Large-scale Simulations Research Articles

Psychological safety during a large-scale simulation-based learning event

Psychological safety (PS) is currently endorsed as best practice in simulated learning environments. PS refers to the belief that a student can express their ideas and concerns without fear of negative consequences. Students who participate in simulation-based scenarios often experience psychological distress during such events if they are immersed in an environment that is not psychologically safe. This phenomenon has been researched in the field of nursing during both the briefing and debriefing phases of simulation. The aim of this study was to determine the level of PS of emergency care students at a large-scale simulation rescue training exercise. A quantitative survey design was used on student participants at an annual rescue training exercise in 2021. Students who participated in a high-fidelity rescue event scored a mean PS score of 48.76. Reluctance to take interpersonal risks and fear of making mistakes in a team setting were identified as areas of concern. When PS is understood and improved, the learning experience for students will be enhanced and they will be able to participate without fear of humiliation.

Approaches for fast Brownian dynamics simulation with constraints

Constraints can be used to eliminate quickly fluctuating degrees of freedom in dynamical systems enabling solutions that resolve the behavior of the system with fewer time steps over long time scales. In Brownian dynamics models, these constraints lead to linear equations which represent saddle point problems describing the velocity tangent to the constraint manifold and the forces resulting from imposition of the constraint on the dynamics. Simulations of these constrained systems must solve these saddle point problems, which are often ill-conditioned and require clever preconditioning in order to facilitate large scale simulation using iterative methods like GMRES. In this work, we show that the projected conjugate gradient (PrCG) method, which applies conjugate gradients projected onto the tangent space of the constraint manifold, has certain advantages compared to preconditioned iterative methods like GMRES, without the need to devise and compute a preconditioner. Specifically, for simulations of beads interacting hydrodynamically with commonly encountered holonomic constraints, PrCG exhibits linear scaling with the number of beads similar to GMRES. However, because PrCG does not require a preconditioner it often requires less precomputational effort both in terms of computational complexity and storage, depending on the nature of the constraints. Furthermore, PrCG produces iterates which are always feasible, allowing for independent tuning of the error tolerance on the bead velocities from the error tolerance on the constraint equations. We demonstrate PrCG's efficiency by solving several example low Reynolds number systems involving rigid bodies, freely jointed chains, and immobile particles, as well as a mixed constraint problem with both a rigid body and immobile particles, comparing to GMRES and analytical solutions, when available.

A Partial Maximum Likelihood Method to Estimate Cox Model for Competing Risk Data with Application

Many Research and clinical studies have addressed the occurrence of failure (death). As a result of other (external) factors, which may introduce additional risks that compete with the event under study. The resulting data refers to the complete data on Competing risks, which are affected by time differences. The exact times of occurrence of these risks are known, meaning the times of failure are observed for all observations with certainty. The impact of these risks on the hazard function is estimated based on the Cox proportional hazards model, which is estimated using the partial maximum likelihood method and numerical algorithms for parameter estimation. This includes the effect of variables on the Cox hazard function and the nonparametric part, estimated by assessing the effect of time on the hazard function using the Kaplan-Meier formula and calculating competing risks through the cumulative hazard function. These methods were applied to experimental data through large-scale simulations of different sizes and parameters and for several arithmetic means and standard deviations models. Moreover, applied to real data from a sample of 80 individuals with breast cancer. Analyzing the simulation results and real data revealed that the Downhill algorithm outperforms the Newton-Raphson algorithm in terms of estimation accuracy and efficiency, based on the statistical criterion for comparison, the root mean square error. In addition, competitive risks explained the effect of common variables in increasing competitive risks beyond the hazard function of the Cox model of the Newton-Raphson estimator. While it is converging to the hazard function of the Cox model for the Downhill estimator . Paper type: Research paper

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

Capture efficiencies of point defects by 1/2 〈111〉 edge dislocations in tungsten using atomistic simulations

We systematically study the interaction behaviors between a point defect (PD) and two types of 1/2 〈111〉 {110} and 1/2 〈111〉 {112} edge dislocations using molecular dynamics (MD), elastic dipole tensor, and kinetic Monte Carlo (KMC) methods in tungsten (W). We first determine the capture region of a vacancy and four types of self-interstitial atoms (SIAs) by calculating their binding energies to both edge dislocations using MD. We then find anisotropic distributions of the diffusion energy barriers of PDs in dislocation systems employing the MD and elastic dipole tensor methods. Subsequently, we utilize the KMC method to obtain the lifetime of PDs and calculate the capture efficiencies of both edge dislocations to PDs at various temperatures and dislocation densities. Results show the capture efficiency of a vacancy decreases with increasing temperature, while it first increases and then decreases with increasing temperature for SIAs. As dislocation density increases, the capture efficiency of PDs increases. Ultimately, the swelling rate is estimated based on the capture efficiency and falls within the range of experimental data. The swelling rate increases first and then decreases with increasing temperature, and the maximum swelling rate occurs around 1200 K, while it increases with increasing the dislocation density. The present results are critical for understanding the interaction behaviors between PDs and 1/2 〈111〉 edge dislocations in W, which provide an essential database for large-scale simulation methods such as rate theory and phase-field simulations.

A Data–Physics-Driven Modeling Approach of Key Equipment for Large-Scale Distribution Network Simulation

Fueled by pressing global climate concerns, the integration of large-scale renewable distributed generation sources, including distributed wind power and photovoltaics, along with electricity substitution loads into the distribution network has been accelerated to diminish carbon emissions. This shift introduces significant challenges and necessitates the advanced operation and control of distribution systems to accommodate these changes effectively. Against this backdrop, there is a growing expectation for an open and scalable central control mode, equipped with compatible interfaces, to offer a visionary development platform for the grid. This platform is anticipated to meet the evolving needs of future distribution system development, ensuring adaptability and forward compatibility. The aforementioned platform requires open, scalable, and interface-compatible models of key distribution network equipment as its foundation. To address the challenges presented, this paper proposes a data–physics-driven modeling approach for automating simulations in distribution systems. This method employs a simplified and standardized system of linear differential equations with undetermined coefficients to capture the common physical characteristics of specific device types. The models designed through this approach are notably open, allowing for real-time data to refine undetermined coefficients and accurately depict the dynamic behavior of equipment over various periods. Their scalability also stands out, rendering them apt for large-scale distribution network simulations. The paper elaborates on models for distributed photovoltaic, wind turbine, energy storage, and electric vehicle, and demonstrates their application within an IEEE-33 node distribution network topology built on Python.

Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates

Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed-form expressions for their likelihood function, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference, likelihood-free inference or approximate Bayesian computation. However, given the high computational requirements of large-scale models, it is often difficult to run these estimation methods, as they require more simulated runs that can feasibly be carried out. The aim is to address the problem by providing a full Bayesian estimation framework where the true but intractable likelihood function of the simulation model is replaced by one generated by a surrogate model trained on the limited simulated data. This is provided by a Linear Model of Coregionalization, where each latent variable is a sparse variational Gaussian process, chosen for its desirable convergence and consistency properties. The effectiveness of the approach is tested using both a simulated Bayesian computing analysis on a known data generating process, and an empirical application in which the free parameters of a computationally demanding agent-based model are estimated on US macroeconomic data.

A parallel framework on hybrid architectures for raster-based geospatial cellular automata models

Geospatial cellular automata (Geo-CA) models have encountered challenges in computing efficiency and scalability when conducting large-scale land use change simulation applications. Parallel computing has proven to be effective to address these challenges. However, parallelization strategies for existing parallel Geo-CA models are always optimized for specific models and lack applicability to others. Besides, most parallel Geo-CA models focus on parallelizing land use change simulations, ignoring land use transition rule mining. Moreover, there is a lack of effective parallel strategies for demand-constrained land use change simulation on distributed heterogeneous architectures. This study proposes a parallel framework on hybrid parallel computing architectures applicable for raster-based Geo-CA models to enhance their computing efficiency and scalability while maintaining simulation accuracy. The framework provides parallelization strategies for both the land use transition rule mining for multiple land use types and the demand-constrained land use change simulation on distributed heterogeneous architectures. The framework was employed to parallelize two contemporary Geo-CA models, i.e., PLUS and MCCA. Experiments showed that the parallelized models achieved significant improvements in computing efficiency and scalability, confirming the effectiveness of the proposed framework for large-scale land use change simulation studies.

Mastering the art: Proficient finite element implementation and robust evaluation of a strain-hardening porous ductile material crack growth prediction model at finite strain

Accurate prediction of crack extension is crucial for ensuring the structural integrity of metal components exposed to diverse loads. While the Gurson model and its extensions are widely accepted for delineating ductile fracture stages, especially in porous materials with a rigid-perfectly plastic matrix, their efficacy diminishes in the presence of strain-hardening matrices. To address this limitation, our study introduces a robust numerical implementation and assessment of Perrin's model. In contrast to Gurson's approach, Perrin's model incorporates two distinct strain-hardening parameters derived from an intricate analysis of a strain-hardening hollow sphere subjected to axisymmetric loading.Moving beyond theoretical discussions, this paper actively engages in the rigorous evaluation of Perrin's model under various scenarios, encompassing isotropic, kinematic, and mixed isotropic-kinematic hardening conditions. The model's effectiveness is convincingly demonstrated through meticulous comparisons between numerical simulations and real-world experimental observations conducted on pre-cracked specimens.Facing challenges related to achieving global elastic-plastic convergence in large-scale simulations, our research introduces a sophisticated approach. Leveraging stiffness tangent moduli ensure the maintenance of quadratic convergence in global Newton method iterations. This not only enhances the reliability of our simulations but also underscores the practical applicability of our findings.The study also focuses on the necking and crack propagation of a cylindrical specimen through meticulous computational modeling. Our mesh captures intricate details and addresses stress gradients, revealing coplanar crack propagation contrary to prior beliefs. The confirmation of the cup-cone fracture effect challenges previous interpretations, emphasizing the critical role of void nucleation. This reinforces the credibility and applicability of our approach in advancing the understanding of material fracture behavior.In summary, this study significantly contributes to existing knowledge in fracture prediction models, offering a robust framework for predicting ductile fracture under substantial deformations. The presented findings pave the way for advancements in structural engineering, providing invaluable insights for optimizing the performance and resilience of metal structures in real-world applications.

Large-scale atomistic simulation of diffusion in refractory metals and alloys

Large-scale atomistic simulation of diffusion in refractory metals and alloys

Molecular Dynamics Simulations of Different Nanoparticles at Substrates.

We report the results of large-scale molecular dynamics simulations of adsorption nanoparticles on solid surfaces. The particles were modeled as stiff aggregates of spherical segments. Three types of particles were studied: rods, rectangles, and triangles built of the same number of segments. We show how the particle shape affects the adsorption, the structure of the surface layer, and the degree of the removal of particles from the solvent. The systems with different segment-segment and segment-surface interactions and different concentrations of particles were investigated. The ordered structures formed in adsorption monolayers were also analyzed. The results are consistent with experimental observations.

GENESIS CGDYN: large-scale coarse-grained MD simulation with dynamic load balancing for heterogeneous biomolecular systems.

Residue-level coarse-grained (CG) molecular dynamics (MD) simulation is widely used to investigate slow biological processes that involve multiple proteins, nucleic acids, and their complexes. Biomolecules in a large simulation system are distributed non-uniformly, limiting computational efficiency with conventional methods. Here, we develop a hierarchical domain decomposition scheme with dynamic load balancing for heterogeneous biomolecular systems to keep computational efficiency even after drastic changes in particle distribution. These schemes are applied to the dynamics of intrinsically disordered protein (IDP) droplets. During the fusion of two droplets, we find that the changes in droplet shape correlate with the mixing of IDP chains. Additionally, we simulate large systems with multiple IDP droplets, achieving simulation sizes comparable to those observed in microscopy. In our MD simulations, we directly observe Ostwald ripening, a phenomenon where small droplets dissolve and their molecules redeposit into larger droplets. These methods have been implemented in CGDYN of the GENESIS software, offering a tool for investigating mesoscopic biological processes using the residue-level CG models.

Turbulent spot formation in three-dimensional yukawa liquids using large-scale molecular dynamics simulation—effect of system size

We have performed classical ‘first principles’ 3D Molecular Dynamics (MD) simulation of plane-Couette flow (PCF) in a 3D Yukawa liquid. The main aim of the present investigation is to study the effect of stream-wise (flow-direction) and span-wise (transverse direction to the flow) width on the subcritical transition to turbulence in plane Couette flow (PCF), separately. In the past, taking very large-aspect ratio systems, subcritical transition to turbulence in PCF has been studied in great detail. However, the effect of stream-wise and span-wise width on the turbulent dynamics separately, have not been studied in detail. In the present work, we have investigated the effect of stream-wise and span-wise width or length and found that the stream-wise length enhances the large-scale energy, which gives rise to strong large-scale flow, and span-wise width enhances the small-scale energy, which gives rise to strong small-scale structures. In other words, topology of the turbulent spot is observed to change with the change in system size. The spectral separation between the modes governing the large and small-scale dynamics improves with the increase in system sizes. The connection between the stream-wise vortices and the stream-wise velocity streaks is investigated in details. We have found that the number stream-wise velocity streaks is one unit larger than the number of stream-wise vortices. A qualitative comparison between the results obtained from the MD simulation and the hydrodynamics experiment is presented in this article. The behavior of the system was observed to vary with the range of interactions among the dust grains.

Correlated evolution between body size and echolocation in bats (order Chiroptera).

Body size and echolocation call frequencies are related in bats. However, it is unclear if this allometry applies to the entire clade. Differences have been suggested between nasal and oral emitting bats, as well as between some taxonomic families. Additionally, the scaling of other echolocation parameters, such as bandwidth and call duration, needs further testing. Moreover, it would be also interesting to test whether changes in body size have been coupled with changes in these echolocation parameters throughout bat evolution. Here, we test the scaling of peak frequency, bandwidth, and call duration with body mass using phylogenetically informed analyses for 314 bat species. We specifically tested whether all these scaling patterns differ between nasal and oral emitting bats. Then, we applied recently developed Bayesian statistical techniques based on large-scale simulations to test for the existence of correlated evolution between body mass and echolocation. Our results showed that echolocation peak frequencies, bandwidth, and duration follow significant allometric patterns in both nasal and oral emitting bats. Changes in these traits seem to have been coupled across the laryngeal echolocation bats diversification. Scaling and correlated evolution analyses revealed that body mass is more related to peak frequency and call duration than to bandwidth. We exposed two non-exclusive kinds of mechanisms to explain the link between size and each of the echolocation parameters. The incorporation of Bayesian statistics based on large-scale simulations could be helpful for answering macroevolutionary patterns related to the coevolution of traits in bats and other taxonomic groups.

Cooperative competition between melt-phase and void during micro-spallation and recompression

Dynamic tensile (micro-spallation) fracture and subsequent recompression in a molten state is the key physical process for metallic materials undergoing multiple intense dynamic loadings. However, there still lacks in-depth analysis of the complete evolution process from damage accumulation to recompression, as well as the influence of damage history on subsequent deformation and fracture. In the present work, the mechanism of cooperative competition evolution between the melt-phase and voids has been proposed as a new perspective to investigate damage evolution. Various important physical phenomena discovered in larger-scale MD simulations of molten aluminum can be self-consistently explained according to the proposed mechanism, such as the rate-dependence of spall strength and the pressure oscillation. The recompression-after-damage and second fracture process are discussed in detail to analyze the damage history effect. Under the present framework, two theoretical models have been proposed from the perspective of void and melt-phase evolution, respectively. The proposed models agree well with the simulation results and provide a comprehensive understanding of the damage evolutionary characteristics.

A NEW Technique for Disseminating Knowledge Concerning VANET Traffic Using Crocodile Hunting Search

VANETs or Vehicular Ad hoc Networks represent the most rapidly emerging application of Ad hoc Networks. It shares traffic condition information and is one of the attempts to improve road safety and traffic management. This paper develops a new technique in the name of "Crocodile Hunting Search" (CHS) to assist information dissemination in Vehicular hoc networks (VANETs). It is named after hunting a crocodile, methodically moving to catch prey, which CHS bases many of its principles upon. The CHS solution that we use sends important traffic data in a decentralized, flexible way within the network. From the point of view of VANET, we consider each vehicle as a mobile node that carries the packets with the information contained on the traffic conditions. These packets are solicited and forwarded to the various vehicles in proportion to closeness and usefulness. The point here is that the search perimeter would have to be adapted to the variation in the quantity of vehicles and their actual traffic. If times are off-peak and there is less traffic, the cars would therefore widen their searching area, which could not be the case for other solutions during that time of less traffic. On the other side, in localities with narrow narrowing information-seeking to find only what is essential and related becomes increasingly important. We show through large-scale simulations and real-world experimentation that our method can work in sending information over Vehicular Ad Hoc Networks (VANETs). This perspective, the current research, clearly demonstrates that knowledge propagation is way more effective in spreading information than traditional methods. It causes low packet loss, and the communication overhead is also low. The Crocodile Hunting Search is another practicable method of solving the problems that arise while trying to send information effectively in VANETs.

Disorder Operator and Rényi Entanglement Entropy of Symmetric Mass Generation.

The "symmetric mass generation" (SMG) quantum phase transition discovered in recent years has attracted great interest from both condensed matter and high energy theory communities. Here, interacting Dirac fermions acquire a gap without condensing any fermion bilinear mass term or any concomitant spontaneous symmetry breaking. It is hence beyond the conventional Gross-Neveu-Yukawa-Higgs paradigm. One important question we address in this Letter is whether the SMG transition corresponds to a true unitary conformal field theory. We employ the sharp diagnosis including the scaling of disorder operator and Rényi entanglement entropy in large-scale lattice model quantum MonteCarlo simulations. Our results strongly suggest that the SMG transition is indeed an unconventional quantum phase transition and it should correspond to a true (2+1)d unitary conformal field theory.

Study on the Transport and Transformation Law of CO<sub>2</sub> Marine Storage in Reservoirs with Various Permeability Anisotropy

There is an international consensus to reduce the pace of global warming caused by greenhouse gases, such as CO<sub>2</sub>.The geological storage of CO<sub>2</sub> plays a crucial role in reducing the atmospheric CO<sub>2</sub> concentration, and hydrate-based CO<sub>2</sub> storage is an important geological storage technology applied to seabed sediments, which has attracted increasing attention due to its advantages of high safety and large storage capacity. In this study, a large-scale numerical simulator applicable to CO<sub>2</sub> hydrate storage is developed, which considers the two-phase flow process including hydrate formation and realizes the coupling of the thermal-fluidic-chemical three fields, and further investigates the transport and transformation behavior of CO<sub>2</sub> in the reservoir with anisotropic permeability during the injection period and at the cessation of injection. The results show that the formation of CO<sub>2</sub> hydrate can easily generate local high-pressure zones and local high-temperature zones inside the reservoir; the CO<sub>2</sub> hydrate cap formed above the wellhead can limit the vertical transport distance of CO<sub>2</sub> and ensure the safe storage of CO<sub>2</sub>. In addition, this study also analyzed the efficiency of CO<sub>2</sub> hydrate storage in different permeability anisotropic reservoirs and found that high <I>K</I><sub>hv</sub> reservoirs are more conducive to CO<sub>2</sub> conversion in the long term. This study can provide scientific value for the hydrate-based CO<sub>2</sub> storage in the ocean and a theoretical basis for the transport behavior of CO<sub>2</sub> in submarine reservoirs.

Effects of shear strain on shock response in single crystal iron

With large-scale non-equilibrium molecular dynamics simulations and in situ x-ray diffraction analysis, we conducted a systematic investigation into the effects of pre-existing shear strain (γxy) on the shock response of single crystal iron. Our findings reveal significant effects of γxy on the deformation of the crystal structure during shock loading, leading to noticeable alterations in the propagation of shock waves. Specifically, during the elastic stage, the presence of γxy results in a reduction of shock strength, consequently diminishing the magnitude of elastic lattice strain (εe). In the plastic stage, γxy stimulates the α–ε phase transformation, and structure deformation undergoes a transition from the sequential activity of dislocation-to-transformation to the synchronous activity of dislocation and transformation. This transition inhibits the propagation of plastic waves and consequently broadens the elastic regime. Additionally, the introduction of γxy activates different slip systems, as it alters the corresponding resolved shear stress. Concurrently, the presence of γxy triggers the activation of different high-pressure phase variants. Our investigation sheds light on the fundamental physics of iron under shock compression and the influence of pre-existing shear strain on its behavior.

The Influence of Online Reviews on the Purchasing Decisions of Travel Consumers

In this study, we investigate the impact of online review characteristics on consumers’ purchasing decisions in the context of spatial distance. We consider the product experience of online travel routes, geographical location characteristics, and price adjustment factors, as well as the dynamics between consumers and businesses during the booking of travel routes. Through empirical research and large-scale data simulation experiments, we have found that the variability in attributes of tourist routes significantly influences the user recommendation rate, while the overall rating has a positive moderating effect. Furthermore, the number of reviews negatively moderates the relationship between them. Additionally, the product information and service quality of tourist routes also significantly affect the recommendation rate. Finally, we propose a management strategy for tourism route managers to enhance user recommendation rates and achieve greater benefits.