxides are promising (photo)electrocatalysts for sustainable energy technologies due to their good activity and abundant resources. Their applications such as photocatalytic water splitting predominantly involve aqueous interfaces under electrochemical conditions, but in situ probing oxide-water interfaces is proven to be extremely challenging. Here, we present an electrochemical scanning tunneling microscopy (EC-STM) study on the rutile TiO2(110)-water interface, and by tuning surface redox chemistry with careful potential control we are able to obtain high quality images of interfacial structures with atomic details. It is interesting to find that the interfacial water exhibits an unexpected double-row pattern that has never been observed. This finding is confirmed by performing a large scale simulation of a stepped interface model enabled by machine learning accelerated molecular dynamics (MLMD) with ab initio accuracy. Furthermore, we show that this pattern is induced by the steps present on the surface, which can propagate across the terraces through interfacial hydrogen bonds. Our work demonstrates that by combining EC-STM and MLMD we can obtain new atomic details of interfacial structures that are valuable to understand the activity of oxides under realistic conditions.

Simulation Study on Rain-Flood Regulation in Urban “Gray-Green-Blue” Spaces Based on System Dynamics: A Case Study of the Guitang River Basin in Changsha

Urban rainstorms and flood disasters are the most common and severe environmental problems worldwide. Many factors influence rain-flood control simulation, forming a complex network system of interconnected and mutually constraining elements. In terms of spatial scale selection, existing research on rain-flood disaster risk largely relies on a single-scale infrastructure index system and has not yet focused on urban “gray-green-blue” spatial scale simulations for rain-flood storage. Regarding research methodology, applying system dynamics methods to the simulation of rain-flood storage and disaster prevention planning in watershed cities is still in its initial stages. System dynamics models can simulate the feedback interactions among various sub-elements in the coupled mega-system, fully addressing complex issues within the system structure that involve multiple variables, non-linear relationships, and numerous feedback loops, thereby compensating for the inadequacies of traditional linear models in the collaborative management of rain-flood risks. Taking the Changsha Guitang River Basin as an example, this paper constructs a system dynamics model covering four dimensions: natural environment, socio-economics, internal structure, and policy development. It aims to derive the optimal planning scheme for gray-green-blue spatial coordination in rain-flood storage by weighing four different development scenarios. The simulation results show: (1) Simply changing the surface substrates without considering rainwater discharge and the plan that emphasizes the construction of municipal drainage facilities will see the capacity gap for rain-flood storage-space construction continue to widen by 2035. This indicates that the plans mentioned above will struggle to bear the socio-economic losses cities face during rain-flood disasters. (2) The plan of combining gray and green infrastructures sees the rain-flood storage construction capacity turn from negative to positive from 2024, rising to 52.259 billion yuan by 2035. This reflects that the plan can significantly reduce the rainwater volume in the later stages of low-impact development infrastructure construction, mitigate rain-flood disaster risks, and reduce government investment in rain-flood disaster risk management, making it a relatively excellent long-term rain-flood storage space planning option. (3) The rain-flood regulation space planning scheme, under the combined effect of the urban “gray-green-blue” network system, sees the capacity for rain-flood storage construction turn positive a year earlier than the previous plan, reaching 54.232 billion yuan by 2035. This indicates that the scheme can not only effectively respond to extreme flood and rainstorm disasters but also maintain ecological environment benefits and mitigate the socio-economic losses caused by disasters, making it the optimal choice for future government disaster management planning. The research results provide a theoretical framework and practical insights for territorial spatial planning, rain-flood control management, and resilient city construction in watershed areas.

Maximum likelihood estimation of a change point for Poisson distributed data

In this study we develop a change point methodology to identify and estimate changes in the parameter of a Poisson distribution. The proposed methodology considers the case when the Poisson parameter changes abruptly at an unknown point of time. For this case, the maximum likelihood estimate of the change point and its asymptotic distribution are pursued. Mainly, we carry out a large scale simulation study for evaluating the appropriateness of the asymptotic distribution of the mle from the view point of finite samples, and also for evaluating the closeness under known and unknown parameters. The simulations study also compares the mle with that of a Bayesian estimate. Then, the methodology is applied to three examples. First, we uncover changes in the number of homicides in California using monthly data from January 2002 until December 2020. Secondly, data about deaths of females caused by stomach cancer is considered to detect possible changes in the numbers recorded from 1930 to 2011. Thirdly, British coal mining disasters from 1851 to 1962 in which more than 10 men were killed are analyzed.

Electrochemistry and Electronic Structure of the Deuterium-Grown Boron-Doped Diamond Interfaces

Integration of the first principles quantum mechanical simulations with electrochemistry represents a difficult research area due to the complexity and large size of real systems and computational limitations. On the one hand it is highly desirable to be able to predict electrochemical properties of materials and systems from simulations to limit the money expenditure for experimental work and accelerate applications [1]. On the other hand, utilization of ab-initio simulations to understand the experimentally observed phenomena is also required to embed them into a solid theoretical framework.To fulfill this task, several multiscale approaches are used with the density functional theory (DFT) being fundamental tool for investigation of electronic structure. DFT results can be further extended to build forcefields used in molecular dynamics (MD) [2] for larger scale simulations and non-equillibrium Green's functions for investigations of electronics and spintronics of nanodevices [3]. Semiconductor electrochemistry is one of the most important areas to explore this experiment-theory interface because of the necessity to develop new materials for photovoltaics, photoelectrochemistry, energy storage and conversion. The purpose of the following talk is to show the power of this paradigm by elucidation of the deuterium-grown boron doped diamond (BDD-D) material [4].A brief introduction to the material synthesis, chemical and physical properties is provided exploiting the synchrotron data of X-Ray absorption spectroscopy and X-Ray photoelectron spectroscopy. Then, a strong emphasis is put into merging the (photo)electrochemical data with the first principle results for better understanding of charge transfer phenomena in the nanoscale. Specifically, DFT calculations of projected local density of states clearly show the presence of highly occupied surface states on the (111) plane of BDD-D in contrast to its standard hydrogen-grown counterpart (BDD-H). The resulting surface states are capable of photocurrent generation in the visible light, which is strongly magnified in BDD-D. Moreover, photoelectrochemical measurements evidenced that photocurrents can be positive or negative depending on the bias - despite BDD being a p-type semiconductor. These confirm a profound role of surface states in the semiconductor electrochemistry and capability of photoinduced charge transfer in the modified materials. The proposed picture shines some new light on the commonly established paradigm of band bending as the key factor driving surface properties of the nanodiamond surfaces [5]. Figure. SEM images of a) BDD@D and b) BDD@H; c) grain size distribution among two samples; d) XRD patterns of the diamond films deposited in the D2/CH4 and H2/CH4 gas mixtures. Reflections at 2θ around 44°, 75°, and 91° correspond to the (111), (220), and (311) diamond lattice planes, respectively. Doubling of the reflections is related to the presence of Kα1 and Kα2 wavelengths in the X-ray radiation [4]. Acknowledgements: M.S., M.B., and M.F. thank Helmholtz-Zentrum Berlin (HZB) for the allocation of synchrotron radiation beamtime at HZB (Germany). M.S. gratefully acknowledges the financial support of these studies fromthe Gdansk University of Technology through the DEC-02/2021/IDUB/ II.1/AMERICIUM grant under the Americium – “Excellence Initiative – Research University” program. R.B. acknowledges the funding from the National Science Centre, Poland under the OPUS call in the Weave programme (Project number: 2021/43/I/ST7/03205).

Role of and dislocations on the room-temperature grain boundary migration in a deformed Mg alloy

During deformation, dislocation movements at grain boundaries (GBs) directly affect GB plastic behaviors, thus affecting the mechanical properties of metal materials. As common defects in deformed Mg alloys, the specific role of <a> and <c+a> dislocations on room-temperature GB migration is still unclear. This work systematically investigates and answers this scientific question via experimental observations and atomic simulations. High-density serrated GBs of the AZ80 Mg alloy are achieved by multi-axial compression at room temperature. At the atomic scale, the overall slip of interfacial <a> dislocations leads to GB migration, while the GB can remain flat. Different local migration rates caused by slip and interlock of adjacent <a> and <c+a> dislocations result in GB steps, thus forming the serrated GB as the deformation proceeds. The basal-pyramidal lock at the GB prevents the continuous migration of the local GB. A new basal-pyramidal lock model at the GB is established to give a criterion for stable interlock of partial <a> and <c+a> dislocations. The experimental results and the atomic simulations show good agreement at the atomic scale and atomic simulations can explain the GB structure changes observed in the experiments. This work contributes to understanding the GB migration mechanisms of Mg alloys, which helps design deformed Mg alloys through GB engineering.

Developing an Implicit Solvation Machine Learning Model for Molecular Simulations of Ionic Media.

Molecular dynamics (MD) simulations of biophysical systems require accurate modeling of their native environment, i.e., aqueous ionic solution, as it critically impacts the structure and function of biomolecules. On the other hand, the models should be computationally efficient to enable simulations of large spatiotemporal scales. Here, we present the deep implicit solvation model for sodium chloride solutions that satisfies both requirements. Owing to the use of the neural network potential, the model can capture the many-body potential of mean force, while the implicit water treatment renders the model inexpensive. We demonstrate our approach first for pure ionic solutions with concentrations ranging from physiological to 2 M. We then extend the model to capture the effective ion interactions in the vicinity and far away from a DNA molecule. In both cases, the structural properties are in good agreement with all-atom MD, showcasing a general methodology for the efficient and accurate modeling of ionic media.

A scalable parallel computing method for autonomous platoons

This paper developed a scalable parallel computing method that can be used for platoon simulations and controller validations. A scalable adaptive platooning control law was firstly designed, which accommodates a variety of vehicle-to-vehicle communication topologies. A road vehicle dynamics model that considered the Magic Formula tyre model and suspension dynamics was then derived and validated. The parallel computing method adopted the Message Passing Interface technique to allow fast and scalable simulations. Platoon length changes do not require controller and algorithm changes. An 11-vehicle platoon on a real-world 10 km long road section was simulated. Different localisation sensor errors, communication delays, heterogenous vehicle masses and driving modes were considered. Results show that localisation errors have negligible influences on space errors. Aggressive driving and heterogeneous vehicle masses slightly increase space errors (increases less than 0.23 m). Communication delays are the greatest influencer for space errors. Increases for 15, 45 and 75 ms delays were 0.43, 1.41 and 2.41 m, respectively. It is further shown that parallel computing can improve the computing speed by three times on personal computers and seven to 12 times on workstations.

Predicting the FCI Energy of Large Systems to Chemical Accuracy from Restricted Active Space Density Matrix Renormalization Group Calculations.

We theoretically derive and validate with large scale simulations a remarkably accurate power law scaling of errors for the restricted active space density matrix renormalization group (DMRG-RAS) method [J. Phys. Chem. A 126, 9709] in electronic structure calculations. This yields a new extrapolation method, DMRG-RAS-X, which reaches chemical accuracy for strongly correlated systems such as the chromium dimer, dicarbon up to a large cc-pVQZ basis and even a large chemical complex such as the FeMoco with significantly lower computational demands than those of previous methods. The method is free of empirical parameters, performed robustly and reliably in all examples we tested, and has the potential to become a vital alternative method for electronic structure calculations in quantum chemistry and more generally for the computation of strong correlations in nuclear and condensed matter physics.

Short-range order-dependent dislocation mobilities in CrCoNi medium entropy alloy: Atomistic simulations and modeling

Understanding dislocation behavior is considered an effective way to design high performance alloys, as it is closely associated with mechanical properties. The existence of short-range order (SRO) and its ability to improve the strength and ductility of high- and medium-entropy alloys (H/MEAs) have been experimentally documented. However, few researches focused on the mechanism of dislocation motion in H/MEAs with SRO. In this study, the correlation between the degree of SRO and edge dislocation mobility in CrCoNi MEA was studied by atomistic simulations. The results show that the dislocation mobility is strongly dependent on the degree of SRO. As the degree of SRO increased, the dislocation mobility decreased linearly due to the increased energy barrier. The dislocation mobility model coupling with the SRO effects was proposed, in which the parameters were determined by molecular dynamic (MD) simulations. The energy barriers varied with the SRO degree were obtained from the model and have been compared with the theoretical predictions. A good agreement between prediction of the model and theoretical values was achieved. Using the drag coefficients obtained from this fitting, a binary linear regression model of the drag coefficient as a function of SRO degree and temperature was constructed. The proposed dislocation mobility model might be important for the construction of mechanistic predictive theory of CrCoNi MEA systems with SRO, and can also provide more accurate and reliable inputs for higher length scale simulations.

First-principles investigation on twin-related lattice reorientation in hexagonal metals and alloys

High-throughput ab initio calculations were employed to study the lattice reorientation process accompanying twinning under plastic deformation in hexagonal metals, as well as in alloyed Mg and Ti. The results indicate that, in unalloyed hexagonal metals, reorientation consumes the highest and lowest energies in Be and Mg, respectively, with intermediate energies in Sc, Co, Ti, Zr and Dy. The shuffle component always contributes a significant part of the reorientation energy in Mg, whereas in Ti with sufficient shear strain, subsequent reorientation process becomes energy downhill. In alloyed Mg and Ti, appropriate alloying significantly reduces the reorientation energy, and especially in alloyed Ti, there is significant negative correlation between the reorientation energy and certain properties of the alloying elements. The results are consistent with the fact that reorientation has been experimentally observed in compressed Mg nanopillars, but only in atomic scale simulations under high strain rate in Ti.

An accelerated scheme for solving inhomogeneous elasticity in phase-field method and application to Ni-based multi-component alloys

On the way towards realistic predictions of various material properties for engineering structural materials, efficient computational simulations of microstructural evolution play an indispensable role. In the present work, an in-house phase-field code with inhomogeneous elasticity has been developed aiming to deal with precipitation processes for multi-component alloys with high efficiency. Two different numerical iterative algorithms, named as Model A and B, are implemented to solve inhomogeneous elastic equations. The resulting simulated morphology and energy evolutions under different conditions show consistency for these two algorithms. In order to perform simulation efficiently, a parallel technique based on CUDA (Computer Unified Device Architecture) architecture is used, which greatly improves the computational efficiency. After estimating the computational efficiency of different acceleration techniques for different grid sizes, Model A is extended to simulate concentration and morphology evolutions in the precipitation and rafting processes of γ′ phase in a Ni-based five-component alloy of Ni−Co−Al−Mo−W. Meanwhile, the CALPHAD method and related databases are linked to provide thermodynamic and diffusivity data. Finally, the computation costs of the parallel technique for different simulation scales are explored, testifying that this technique is capable of performing larger scale simulations for multi-component multi-phase structural materials.

Modeling of methane and carbon dioxide sorption capacity in tight reservoirs using Machine learning techniques

The viability of enhanced coalbed methane recovery (ECBM) and enhanced shale gas recovery (ESGR) are abundantly explored in various studies since they present a solution for ongoing energy demand and environmental crisis. As a matter of challenge, the prediction of the gas sorption profile poses a significant obstacle in the development of such resources. In this regard, mathematical models, numerical simulations, empirical correlations, and experimental measurements suffer from over-simplification, computationally extensive calculations, time-consuming, and costly processes. This work examines different machine learning methods, from shallow to deep learning, to investigate their capability to model 3804 data points of methane (CH4) and/or carbon dioxide (CO2) sorption capacity in shale and coal at different thermodynamic conditions. Percentage of CO2 in injection gas, rock type, total organic carbon (TOC), moisture, temperature, and pressure were taken as input. Hyperparameter tuning was executed by Optuna optimizer. According to the results, the random forest (RF) was the most proficient predictor for both test subsets of CH4 (MAE = 0.0864, MSE = 0.0231, RMSE = 0.1520) and CO2 (MAE = 0.0529, MSE = 0.0533, RMSE = 0.2308) sorption capacity. Based on the trend prediction simulation, RF predictors were able to properly capture the single and binary sorption capacities. More importantly, they successfully predict the abnormal descending behavior of CO2 sorption in high pressures. In addition, a sensitivity analysis was carried out to acquire the importance of input features and model hyperparameters in the training process. Input feature impacts were consistent with technical expectations of geology and reservoir engineering aspects. Both models are capable of being applied to lab scale and further developed for their application in reservoir scale simulators.

Multi scale simulation of crack propagation in polycrystalline SiC

Silicon carbide (SiC) is considered one of the most promising emerging materials in the fields of refractory materials and semiconductors. In this paper, a multi-scale numerical simulation method is proposed to investigate crack propagation in SiC at different scales, using a combination of cohesive zone model and molecular dynamics (MD). The microstructure of the material is represented by Voronoi subdivision, and bilinear treatment is applied at the grain boundaries. Firstly, MD models with different microscopic defects were established to obtain the corresponding traction separation curves. Then, the element parameters, obtained by fitting the traction separation law, were inserted into the cohesive elements located at the grain boundary and within the grain. Finally, the finite element models were simulated by the failure criterion. The fracture toughness of the material is predicted, and the influence of microscopic defects and grain distribution on macroscopic fracture toughness is explored. The results indicate that the grain distribution and the type of micro defect have an impact on the crack growth path and macro fracture toughness. The simulation results are consistent with published experimental results, which demonstrates the viability of employing this multi-scale analysis method for examining and replicating the micro-, meso-, and macro-scale crack propagation characteristics of SiC.

Molecular Dynamics Simulations of Displacement Cascades in BCC-Fe: Effects of Dislocation, Dislocation Loop and Grain Boundary

The interactions between displacement cascades and three types of structures, dislocations, dislocation loops and grain boundaries, in BCC-Fe are investigated through molecular dynamics simulations. Wigner–Seitz analysis is used to calculate the number of point defects induced in order to illustrate the effects of three special structures on the displacement cascade. The displacement cascades in systems interacting with all three types of structure tend to generate more total defects compared to bulk Fe. The surviving number of point defects in the grain boundary case is the largest of the three types of structures. The changes in the atomic structures of dislocations, dislocation loops and grain boundaries after displacement cascades are analyzed to understand how irradiation damage affects them. These results could reveal irradiation damage at the microscale. Varied defect production numbers and efficiencies are investigated, which could be used as the input parameters for higher scale simulation.