Machine learning-enhanced Monte Carlo and subset simulations for advanced risk assessment in transportation infrastructure

The maintenance of safety and dependability in rail and road embankments is of utmost importance in order to facilitate the smooth operation of transportation networks.This study introduces a comprehensive methodology for soil slope stability evaluation,employing Monte Carlo Simulation(MCS)and Subset Simulation(SS)with the"UPSS 3.0 Add-in"in MS-Excel.Focused on an 11.693-meter embankment with a soil slope(inclination ratio of 2H:1V),the investigation considers earthquake coefficients(kh)and pore water pressure ratios(ru)following Indian zoning requirements.The chance of slope failure showed a considerable increase as the Coefficient of Variation(COV),seismic coefficients(kh),and pore water pressure ratios(ru)experienced an escalation.The SS approach showed exceptional efficacy in calculating odds of failure that are notably low.Within computational modeling,the study optimized the worst-case scenario using ANFIS-GA,ANFIS-GWO,ANFIS-PSO,and ANFIS-BBO models.The ANFIS-PSO model exhibits exceptional accuracy(training R2=0.9011,RMSE=0.0549;testing R2=0.8968,RMSE=0.0615),emerging as the most promising.This study highlights the significance of conducting thorough risk assessments and offers practical insights into evaluating and improving the stability of soil slopes in transportation infrastructure.These findings contribute to the enhancement of safety and reliability in real-world situations.

Monte Carlo Simulations of Doping Properties of a Spin-3/2 Ising Nanotube

The effect of spin-1 impurities doping on the magnetic properties of a spin-3/2 Ising nanotube is investigated using Monte Carlo simulations within the Blume-Emery-Griffiths model in the presence of an external magnetic field. The thermal behaviors of the order parameters and different macroscopic instabilities as well as the hysteretic behavior of the material are examined in great detail as a function of the dopant density. It is found that the impurities concentration affects all the system magnetic properties generating for some specific values, compensation points and multi-cycle hysteresis. Doping conditions where the saturation/remanent magnetization and coercive field of the investigated material can be modified for permanent or soft magnets synthesis purpose are discussed.

Exhaustive review of acceleration strategies for Monte Carlo simulations in photon transit

Monte Carlo simulation techniques have become the quintessence and a pivotal nexus of inquiry in the realm of simulating photon movement within biological fabrics.Through the stochastic sampling of tissue archetypes delineated by explicit optical characteristics,Monte Carlo simulations possess the theoretical capacity to render unparalleled accuracy in the depiction of exceedingly intricate phenomena.Nonetheless,the quintessential challenge associated with Monte Carlo simulation methodologies resides in their extended computational duration,which significantly impedes the refinement of their precision.Consequently,this discourse is specifically dedicated to exploring innovations in strategies and technologies aimed at expediting Monte Carlo simulations.It delves into the foundational concepts of various acceleration tactics,evaluates these strategies concerning their speed,accuracy,and practicality,and amalgamates a comprehensive overview and critique of acceleration methodologies for Monte Carlo simulations.Ultimately,the discourse envisages prospective trajectories for the employment of Monte Carlo techniques within the domain of tissue optics.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

Optimization of the Use of Spherical Targets for Point Cloud Registration Using Monte Carlo Simulation

Registrations based on the manual placement of spherical targets are still being employed by many professionals in the industry.However,the placement of those targets usually relies solely on personal experience without scientific evidence supported by numerical analysis.This paper presents a comprehensive investigation,based on Monte Carlo simulation,into determining the optimal number and positions for efficient target placement in typical scenes consisting of a pair of facades.It demonstrates new check-up statistical rules and geometrical constraints that can effectively extract and analyze massive simulations of unregistered point clouds and their corresponding registrations.More than 6×10^(7) sets of the registrations were simulated,whereas more than IOO registrations with real data were used to verify the results of simulation.The results indicated that using five spherical targets is the best choice for the registration of a large typical registration site consisting of two vertical facades and a ground,when there is only a box set of spherical targets available.As a result,the users can avoid placing extra targets to achieve insignificant improvements in registration accuracy.The results also suggest that the higher registration accuracy can be obtained when the ratio between the facade-to-target distance and target-to-scanner distance is approximately 3:2.Therefore,the targets should be placed closer to the scanner rather than in the middle between the facades and the scanner,contradicting to the traditional thought. Besides,the results reveal that the accuracy can be increased by setting the largest projected triangular area of the targets to be large.

基于Monte-Carlo仿真的恒应力加速试验方案模拟评价

为了保证可靠性加速试验方案的正确性和可行性,提出基于仿真的模拟评价方法。以最具代表性的Weibull寿命分布产品的恒应力加速寿命试验为例,以代表试验结果准确性和稳定性的指标——中位寿命估计值渐进方差的均值和标准差作为评价指标,通过Monte-Carlo仿真模拟生成失效数据,对不同试验方案的统计精度和稳定性进行评价,为加速寿命试验优化设计的工程实现提供了思路和实例验证。

Sample size adaptive strategy for time-dependent Monte Carlo particle transport simulation

When multiphysics coupling calculations contain time-dependent Monte Carlo particle transport simulations, these simulations often account for the largest part of the calculation time, which is insufferable in certain important cases. This study proposes an adaptive strategy for automatically adjusting the sample size to fulfil more reasonable simulations. This is realized based on an extension of the Shannon entropy concept and is essentially different from the popular methods in timeindependent Monte Carlo particle transport simulations, such as controlling the sample size according to the relative error of a target tally or by experience. The results of the two models show that this strategy can yield almost similar results while significantly reducing the calculation time. Considering the efficiency, the sample size should not be increased blindly if the efficiency cannot be enhanced further. The strategy proposed herein satisfies this requirement.

EJUSTCO:Monte Carlo radiation transport code hybrid with ANN model for gamma-ray shielding simulation

Gamma ray shielding is essential to ensure the safety of personnel and equipment in facilities and environments where radiation exists.The Monte Carlo technique is vital for analyzing the gamma-ray shielding capabilities of materials.In this study,a simple Monte Carlo code,EJUSTCO,is developed to cd simulate gamma radiation transport in shielding materials for academic purposes.The code considers the photoelectric effect,Compton(incoherent)scattering,pair production,and photon annihilation as the dominant interaction mechanisms in the gamma radiation shielding problem.Variance reduction techniques,such as the Russian roulette,survival weighting,and exponential transformation,are incorporated into the code to improve computational efficiency.Predicting the exponential transformation parameter typically requires trial and error as well as expertise.Herein,a deep learning neural network is proposed as a viable method for predicting this parameter for the first time.The model achieves an MSE of 0.00076752 and an R-value of 0.99998.The exposure buildup factors and radiation dose rates due to the passage of gamma radiation with different source energies and varying thicknesses of lead,water,iron,concrete,and aluminum in single-,double-,and triple-layer material systems are validated by comparing the results with those of MCNP,ESG,ANS-6.4.3,MCBLD,MONTEREY MARK(M),PENELOPE,and experiments.Average errors of 5.6%,2.75%,and 10%are achieved for the exposure buildup factor in single-,double-,and triple-layer materials,respectively.A significant parameter that is not considered in similar studies is the gamma ray albedo.In the EJUSTCO code,the total number and energy albedos have been computed.The results are compared with those of MCNP,FOTELP,and PENELOPE.In general,the EJUSTCO-developed code can be employed to assess the performance of radiation shielding materials because the validation results are consistent with theoretical,experimental,and literary results.

Grand canonical Monte Carlo simulation study of hydrogen storage by Li-decorated pha-graphene

Grand canonical Monte Carlo simulation(GCMCs)is utilized for studying hydrogen storage gravimetric density by pha-graphene at different metal densities,temperatures and pressures.It is demonstrated that the optimum adsorbent location for Li atoms is the center of the seven-membered ring of pha-graphene.The binding energy of Li-decorated phagraphene is larger than the cohesive energy of Li atoms,implying that Li can be distributed on the surface of pha-graphene without forming metal clusters.We fitted the force field parameters of Li and C atoms at different positions and performed GCMCs to study the absorption capacity of H_(2).The capacity of hydrogen storage was studied by the differing density of Li decoration.The maximum hydrogen storage capacity of 4Li-decorated pha-graphene was 15.88 wt%at 77 K and100 bar.The enthalpy values of adsorption at the three densities are in the ideal range of 15 kJ·mol^(-1)-25 kJ·mol^(-1).The GCMC results at different pressures and temperatures show that with the increase in Li decorative density,the hydrogen storage gravimetric ratio of pha-graphene decreases but can reach the 2025 US Department of Energy's standard(5.5 wt%).Therefore,pha-graphene is considered to be a potential hydrogen storage material.

Comparison Between Three-Valley Model and Full Band Model in Monte Carlo Simulation of Bulk Wurtzite GaN

The Monte Carlo simulators with the three valley model and the full band Monte Carlo model are used to explore electron transport in bulk wurtzite gallium nitride (GaN).Comparison of the results based on the two models is made.The results based on both models are basically the same at the lower field region,but exhibit some differences at the higher field region.The electron average energy exhibits obvious difference at the high field region between the two models.This difference further causes several other differences of GaN properties,such as the drift velocity versus field characteristics,the repopulation.Because of the complicated energy band structures at the high energy region for wurtzite GaN,the analytical band structures in the three valley model can not cover all properties of the band structures of wurtzite GaN,so the results based on the full band Monte Carlo model should be more exact.

Simulation study of the performance of the Very Large Area gamma-ray Space Telescope

The Very Large Area gamma-ray Space Telescope(VLAST)is a mission concept proposed to detect gamma-ray photons through both Compton scattering and electron–positron pair production mechanisms,thus enabling the detection of photons with energies ranging from MeV to TeV.This project aims to conduct a comprehensive survey of the gamma-ray sky from a low-Earth orbit using an anti-coincidence detector,a tracker detector that also serves as a low-energy calorimeter,and a high-energy imaging calorimeter.We developed a Monte Carlo simulation application of the detector using the GEANT4 toolkit to evaluate the instrument performance,including the effective area,angular resolution,and energy resolution,and explored specific optimizations of the detector configuration.Our simulation-based analysis indicates that the current design of the VLAST is physically feasible,with an acceptance above 10 m^(2)sr which is four times larger than that of the Fermi-LAT,an energy resolution better than 2%at 10 GeV,and an angular resolution better than 0.2◦at 10 GeV.The VLAST project promises to make significant contributions to the field of gamma-ray astronomy and enhance our understanding of the cosmos.

Fe-Cr二元合金微观组织演化的质量密度场耦合动力学Monte-Carlo模拟研究

本文建立了一种全新的将动力学Monte-Carlo粒子模拟与基于归一化Gauss函数基组的质量密度场空间粗粒化模型耦合的杂化模拟算法.采用该杂化模拟算法,系统对比研究了4种Cr原子含量分别为12.8%,20.0%,30.0%和40.0%的Fe-Cr合金中Cr相在温度为673 K下的时效析出动力学机制,及其时效不同阶段微观组织形貌的演变规律.研究得出Fe-Cr(12.8%)合金富Cr相时效组织形貌呈现孤立颗粒状空间分布形态,时效机制属于形核-长大(NG)机制;对于Fe-Cr(30.0%)和Fe-Cr(40.0%),富Cr相时效形貌在形核-生长及熟化阶段均呈现为三维蠕虫状空间分布特征,时效机制属于条幅分解(SD)机制;对于Fe-Cr(20.0%)合金,其富Cr相组织演化特征介于NG和SD机制之间.研究进一步发现Cr原子短程序参量可用来分析富Cr相形核-生长阶段Fe-Cr合金原子尺度结构的演变,但对于时效熟化阶段微观结构组织变化不敏感.基于空间粗粒化后Fe-Cr合金微观组织形貌,进一步分析了4种Cr原子含量下Fe-Cr合金相变动力学参数如富Cr相体积分数、平均粒径及相颗粒数密度随时效时间演变.本文建立的质量密度场耦合动力学Monte-Carlo模拟方法,为开发多尺度算法模拟合金时效动力学机制及微观组织形貌演变提供了新的思路和研究基础.

非对称循环弯曲变形再结晶组织的Monte Carlo模拟

利用Monte Carlo方法模拟了非对称循环弯曲变形工艺下的静态再结晶过程,分析了循环道次、退火温度等工艺参数对板材表面及厚度方向上组织演变和晶粒尺寸分布的影响,并基于模拟数据,建立了静态再结晶晶粒尺寸预测模型。结果表明,在450~750℃的退火温度范围内,随着循环道次增加、退火温度升高以及与板材表面距离的减小,静态再结晶晶粒尺寸显著减小。当达到7个循环道次后,板材表面的晶粒尺寸趋于稳定;而在11个循环道次后,厚度方向的晶粒尺寸均匀化,并细化为等轴晶。此外,通过将模拟结果和金相实验得到的再结晶晶粒尺寸进行对比,模型的预测结果和实验数据吻合度高,所建模型的有效性和准确性得到验证。

Simulation and experimental comparison of the performance of four-corner-readout plastic scintillator muon-detector system

Cosmic-ray muons are highly penetrating background-radiation particles found in natural environments.In this study,we develop and test a plastic scintillator muon detector based on machine-learning algorithms.The detector underwent muon position-resolution tests at the Institute of Modern Physics in Lanzhou using a multiwire drift chamber(MWDC)experimental platform.In the simulation,the same structural and performance parameters were maintained to ensure the reliability of the simulation results.The Gaussian process regression(GPR)algorithm was used as the position-reconstruction algorithm owing to its optimal performance.The results of the Time Difference of Arrival algorithm were incorporated as one of the features of the GPR model to reconstruct the muon hit positions.The accuracy of the position reconstruction was evaluated by comparing the experimental results with Geant4 simulation results.In the simulation,large-area plastic scintillator detectors achieved a position resolution better than 20 mm.In the experimental-platform tests,the position resolutions of the test detectors were 27.9 mm.We also analyzed factors affecting the position resolution,including the critical angle of the total internal reflection of the photomultiplier tubes and distribution of muons in the MWDC.Simulations were performed to image both large objects and objects with different atomic numbers.The results showed that the system could image high-and low-Z materials in the constructed model and distinguish objects with significant density differences.This study demonstrates the feasibility of the proposed system,thereby providing a new detector system for muon-imaging applications.

Simulation of space heavy-ion induced primary knock-on atoms in bipolar devices

Bipolar junction transistors(BJTs) are often used in spacecraft due to their excellent working characteristics. However,the complex space radiation environment induces primary knock-on atoms(PKAs) in BJTs through collisions, resulting in hard-to-recover displacement damage and affecting the performance of electronic components. In this paper, the properties of PKAs induced by typical space heavy ions(C, N, O, Fe) in BJTs are investigated using Monte Carlo simulations. The simulated results show that the energy spectrum of ion-induced PKAs is primarily concentrated in the low-energy range(17eV–100eV) and displays similar features across all tested ions. The PKAs induced by the collision of energetic ions have large forward scattering angles, mainly around 88°. Moreover, the distribution of PKAs within a transistor as a function of depth displays a peak characteristic, and the peak position is linearly proportional to the incident energy at a certain energy range. These simulation outcomes serve as crucial theoretical support for long-term semiconductor material defect evolution and ground testing of semiconductor devices.

Three-Dimensional and Cross-sectional Characteristics of Normal Grain Growth Based on Monte Carlo Simulation

An appropriate Monte Carlo method was developed to simulate the three-dimensional normal grain growth more completely. Comparative investigation on the three-dimensional and the cross-sectional characteristics of normal grain growth was done. It was found that the time exponent of grain growth determined from cross-section exhibits the same rule of increasing slowly with time and approaching the theoretical value n = 0.5 of steadygrain growth as the three-dimensional (3-D) system. From change of the number of grains per unit area with timemeasured in cross-section, the state of 3-D normal grain growth may be predicted. The gtain size distribution incross-section is different from that in 3-D system and can not express the evolution characteristic of the 3-D distribution. Furthermore, there exists statistical connection between the topological parameters in cross-section and thosein three-dimensions.

Grain size distribution and topology in 3D grain growth simulation with large-scale Monte Carlo method

Three-dimensional normal grain growth was appropriately simulated using a Potts model Monte Carlo algorithm. The quasi-stationary grain size distribution obtained from simulation agreed well with the experimental result of pure iron. The Weibull function with a parameter β=2.77 and the Yu-Liu function with a parameter v =2.71 fit the quasi-stationary grain size distribution well. The grain volume distribution is a function that decreased exponentially with increasing grain volume. The distribution of boundary area of grains has a peak at S/〈S〉=0.5, where S is the boundary area of a grain and 〈S〉 is the mean boundary area of all grains in the system. The lognormal function fits the face number distribution well and the peak of the face number distribution is f=10. The mean radius off-faced grains is not proportional to the face number, but appears to be related by a curve convex upward. In the 2D cross-section, both the perimeter law and the Aboav-Weaire law are observed to hold.

基于Monte-Carlo模拟的小样本下齿轮疲劳极限计算方法及软件开发

齿轮疲劳极限评估Dixon-Mood(D-M)法的试验样本需求量较大,标准差估计值存在较大偏差,因此基于Monte-Carlo模拟提出小样本的齿轮疲劳极限分析方法(CQUboot),并结合大量弯曲疲劳极限试验对D-M法和CQUboot法进行了对比分析。与GB/T 14230—2021《齿轮弯曲疲劳强度试验方法》推荐的20~22个样本计算结果相比,样本量降至12时,D-M法的最大误差为15.76%~24.99%,CQUboot法为8.02%~12.98%;以12.66%为疲劳极限预估允许的最大误差时,D-M法至少需要10~18个样本点,CQUboot法只需8个样本点。

On the energy conservation electrostatic particle-in-cell/Monte Carlo simulation: Benchmark and application to the radio frequency discharges

We benchmark and analyze the error of energy conservation （EC） scheme in particle-in-cell/Monte Carlo （PIC/MC） algorithms by simulating the radio frequency discharge. The plasma heating behaviors and electron distributing functions obtained by one-dimensional （1D） simulation are analyzed. Both explicit and implicit algorithms are checked. The results showed that the EC scheme can eliminated the self-heating with wide grid spacing in both cases with a small reduction of the accuracies. In typical parameters, the EC implicit scheme has higher precision than EC explicit scheme. Some ＂numerical cooling＂ behaviors are observed and analyzed. Some other errors are also analyzed. The analysis showed that the EC implicit scheme can be used to qualitative estimation of some discharge problems with much less computational resource cost without much loss of accuracies.

Method“Monte Carlo”in healthcare

In public health,simulation modeling stands as an invaluable asset,enabling the evaluation of new systems without their physical implementation,experimentation with existing systems without operational adjustments,and testing system limits without real-world repercussions.In simulation modeling,the Monte Carlo method emerges as a powerful yet underutilized tool.Although the Monte Carlo method has not yet gained widespread prominence in healthcare,its technological capabilities hold promise for substantial cost reduction and risk mitigation.In this review article,we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts.We underscore the significance of experiential insights derived from simulated experimentation,especially in resource-constrained scenarios where time,financial constraints,and limited resources necessitate innovative and efficient approaches.As public health faces increasing challenges,incorporating the Monte Carlo method presents an opportunity for enhanced system construction,analysis,and evaluation.