Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of s...Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.展开更多
Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmenta...Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.展开更多
This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenari...This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.展开更多
Flat-panel X-ray sources(FPXSs)have many advantages in terms of compactness and low-dose imaging,enhancing their capability for novel X-ray applications.Experimental analysis of the X-ray characteristics and optimizin...Flat-panel X-ray sources(FPXSs)have many advantages in terms of compactness and low-dose imaging,enhancing their capability for novel X-ray applications.Experimental analysis of the X-ray characteristics and optimizing the anode panel of an FPXS are time-consuming,expensive,and sometimes impractical.In this study,a FPXS was prepared using a ZnO nanowire cold cathode and a molybdenum film anode target.Monte Carlo(MC)simulations were utilized to optimize the anode panel and obtain the average fluence,average energy,and spatial distribution of the X-rays for the ZnO nanowire FPXS.The accuracy of the MC simulations was verified by comparing the measured and simulated energy spectra.Optimization of the anode target considers the material,thickness,and morphology,whereas optimization of the substrate focuses on the material and thickness.The results show that the difference between the positions of the K-shell peaks in the measured and simulated energy spectra is within 0.26 keV.At the acceleration voltages of 30 kV,60 kV,and 90 kV,the optimal thicknesses of the tungsten array anode were 0.65μm,2.45μm,and 5μm,respectively,while the molybdenum array anode has the optimal thicknesses of 1.45μm,5.25μm,and 24μm,respectively.The microsemi-ellipsoidal anode with a recessed design showed a 5%increase in the transmitted X-ray fluence compared with the film target.The sapphire substrate with a thickness of 0.78 mm exhibits a mechanical strength comparable to that of a glass substrate with a thickness of 3 mm,implying that the former can increase the average X-ray fluence by reducing the filtration of X-rays.The findings of this study provide valuable guidance for the fabrication and optimization of the ZnO nanowire FPXS.展开更多
A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties.Simulating solute segregation in a plastically deformed crystalline system ...A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties.Simulating solute segregation in a plastically deformed crystalline system at atomic resolution remains challenging.The objective is to efficiently model and predict a phys-ically informed segregated solute distribution rather than simulating a series of diffusion kinetics.To ad-dress this objective,we coupled molecular dynamics(MD)and Monte Carlo(MC)methods using a novel method based on virtual atoms technique.We applied our MD-MC coupling approach to model off-lattice carbon(C)solute segregation in nanoindented Fe-C samples containing complex dislocation networks.Our coupling framework yielded the final configuration through efficient parallelization and localized en-ergy computations,showing C Cottrell atmospheres near dislocations.Different initial C concentrations resulted in a consistent trend of C atoms migrating from less crystalline distortion to high crystalline distortion regions.Besides unraveling the strong spatial correlation between local C concentration and defect regions,our results revealed two crucial aspects of solute segregation preferences:(1)defect ener-getics hierarchy and(2)tensile strain fields near dislocations.The proposed approach is generic and can be applied to other material systems as well.展开更多
Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution funct...Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable information.In this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is developed.The framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster convergence.System performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair sequences.Finally,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are evaluated.The framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and resilience.Application results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.展开更多
The Underwater Communication Link(UCL)is a crucial component of Underwater Wireless Optical Communication(UWOC)systems,requiring optimised design to mitigate the high power attenuation inherent in seawater.To ensure t...The Underwater Communication Link(UCL)is a crucial component of Underwater Wireless Optical Communication(UWOC)systems,requiring optimised design to mitigate the high power attenuation inherent in seawater.To ensure the reliability of an optimal UCL design,it is essential to account for the three primary scattering regimes:forward scattering(FSC),backward scattering(BSC),and isotropic scattering(ISC)in seawater channels.This study introduces a new photon-tracking model based on a discrete equation,facilitating Monte Carlo Simulation(MCS)to evaluate how different scattering regimes influence received photon distribution.Three distinct Scattering Regime Contribution Weight(SRCW)probability sets were employed,each representing different UCL operational configurations dominated by specific scattering regimes.The proposed modeling approach enables a comprehensive assessment of the temporal characteristics of received optical pulses,channel loss,and time spread-ultimately defining the optimal UCL design parameters.The key findings of this study include:(1)Enhancing the FSC regime dominance leads to a quasi-light waveguide effect over link spans and small Fields of View(FOV)<25°,significantly improving channel performance in Harbor seawater compared to Coastal seawater.(2)A well-designed UCL with a small FOV(<25°)can minimise channel loss and time spread,ensuring high capacity and efficient performance in both Coastal and Harbor seawaters.(3)When BSC and ISC contributions exceed FSC dominance,the received optical pulse undergoes significant temporal broadening,particularly for larger FOV angles(>25°)and extended link spans.(4)The developed novel MCS-based discrete equation provides a simple yet robust model for simulating photon propagation in both homogeneous and inhomogeneous underwater channels.These insights contribute to developing more efficient and reliable UCL designs with military standards by enhancing UWOC system performance over a longer linkspan for a given limited optical power across various underwater environments.展开更多
针对多部位损伤(Multiple Site Damage,MSD)结构安全性评估问题,通过Monte-Carlo方法对MSD结构的失效概率进行预测和分析。首先,基于多孔铝板的多裂纹萌生试验,得出裂纹萌生寿命服从对数正态分布,为多裂纹萌生分析提供支持;通过多孔铝...针对多部位损伤(Multiple Site Damage,MSD)结构安全性评估问题,通过Monte-Carlo方法对MSD结构的失效概率进行预测和分析。首先,基于多孔铝板的多裂纹萌生试验,得出裂纹萌生寿命服从对数正态分布,为多裂纹萌生分析提供支持;通过多孔铝板的剩余强度试验得到铆钉孔直径、铆钉孔间距和裂纹萌生位置对结构剩余强度均有一定影响。其次,通过对裂纹萌生寿命分布进行随机抽样生成初始裂纹并使用组合法结合Paris公式,实现多裂纹随机扩展的模拟;在试验数据基础上,对传统的Irwin塑性区连通准则进行改进,发现改进的Irwin塑性区连通准则在孔间距大于10mm时的误差大大降低,并结合净截面屈服准则以获得更好的剩余强度预测结果;将随机性的裂纹萌生和扩展过程与确定性的剩余强度预测方法相结合,建立基于Monte-Carlo方法的MSD结构的失效概率预测模型。最后,通过算例分析,该模型能够得到MSD结构的失效概率曲线,实现结构安全性评估。结果表明MSD结构的失效概率会在短时间内迅速增加,需要在裂纹萌生寿命附近进行限制。展开更多
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 ...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.展开更多
The Monte Carlo(MC)method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis,radiation ...The Monte Carlo(MC)method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis,radiation shielding design,and medical physics.However,with the rapid advancement of new nuclear energy systems,the Monte Carlo method faces challenges in efficiency,accuracy,and adaptability,limiting its effectiveness in meeting modern design requirements.Overcoming technical obstacles related to high-fidelity coupling,high-resolution computation,and intelligent design is essential for using the Monte Carlo method as a reliable tool in numerical analysis for these new nuclear energy systems.To address these challenges,the Nuclear Energy and Application Laboratory(NEAL)team at the University of South China developed a multifunctional and generalized intelligent code platform called MagicMC,based on the Monte Carlo particle transport method.MagicMC is a developing tool dedicated to nuclear applications,incorporating intelligent methodologies.It consists of two primary components:a basic unit and a functional unit.The basic unit,which functions similarly to a standard Monte Carlo particle transport code,includes seven modules:geometry,source,transport,database,tally,output,and auxiliary.The functional unit builds on the basic unit by adding functional modules to address complex and diverse applications in nuclear analysis.MagicMC introduces a dynamic Monte Carlo particle transport algorithm to address time-space particle transport problems within emerging nuclear energy systems and incorporates a CPU-GPU heterogeneous parallel framework to enable high-efficiency,high-resolution simulations for large-scale computational problems.Anticipating future trends in intelligent design,MagicMC integrates several advanced features,including CAD-based geometry modeling,global variance reduction methods,multi-objective shielding optimization,high-resolution activation analysis,multi-physics coupling,and radiation therapy.In this paper,various numerical benchmarks-spanning reactor transient simulations,material activation analysis,radiation shielding optimization,and medical dosimetry analysis-are presented to validate MagicMC.The numerical results demonstrate MagicMC's efficiency,accuracy,and reliability in these preliminary applications,underscoring its potential to support technological advancements in developing high-fidelity,high-resolution,and high-intelligence MC-based tools for advanced nuclear applications.展开更多
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 d...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.展开更多
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 witho...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.展开更多
随着自动驾驶技术的快速发展,如何保证自动驾驶系统的安全性变得愈发重要,因此预期功能安全(Safety of The Intended Functionality, SOTIF)的概念应运而生,它主要是为了减少由于系统非预期的感知和决策错误而引起的危害。本文提出了一...随着自动驾驶技术的快速发展,如何保证自动驾驶系统的安全性变得愈发重要,因此预期功能安全(Safety of The Intended Functionality, SOTIF)的概念应运而生,它主要是为了减少由于系统非预期的感知和决策错误而引起的危害。本文提出了一种依托自然驾驶数据的SOTIF自动化生成测试用例的方法。通过采集360°IBEO与环视摄像头数据,分析了4000多个前车切入场景,对关键变量进行参数化建模。采用改进的Monte-Carlo抽样技术,处理独立与非独立随机变量的联合分布,生成覆盖广泛场景的测试用例。实验结果表明该方法显著提升了测试用例生成效率,全面覆盖边角、危险及极端场景,解决了SOTIF测试中自动化生成测试用例的难题,为自动驾驶系统的预期功能安全评估提供了有效支持。展开更多
文摘Gassy soils are distributed in relatively shallow layers the Quaternary deposit in Hangzhou Bay area. The shallow gassy soils significantly affect the construction of underground projects. Proper characterization of spatial distribution of shallow gassy soils is indispensable prior to construction of underground projects in the area. Due to the costly conditions required in the site investigation for gassy soils, only a limited number of gas pressure data can be obtained in engineering practice, which leads to the uncertainty in characterizing spatial distribution of gassy soils. Determining the number of boreholes for investigating gassy soils and their corresponding locations is pivotal to reducing construction risk induced by gassy soils. However, this primarily relies on the engineering experience in the current site investigation practice. This study develops a probabilistic site investigation optimization method for planning investigation schemes (including the number and locations of boreholes) of gassy soils based on the conditional random field and Monte Carlo simulation. The proposed method aims to provide an optimal investigation scheme before the site investigation based on prior knowledge. Finally, the proposed approach is illustrated using a case study.
基金Postgraduate Innovation Top notch Talent Training Project of Hunan Province,Grant/Award Number:CX20220045Scientific Research Project of National University of Defense Technology,Grant/Award Number:22-ZZCX-07+2 种基金New Era Education Quality Project of Anhui Province,Grant/Award Number:2023cxcysj194National Natural Science Foundation of China,Grant/Award Numbers:62201597,62205372,1210456foundation of Hefei Comprehensive National Science Center,Grant/Award Number:KY23C502。
文摘Large-scale point cloud datasets form the basis for training various deep learning networks and achieving high-quality network processing tasks.Due to the diversity and robustness constraints of the data,data augmentation(DA)methods are utilised to expand dataset diversity and scale.However,due to the complex and distinct characteristics of LiDAR point cloud data from different platforms(such as missile-borne and vehicular LiDAR data),directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks.To address this issue,the present study explores DA for missile-borne LiDAR point cloud using a Monte Carlo(MC)simulation method that closely resembles practical application.Firstly,the model of multi-sensor imaging system is established,taking into account the joint errors arising from the platform itself and the relative motion during the imaging process.A distortion simulation method based on MC simulation for augmenting missile-borne LiDAR point cloud data is proposed,underpinned by an analysis of combined errors between different modal sensors,achieving high-quality augmentation of point cloud data.The effectiveness of the proposed method in addressing imaging system errors and distortion simulation is validated using the imaging scene dataset constructed in this paper.Comparative experiments between the proposed point cloud DA algorithm and the current state-of-the-art algorithms in point cloud detection and single object tracking tasks demonstrate that the proposed method can improve the network performance obtained from unaugmented datasets by over 17.3%and 17.9%,surpassing SOTA performance of current point cloud DA algorithms.
文摘This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.
基金supported by the National Key Research and Development Program of China(Nos.2022YFA1204203 and 2022YFA1204201)Opening Fund of the State Key Laboratory of Optoelectronic Materials and Technologies at Sun Yat-sen University(No.OEMT-2023-KF-01)+1 种基金National Natural Science Foundation of China(Nos.61971463,82272131,and 82202960)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515010537).
文摘Flat-panel X-ray sources(FPXSs)have many advantages in terms of compactness and low-dose imaging,enhancing their capability for novel X-ray applications.Experimental analysis of the X-ray characteristics and optimizing the anode panel of an FPXS are time-consuming,expensive,and sometimes impractical.In this study,a FPXS was prepared using a ZnO nanowire cold cathode and a molybdenum film anode target.Monte Carlo(MC)simulations were utilized to optimize the anode panel and obtain the average fluence,average energy,and spatial distribution of the X-rays for the ZnO nanowire FPXS.The accuracy of the MC simulations was verified by comparing the measured and simulated energy spectra.Optimization of the anode target considers the material,thickness,and morphology,whereas optimization of the substrate focuses on the material and thickness.The results show that the difference between the positions of the K-shell peaks in the measured and simulated energy spectra is within 0.26 keV.At the acceleration voltages of 30 kV,60 kV,and 90 kV,the optimal thicknesses of the tungsten array anode were 0.65μm,2.45μm,and 5μm,respectively,while the molybdenum array anode has the optimal thicknesses of 1.45μm,5.25μm,and 24μm,respectively.The microsemi-ellipsoidal anode with a recessed design showed a 5%increase in the transmitted X-ray fluence compared with the film target.The sapphire substrate with a thickness of 0.78 mm exhibits a mechanical strength comparable to that of a glass substrate with a thickness of 3 mm,implying that the former can increase the average X-ray fluence by reducing the filtration of X-rays.The findings of this study provide valuable guidance for the fabrication and optimization of the ZnO nanowire FPXS.
基金the funding from the Ger-man Research Foundation(DFG)-BE 5360/1-1 and ThyssenKrupp Europe.
文摘A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties.Simulating solute segregation in a plastically deformed crystalline system at atomic resolution remains challenging.The objective is to efficiently model and predict a phys-ically informed segregated solute distribution rather than simulating a series of diffusion kinetics.To ad-dress this objective,we coupled molecular dynamics(MD)and Monte Carlo(MC)methods using a novel method based on virtual atoms technique.We applied our MD-MC coupling approach to model off-lattice carbon(C)solute segregation in nanoindented Fe-C samples containing complex dislocation networks.Our coupling framework yielded the final configuration through efficient parallelization and localized en-ergy computations,showing C Cottrell atmospheres near dislocations.Different initial C concentrations resulted in a consistent trend of C atoms migrating from less crystalline distortion to high crystalline distortion regions.Besides unraveling the strong spatial correlation between local C concentration and defect regions,our results revealed two crucial aspects of solute segregation preferences:(1)defect ener-getics hierarchy and(2)tensile strain fields near dislocations.The proposed approach is generic and can be applied to other material systems as well.
基金National Key R&D Program of China under Grant No.2022YFC3003600National Natural Science Foundation of China(NSFC)under Grant No.51978023。
文摘Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable information.In this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is developed.The framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster convergence.System performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair sequences.Finally,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are evaluated.The framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and resilience.Application results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia,has funded this project under Grant No.(KEP-PhD:72-130-1443).
文摘The Underwater Communication Link(UCL)is a crucial component of Underwater Wireless Optical Communication(UWOC)systems,requiring optimised design to mitigate the high power attenuation inherent in seawater.To ensure the reliability of an optimal UCL design,it is essential to account for the three primary scattering regimes:forward scattering(FSC),backward scattering(BSC),and isotropic scattering(ISC)in seawater channels.This study introduces a new photon-tracking model based on a discrete equation,facilitating Monte Carlo Simulation(MCS)to evaluate how different scattering regimes influence received photon distribution.Three distinct Scattering Regime Contribution Weight(SRCW)probability sets were employed,each representing different UCL operational configurations dominated by specific scattering regimes.The proposed modeling approach enables a comprehensive assessment of the temporal characteristics of received optical pulses,channel loss,and time spread-ultimately defining the optimal UCL design parameters.The key findings of this study include:(1)Enhancing the FSC regime dominance leads to a quasi-light waveguide effect over link spans and small Fields of View(FOV)<25°,significantly improving channel performance in Harbor seawater compared to Coastal seawater.(2)A well-designed UCL with a small FOV(<25°)can minimise channel loss and time spread,ensuring high capacity and efficient performance in both Coastal and Harbor seawaters.(3)When BSC and ISC contributions exceed FSC dominance,the received optical pulse undergoes significant temporal broadening,particularly for larger FOV angles(>25°)and extended link spans.(4)The developed novel MCS-based discrete equation provides a simple yet robust model for simulating photon propagation in both homogeneous and inhomogeneous underwater channels.These insights contribute to developing more efficient and reliable UCL designs with military standards by enhancing UWOC system performance over a longer linkspan for a given limited optical power across various underwater environments.
文摘针对多部位损伤(Multiple Site Damage,MSD)结构安全性评估问题,通过Monte-Carlo方法对MSD结构的失效概率进行预测和分析。首先,基于多孔铝板的多裂纹萌生试验,得出裂纹萌生寿命服从对数正态分布,为多裂纹萌生分析提供支持;通过多孔铝板的剩余强度试验得到铆钉孔直径、铆钉孔间距和裂纹萌生位置对结构剩余强度均有一定影响。其次,通过对裂纹萌生寿命分布进行随机抽样生成初始裂纹并使用组合法结合Paris公式,实现多裂纹随机扩展的模拟;在试验数据基础上,对传统的Irwin塑性区连通准则进行改进,发现改进的Irwin塑性区连通准则在孔间距大于10mm时的误差大大降低,并结合净截面屈服准则以获得更好的剩余强度预测结果;将随机性的裂纹萌生和扩展过程与确定性的剩余强度预测方法相结合,建立基于Monte-Carlo方法的MSD结构的失效概率预测模型。最后,通过算例分析,该模型能够得到MSD结构的失效概率曲线,实现结构安全性评估。结果表明MSD结构的失效概率会在短时间内迅速增加,需要在裂纹萌生寿命附近进行限制。
基金supported by Project of Chongqing Science and Technology Bureau (cstc2022jxjl0005)。
文摘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.
基金supported by the National Natural Science Foundation of China(Nos.12475174 and U2267207)YueLuShan Center Industrial Innovation(No.2024YCII0108)+2 种基金Natural Science Foundation of Hunan Province(No.2022JJ40345)Science and Technology Innovation Project of Hengyang(No.202250045336)the Project of State Key Laboratory of Radiation Medicine and Protection,Soochow University(No.GZK12023031)。
文摘The Monte Carlo(MC)method offers significant advantages in handling complex geometries and physical processes in particle transport problems and has become a widely used approach in reactor physics analysis,radiation shielding design,and medical physics.However,with the rapid advancement of new nuclear energy systems,the Monte Carlo method faces challenges in efficiency,accuracy,and adaptability,limiting its effectiveness in meeting modern design requirements.Overcoming technical obstacles related to high-fidelity coupling,high-resolution computation,and intelligent design is essential for using the Monte Carlo method as a reliable tool in numerical analysis for these new nuclear energy systems.To address these challenges,the Nuclear Energy and Application Laboratory(NEAL)team at the University of South China developed a multifunctional and generalized intelligent code platform called MagicMC,based on the Monte Carlo particle transport method.MagicMC is a developing tool dedicated to nuclear applications,incorporating intelligent methodologies.It consists of two primary components:a basic unit and a functional unit.The basic unit,which functions similarly to a standard Monte Carlo particle transport code,includes seven modules:geometry,source,transport,database,tally,output,and auxiliary.The functional unit builds on the basic unit by adding functional modules to address complex and diverse applications in nuclear analysis.MagicMC introduces a dynamic Monte Carlo particle transport algorithm to address time-space particle transport problems within emerging nuclear energy systems and incorporates a CPU-GPU heterogeneous parallel framework to enable high-efficiency,high-resolution simulations for large-scale computational problems.Anticipating future trends in intelligent design,MagicMC integrates several advanced features,including CAD-based geometry modeling,global variance reduction methods,multi-objective shielding optimization,high-resolution activation analysis,multi-physics coupling,and radiation therapy.In this paper,various numerical benchmarks-spanning reactor transient simulations,material activation analysis,radiation shielding optimization,and medical dosimetry analysis-are presented to validate MagicMC.The numerical results demonstrate MagicMC's efficiency,accuracy,and reliability in these preliminary applications,underscoring its potential to support technological advancements in developing high-fidelity,high-resolution,and high-intelligence MC-based tools for advanced nuclear applications.
基金funded by the Chinese Academy of Medical Science health innovation project(grant nos.2021-I2M-1-042,2021-I2M-1-058,and 2022-I2M-C&T-A-005)Tianjin Outstanding Youth Fund Project(grant no.20JCJQIC00230)CAMS Innovation Fund for Medical Sciences(CIFMS)(grant no.2022-I2M-C&T-B-012).
文摘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.
基金Key Research and Development Program of Guangdong Province (No.2020B0101130009)
文摘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.
文摘随着自动驾驶技术的快速发展,如何保证自动驾驶系统的安全性变得愈发重要,因此预期功能安全(Safety of The Intended Functionality, SOTIF)的概念应运而生,它主要是为了减少由于系统非预期的感知和决策错误而引起的危害。本文提出了一种依托自然驾驶数据的SOTIF自动化生成测试用例的方法。通过采集360°IBEO与环视摄像头数据,分析了4000多个前车切入场景,对关键变量进行参数化建模。采用改进的Monte-Carlo抽样技术,处理独立与非独立随机变量的联合分布,生成覆盖广泛场景的测试用例。实验结果表明该方法显著提升了测试用例生成效率,全面覆盖边角、危险及极端场景,解决了SOTIF测试中自动化生成测试用例的难题,为自动驾驶系统的预期功能安全评估提供了有效支持。
