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
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
Scramjet is the most promising propulsion system for Air-breathing Hypersonic Vehicle(AHV),and the Infrared(IR)radiation it emits is critical for early warning,detection,and identification of such weapons.This work pr...Scramjet is the most promising propulsion system for Air-breathing Hypersonic Vehicle(AHV),and the Infrared(IR)radiation it emits is critical for early warning,detection,and identification of such weapons.This work proposes an Adaptive Reverse Monte Carlo(ARMC)method and develops an analytical model for the IR radiation of scramjet considering gaseous kerosene and hydrogen fueled conditions.The evaluation studies show that at a global equivalence ratio of 0.8,the IR radiation from hydrogen-fueled plume is predominantly from H_(2)O and spectral peak is 1.53 kW·Sr^(-1)·μm^(-1)at the 2.7μm band,while the kerosene-fueled plume exhibits a spectral intensity approaching 7.0 kW·Sr^(-1)·μm^(-1)at the 4.3μm band.At the backward detection angle,both types of scramjets exhibit spectral peaks within the 1.3-1.4μm band,with intensities around10 kW·Sr^(-1)·μm^(-1).The integral radiation intensity of hydrogen-fueled scramjet is generally higher than kerosene-fueled scramjet,particularly in 1-3μm band.Meanwhile,at wide detection angles,the solid walls become the predominant radiation source.The radiation intensity is highest in1-3μm and weakest in 8-14μm band,with values of 21.5 kW·Sr^(-1)and 0.57 kW·Sr^(-1)at the backward detection angles,respectively.Significant variations in the radiation contributions from gases and solids are observed across different bands under the two fuel conditions,especially within 3-5μm band.This research provides valuable insights into the IR radiation characteristics of scramjets,which can aid in the development of IR detection systems for AHV.展开更多
Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patte...Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns.This complexity poses significant challenges for slope stability analysis,requiring the development of specialized techniques to address these issues.This research presents a numerical methodology that incorporates spatial variability,nonlinear material characterization,and probabilistic analysis using a Monte Carlo framework to address this issue.The heterogeneous structure is represented by randomly assigning different lithotypes across the slope,while maintaining predefined global proportions.This contrasts with the more common approach of applying probabilistic variability to mechanical parameters within a homogeneous slope model.The material behavior is defined using complex nonlinear failure criteria,such as the Hoek-Brown model and a parabolic model with collapse,both implemented through linearization techniques.The Discontinuity Layout Optimization(DLO)method,a novel numerical approach based on limit analysis,is employed to efficiently incorporate these advances and compute the factor of safety of the slope.Within this framework,the Monte Carlo procedure is used to assess slope stability by conducting a large number of simulations,each with a different lithotype distribution.Based on the results,a hybrid method is proposed that combines probabilistic modeling with deterministic design principles for the slope stability assessment.As a case study,the methodology is applied to a 20-m-high vertical slope composed of three lithotypes(altered scoria,welded scoria,and basalt)randomly distributed in proportions of 15%,60%,and 25%,respectively.The results show convergence of mean values after approximately 400 simulations and highlight the significant influence of spatial heterogeneity,with variations of the factor of safety between 5 and 12 in 85%of cases.They also reveal non-circular and mid-slope failure wedges not captured by traditional stability methods.Finally,an equivalent normal probability distribution is proposed as a reliable approximation of the factor of safety for use in risk analysis and engineering decision-making.展开更多
GPU-based Monte Carlo(MC)simulations are highly valued for their potential to improve both the computational efficiency and accuracy of radiotherapy.However,in proton therapy,these methods often simplify human tissues...GPU-based Monte Carlo(MC)simulations are highly valued for their potential to improve both the computational efficiency and accuracy of radiotherapy.However,in proton therapy,these methods often simplify human tissues as water for nuclear reactions,disregarding their true elemental composition and thereby potentially compromising calculation accuracy.Consequently,this study developed the program g MCAP(GPU-based proton MC Algorithm for Proton therapy),incorporating precise discrete interactions,and established a refined nuclear reaction model(REFINED)that considers the actual materials of the human body.Compared to the approximate water model(APPROX),the REFINED model demonstrated an improvement in calculation accuracy of 3%.In particular,in high-density tissue regions,the maximum dose deviation between the REFINED and APPROX models was up to 15%.In summary,the g MCAP program can efficiently simulate 1 million protons within 1 s while significantly enhancing dose calculation accuracy in high-density tissues,thus providing a more precise and efficient engine for proton radiotherapy dose calculations in clinical practice.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
Monte Carlo(MC) simulations have been performed to refine the estimation of the correction-toscaling exponent ω in the 2D φ^(4)model,which belongs to one of the most fundamental universality classes.If corrections h...Monte Carlo(MC) simulations have been performed to refine the estimation of the correction-toscaling exponent ω in the 2D φ^(4)model,which belongs to one of the most fundamental universality classes.If corrections have the form ∝ L^(-ω),then we find ω=1.546(30) andω=1.509(14) as the best estimates.These are obtained from the finite-size scaling of the susceptibility data in the range of linear lattice sizes L ∈[128,2048] at the critical value of the Binder cumulant and from the scaling of the corresponding pseudocritical couplings within L∈[64,2048].These values agree with several other MC estimates at the assumption of the power-law corrections and are comparable with the known results of the ε-expansion.In addition,we have tested the consistency with the scaling corrections of the form ∝ L^(-4/3),∝L^(-4/3)In L and ∝L^(-4/3)/ln L,which might be expected from some considerations of the renormalization group and Coulomb gas model.The latter option is consistent with our MC data.Our MC results served as a basis for a critical reconsideration of some earlier theoretical conjectures and scaling assumptions.In particular,we have corrected and refined our previous analysis by grouping Feynman diagrams.The renewed analysis gives ω≈4-d-2η as some approximation for spatial dimensions d <4,or ω≈1.5 in two dimensions.展开更多
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
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
In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were perform...In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were performed using CAD-to-Monte Carlo geometry conversion(CMGC)code.Boundary representation(BRep)to constructive solid geometry(CSG)conversion and visual CSG modeling were combined to address the problem of non-convertible geometries such as spline surfaces.The splitting surface assessment method in BRep-to-CSG conversion was optimized to reduce the number of Boolean operations using an Open Cascade.This,in turn,reduced the probability of CMGC conversion failure.The auxiliary surface generation algorithm was optimized to prevent the generation of redundant auxiliary surfaces that cause an excessive decomposition of CAD geometry solids.These optimizations enhanced the usability and stability of the CMGC model conversion.CMGC was applied successfully to the JMCT transportation calculations for the conceptual designs of five China Fusion Engineering Test Reactor(CFETR)blankets.The rapid replacement of different blanket schemes was achieved based on the baseline CFETR model.The geometric solid number of blankets ranged from hundreds to tens of thousands.The correctness of the converted CFETR models using CMGC was verified through comparisons with the MCNP calculation results.The CMGC supported radiation field evaluations for a large urban scene and detailed ship scene.This enabled the rapid conversion of CAD models with thousands of geometric solids into Monte Carlo CSG models.An analysis of the JMCT transportation simulation results further demonstrated the accuracy and effectiveness of the CMGC.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
We extend a semiclassical numerical method, bosonic auxiliary-field Monte Carlo, to quantum spin systems. This method breaks the lattice into clusters, solves each cluster precisely and couples them with classical aux...We extend a semiclassical numerical method, bosonic auxiliary-field Monte Carlo, to quantum spin systems. This method breaks the lattice into clusters, solves each cluster precisely and couples them with classical auxiliary fields through classical Monte Carlo simulation. We test the method with antiferromagnetic spin models in one-dimensional chains, square lattices and triangular lattices, and obtain reasonable results at finite temperatures. This algorithm builds a bridge between classical Monte Carlo method and quantum methods. The algorithm can be improved with either progress in classical Monte Carlo sampling or the development of quantum solvers, and can also be further applied to systems with different lattices or interactions.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
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
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supported by the National Natural Science Foundation of China(No.12102356)。
文摘Scramjet is the most promising propulsion system for Air-breathing Hypersonic Vehicle(AHV),and the Infrared(IR)radiation it emits is critical for early warning,detection,and identification of such weapons.This work proposes an Adaptive Reverse Monte Carlo(ARMC)method and develops an analytical model for the IR radiation of scramjet considering gaseous kerosene and hydrogen fueled conditions.The evaluation studies show that at a global equivalence ratio of 0.8,the IR radiation from hydrogen-fueled plume is predominantly from H_(2)O and spectral peak is 1.53 kW·Sr^(-1)·μm^(-1)at the 2.7μm band,while the kerosene-fueled plume exhibits a spectral intensity approaching 7.0 kW·Sr^(-1)·μm^(-1)at the 4.3μm band.At the backward detection angle,both types of scramjets exhibit spectral peaks within the 1.3-1.4μm band,with intensities around10 kW·Sr^(-1)·μm^(-1).The integral radiation intensity of hydrogen-fueled scramjet is generally higher than kerosene-fueled scramjet,particularly in 1-3μm band.Meanwhile,at wide detection angles,the solid walls become the predominant radiation source.The radiation intensity is highest in1-3μm and weakest in 8-14μm band,with values of 21.5 kW·Sr^(-1)and 0.57 kW·Sr^(-1)at the backward detection angles,respectively.Significant variations in the radiation contributions from gases and solids are observed across different bands under the two fuel conditions,especially within 3-5μm band.This research provides valuable insights into the IR radiation characteristics of scramjets,which can aid in the development of IR detection systems for AHV.
基金the project PID2022-139202OB-I00Neural Networks and Optimization Techniques for the Design and Safe Maintenance of Transportation Infrastructures:Volcanic Rock Geotechnics and Slope Stability(IA-Pyroslope),funded by the Spanish State Research Agency of the Ministry of Science,Innovation and Universities of Spain and the European Regional Development Fund,MCIN/AEI/10.13039/501100011033/FEDER,EU。
文摘Volcanic terrains exhibit a complex structure of pyroclastic deposits interspersed with sedimentary processes,resulting in irregular lithological sequences that lack lateral continuity and distinct stratigraphic patterns.This complexity poses significant challenges for slope stability analysis,requiring the development of specialized techniques to address these issues.This research presents a numerical methodology that incorporates spatial variability,nonlinear material characterization,and probabilistic analysis using a Monte Carlo framework to address this issue.The heterogeneous structure is represented by randomly assigning different lithotypes across the slope,while maintaining predefined global proportions.This contrasts with the more common approach of applying probabilistic variability to mechanical parameters within a homogeneous slope model.The material behavior is defined using complex nonlinear failure criteria,such as the Hoek-Brown model and a parabolic model with collapse,both implemented through linearization techniques.The Discontinuity Layout Optimization(DLO)method,a novel numerical approach based on limit analysis,is employed to efficiently incorporate these advances and compute the factor of safety of the slope.Within this framework,the Monte Carlo procedure is used to assess slope stability by conducting a large number of simulations,each with a different lithotype distribution.Based on the results,a hybrid method is proposed that combines probabilistic modeling with deterministic design principles for the slope stability assessment.As a case study,the methodology is applied to a 20-m-high vertical slope composed of three lithotypes(altered scoria,welded scoria,and basalt)randomly distributed in proportions of 15%,60%,and 25%,respectively.The results show convergence of mean values after approximately 400 simulations and highlight the significant influence of spatial heterogeneity,with variations of the factor of safety between 5 and 12 in 85%of cases.They also reveal non-circular and mid-slope failure wedges not captured by traditional stability methods.Finally,an equivalent normal probability distribution is proposed as a reliable approximation of the factor of safety for use in risk analysis and engineering decision-making.
文摘GPU-based Monte Carlo(MC)simulations are highly valued for their potential to improve both the computational efficiency and accuracy of radiotherapy.However,in proton therapy,these methods often simplify human tissues as water for nuclear reactions,disregarding their true elemental composition and thereby potentially compromising calculation accuracy.Consequently,this study developed the program g MCAP(GPU-based proton MC Algorithm for Proton therapy),incorporating precise discrete interactions,and established a refined nuclear reaction model(REFINED)that considers the actual materials of the human body.Compared to the approximate water model(APPROX),the REFINED model demonstrated an improvement in calculation accuracy of 3%.In particular,in high-density tissue regions,the maximum dose deviation between the REFINED and APPROX models was up to 15%.In summary,the g MCAP program can efficiently simulate 1 million protons within 1 s while significantly enhancing dose calculation accuracy in high-density tissues,thus providing a more precise and efficient engine for proton radiotherapy dose calculations in clinical practice.
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Monte Carlo(MC) simulations have been performed to refine the estimation of the correction-toscaling exponent ω in the 2D φ^(4)model,which belongs to one of the most fundamental universality classes.If corrections have the form ∝ L^(-ω),then we find ω=1.546(30) andω=1.509(14) as the best estimates.These are obtained from the finite-size scaling of the susceptibility data in the range of linear lattice sizes L ∈[128,2048] at the critical value of the Binder cumulant and from the scaling of the corresponding pseudocritical couplings within L∈[64,2048].These values agree with several other MC estimates at the assumption of the power-law corrections and are comparable with the known results of the ε-expansion.In addition,we have tested the consistency with the scaling corrections of the form ∝ L^(-4/3),∝L^(-4/3)In L and ∝L^(-4/3)/ln L,which might be expected from some considerations of the renormalization group and Coulomb gas model.The latter option is consistent with our MC data.Our MC results served as a basis for a critical reconsideration of some earlier theoretical conjectures and scaling assumptions.In particular,we have corrected and refined our previous analysis by grouping Feynman diagrams.The renewed analysis gives ω≈4-d-2η as some approximation for spatial dimensions d <4,or ω≈1.5 in two dimensions.
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.
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 Natural Science Foundation of China(No.U23B2067)Innovation Program of CAEP(No.CX20210045)。
文摘In response to the demand for rapid geometric modeling in Monte Carlo radiation transportation calculations for large-scale and complex geometric scenes,functional improvements,and algorithm optimizations were performed using CAD-to-Monte Carlo geometry conversion(CMGC)code.Boundary representation(BRep)to constructive solid geometry(CSG)conversion and visual CSG modeling were combined to address the problem of non-convertible geometries such as spline surfaces.The splitting surface assessment method in BRep-to-CSG conversion was optimized to reduce the number of Boolean operations using an Open Cascade.This,in turn,reduced the probability of CMGC conversion failure.The auxiliary surface generation algorithm was optimized to prevent the generation of redundant auxiliary surfaces that cause an excessive decomposition of CAD geometry solids.These optimizations enhanced the usability and stability of the CMGC model conversion.CMGC was applied successfully to the JMCT transportation calculations for the conceptual designs of five China Fusion Engineering Test Reactor(CFETR)blankets.The rapid replacement of different blanket schemes was achieved based on the baseline CFETR model.The geometric solid number of blankets ranged from hundreds to tens of thousands.The correctness of the converted CFETR models using CMGC was verified through comparisons with the MCNP calculation results.The CMGC supported radiation field evaluations for a large urban scene and detailed ship scene.This enabled the rapid conversion of CAD models with thousands of geometric solids into Monte Carlo CSG models.An analysis of the JMCT transportation simulation results further demonstrated the accuracy and effectiveness of the CMGC.
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supports by the National Key Research and Development Program of China (Grant No.2024YFA1409200)the National Natural Science Foundation of China (Grant Nos.12222412 and 12047503)+1 种基金CAS Project for Young Scientists in Basic Research (Grant No.YSBR-057)supports by the National Natural Science Foundation of China (Grant No.12374144)。
文摘We extend a semiclassical numerical method, bosonic auxiliary-field Monte Carlo, to quantum spin systems. This method breaks the lattice into clusters, solves each cluster precisely and couples them with classical auxiliary fields through classical Monte Carlo simulation. We test the method with antiferromagnetic spin models in one-dimensional chains, square lattices and triangular lattices, and obtain reasonable results at finite temperatures. This algorithm builds a bridge between classical Monte Carlo method and quantum methods. The algorithm can be improved with either progress in classical Monte Carlo sampling or the development of quantum solvers, and can also be further applied to systems with different lattices or interactions.
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 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.
