Chord length sampling correction analysis for dispersion fuel in Monte Carlo simulation

Dispersion fuels,knowned for their excellent safety performance,are widely used in advanced reactors,such as hightemperature gas-cooled reactors.Compared with deterministic methods,the Monte Carlo method has more advantages in the geometric modeling of stochastic media.The explicit modeling method has high computational accuracy and high computational cost.The chord length sampling(CLS)method can improve computational efficiency by sampling the chord length during neutron transport using the matrix chord length?s probability density function.This study shows that the excluded-volume effect in realistic stochastic media can introduce certain deviations into the CLS.A chord length correction approach is proposed to obtain the chord length correction factor by developing the Particle code based on equivalent transmission probability.Through numerical analysis against reference solutions from explicit modeling in the RMC code,it was demonstrated that CLS with the proposed correction method provides good accuracy for addressing the excludedvolume effect in realistic infinite stochastic media.

Emergent topological ordered phase for the Ising-XY model revealed by cluster-updating Monte Carlo method

The two-component cold atom systems with anisotropic hopping amplitudes can be phenomenologically described by a two-dimensional Ising-XY coupled model with spatial anisotropy.At low temperatures,theoretical predictions[Phys.Rev.A 72053604(2005)]and[arXiv:0706.1609]indicate the existence of a topological ordered phase characterized by Ising and XY disorder but with 2XY ordering.However,due to ergodic difficulties faced by Monte Carlo methods at low temperatures,this topological phase has not been numerically explored.We propose a linear cluster updating Monte Carlo method,which flips spins without rejection in the anisotropy limit but does not change the energy.Using this scheme and conventional Monte Carlo methods,we succeed in revealing the nature of topological phases with half-vortices and domain walls.In the constructed global phase diagram,Ising and XY-type transitions are very close to each other and differ significantly from the schematic phase diagram reported earlier.We also propose and explore a wide range of quantities,including magnetism,superfluidity,specific heat,susceptibility,and even percolation susceptibility,and obtain consistent and reliable results.Furthermore,we observed first-order transitions characterized by common intersection points in magnetizations for different system sizes,as opposed to the conventional phase transition where Binder cumulants of various sizes share common intersections.The critical exponents of different types of phase transitions are reasonably fitted.The results are useful to help cold atom experiments explore the half-vortex topological phase.

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

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

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

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

Enhancing Precision in Radiotherapy Delivery: Validating Monte Carlo Simulation Models for 6 MV Elekta Synergy Agility LINAC Photon Beam Using Two Models of the GAMOS Code

The most crucial requirement in radiation therapy treatment planning is a fast and accurate treatment planning system that minimizes damage to healthy tissues surrounding cancer cells. The use of Monte Carlo toolkits has become indispensable for research aimed at precisely determining the dose in radiotherapy. Among the numerous algorithms developed in recent years, the GAMOS code, which utilizes the Geant4 toolkit for Monte Carlo simula-tions, incorporates various electromagnetic physics models and multiple scattering models for simulating particle interactions with matter. This makes it a valuable tool for dose calculations in medical applications and throughout the patient’s volume. The aim of this present work aims to vali-date the GAMOS code for the simulation of a 6 MV photon-beam output from the Elekta Synergy Agility linear accelerator. The simulation involves mod-eling the major components of the accelerator head and the interactions of the radiation beam with a homogeneous water phantom and particle information was collected following the modeling of the phase space. This space was po-sitioned under the X and Y jaws, utilizing three electromagnetic physics mod-els of the GAMOS code: Standard, Penelope, and Low-Energy, along with three multiple scattering models: Goudsmit-Saunderson, Urban, and Wentzel-VI. The obtained phase space file was used as a particle source to simulate dose distributions (depth-dose and dose profile) for field sizes of 5 × 5 cm2 and 10 × 10 cm2 at depths of 10 cm and 20 cm in a water phantom, with a source-surface distance (SSD) of 90 cm from the target. We compared the three electromagnetic physics models and the three multiple scattering mod-els of the GAMOS code to experimental results. Validation of our results was performed using the gamma index, with an acceptability criterion of 3% for the dose difference (DD) and 3 mm for the distance-to-agreement (DTA). We achieved agreements of 94% and 96%, respectively, between simulation and experimentation for the three electromagnetic physics models and three mul-tiple scattering models, for field sizes of 5 × 5 cm2 and 10 × 10 cm2 for depth-dose curves. For dose profile curves, a good agreement of 100% was found between simulation and experimentation for the three electromagnetic physics models, as well as for the three multiple scattering models for a field size of 5 × 5 cm2 at 10 cm and 20 cm depths. For a field size of 10 × 10 cm2, the Penelope model dominated with 98% for 10 cm, along with the three multiple scattering models. The Penelope model and the Standard model, along with the three multiple scattering models, dominated with 100% for 20 cm. Our study, which compared these different GAMOS code models, can be crucial for enhancing the accuracy and quality of radiotherapy, contributing to more effective patient treatment. Our research compares various electro-magnetic physics models and multiple scattering models with experimental measurements, enabling us to choose the models that produce the most reli-able results, thereby directly impacting the quality of simulations. This en-hances confidence in using these models for treatment planning. Our re-search consistently contributes to the progress of Monte Carlo simulation techniques in radiation therapy, enriching the scientific literature.

Evaluation of the Reliability of a System: Approach by Monte Carlo Simulation and Application

The objective of this paper is to evaluate the reliability of a system in its different states (absence of failures, partial failure and total failure) and to propose actions to improve this reliability by an approach based on Monte Carlo simulation. It consists of a probabilistic evaluation based on Markov Chains. In order to achieve this goal, the functionalities of Markov Chains and Monte Carlo simulation steps are deployed. The application is made on a production system. .

Evaluation of Particle Properties of MgO/TiO2 Material by Monte Carlo Simulation Method

The simulation by the Monte Carlo method executed by the software PyPENELOPE proved effective to specify the particle propagation characteristics by calculating the absorption fractions, backscattering and transmission of electrons and secondary photons under the incidence of 0.5 to 20 KeV range of primary electrons. More than 99.9% of the primary electrons were transmitted in the 125 nm thick MgO/TiO2 material at 20 KeV. This occurred because several interactions took place in the transmitted primary irradiation such as characteristic, fluorescence, and bremsstrahlung produced when of the occupation of the KL3, KL2, KM3, and KM2 shell and sub-shell of titanium and magnesium which are the elements with a high atomic number in the material. The transmission particle characteristic of this material is therefore an indicator capable of improving the electrical performance and properties of the sensor.

基于二次电子发射的Monte Carlo仿真模拟

二次电子发射特性在现代先进真空电子器件中有着广泛的应用,准确测量二次电子的产额数据对进一步确定二次电子发射特性至关重要。然而,目前的实验条件无法保证高真空的实验环境和高精准的测量技术,导致得出的实验数据往往存在着不可避免的误差。因此,利用Monte Carlo计算方法模拟电子束与金属固体的相互作用过程已然成为了一种较为实用的解题方法。此方法是一种基于随机模拟的数值计算方法,通过重复随机采样,根据采样结果计算目标量的期望值,进而得到数值解,它在研究电子运输问题上具有极强的解题能量。模型采用经验Mott散射截面公式来快速描述电子的弹性散射过程,利用介电函数模型来描述电子的非弹性散射过程,从而快速计算出二次电子在金属内部的散射运动轨迹,通过定量分析出二次电子发射产额的影响因素。研究发现,二次电子产额随着入射粒子能量的增加而增加,在一定范围内产额随能量增加的趋势比较明显,但达到一定能量后,产额将趋于饱和。此外,入射角度对二次电子的产额分布也有显著影响。

基于Monte Carlo方法的磁驱动准等熵压缩实验不确定度量化评估

磁驱动准等熵压缩实验是研究材料偏离Hugoniot状态高压物性和动力学行为的重要实验技术之一,开展不确定量化评估具有重要意义和价值。基于Monte Carlo原理,结合磁驱动准等熵压缩实验过程分析、Lagrange分析和特征线正向数据处理方法建立了适用于此类实验的Monte Carlo不确定度量化评估方法,实现利用磁驱动准等熵压缩实验获取材料声速、应力、应变等物理量以及状态方程和本构关系等物理模型的不确定度量化评估。利用建立的不确定度评估方法,对文献中已开展的钽、铜和NiTi合金的磁驱动准等熵压缩实验结果进行不确定度量化评估与分析。结果表明,基于本文中方法的评估结果与国外文献以相同原理得到的评估结果一致。对基于CQ-4装置开展的NiTi合金磁驱动准等熵压缩实验的评估结果表明,设计的磁驱动准等熵压缩实验是一种可靠的精密物理实验。在此基础上,深入讨论了磁驱动准等熵压缩实验的误差相关性和敏感性。结果表明:台阶样品厚度和粒子速度的测量是影响实验精度的主要因素。

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

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

GPU-based cross-platform Monte Carlo proton dose calculation engine in the framework of Taichi

In recent years,graphics processing units(GPUs)have been applied to accelerate Monte Carlo(MC)simulations for proton dose calculation in radiotherapy.Nonetheless,current GPU platforms,such as Compute Unified Device Architecture(CUDA)and Open Computing Language(OpenCL),suffer from cross-platform limitation or relatively high programming barrier.However,the Taichi toolkit,which was developed to overcome these difficulties,has been successfully applied to high-performance numerical computations.Based on the class II condensed history simulation scheme with various proton-nucleus interactions,we developed a GPU-accelerated MC engine for proton transport using the Taichi toolkit.Dose distributions in homogeneous and heterogeneous geometries were calculated for 110,160,and 200 MeV protons and were compared with those obtained by full MC simulations using TOPAS.The gamma passing rates were greater than 0.99 and 0.95 with criteria of 2 mm,2%and 1 mm,1%,respectively,in all the benchmark tests.Moreover,the calculation speed was at least 5800 times faster than that of TOPAS,and the number of lines of code was approximately 10 times less than those of CUDA or OpenCL.Our study provides a highly accurate,efficient,and easy-to-use proton dose calculation engine for fast prototyping,beamlet calculation,and education purposes.

THUDosePD:a three-dimensional Monte Carlo platform for phantom dose assessment

Monte Carlo simulations are frequently utilized in radiation dose assessments.However,many researchers find the prevailing computing platforms to be intricate.This highlights a pressing need for a specialized framework for phantom dose evalua-tion.To address this gap,we developed a user-friendly radiation dose assessment platform using the Monte Carlo toolkit,Geant4.The Tsinghua University Phantom Dose(THUDosePD)augments the flexibility of Monte Carlo simulations in dosi-metric research.Originating from THUDose,a code with generic,functional,and application layers,THUDosePD focuses predominantly on anatomical phantom dose assessment.Additionally,it enables medical exposure simulation,intricate geometry creation,and supports both three-dimensional radiation dose analysis and phantom format transformations.The system operates on a multi-threaded parallel CPU architecture,with some modules enhanced for GPU parallel computing.Benchmark tests on the ICRP reference male illustrated the capabilities of THUDosePD in phantom dose assessment,covering the effective dose,three-dimensional dose distribution,and three-dimensional organ dose.We also conducted a voxelization conversion on the polygon mesh phantom,demonstrating the method’s efficiency and consistency.Extended applications based on THUDosePD further underline its broad adaptability.This intuitive,three-dimensional platform stands out as a valuable tool for phantom radiation dosimetry research.

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

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

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

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

Neural network analytic continuation for Monte Carlo:Improvement by statistical errors

This study explores the use of neural network-based analytic continuation to extract spectra from Monte Carlo data.We apply this technique to both synthetic and Monte Carlo-generated data.The training sets for neural networks are carefully synthesized without“data leakage”.We find that the training set should match the input correlation functions in terms of statistical error properties,such as noise level,noise dependence on imaginary time,and imaginary time-displaced correlations.We have developed a systematic method to synthesize such training datasets.Our improved algorithm outperforms the widely used maximum entropy method in highly noisy situations.As an example,our method successfully extracted the dynamic structure factor of the spin-1/2 Heisenberg chain from quantum Monte Carlo simulations.

A refined Monte Carlo code for low-energy electron emission from gold material irradiated with sub-keV electrons

Considering the significance of low-energy electrons(LEEs;0–20 eV) in radiobiology, the sensitization potential of gold nanoparticles(AuNPs) as high-flux LEE emitters when irradiated with sub-keV electrons has been suggested. In this study, a track-structure Monte Carlo simulation code using the dielectric theory was developed to simulate the transport of electrons below 50 keV in gold. In this code, modifications, particularly for elastic scattering, are implemented for a more precise description of the LEE emission in secondary electron emission. This code was validated using the secondary electron yield and backscattering coefficient. To ensure dosimetry accuracy, we further verified the code for energy deposition calculations using the Monte Carlo toolkit, Geant4. The development of this code provides a basis for future studies regarding the role of AuNPs in targeted radionuclide radiotherapy.

Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform

As one type of spatially offset Raman spectroscopy(SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light–tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo e Xtreme(MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.

Research Progress of Reverse Monte Carlo and Its Application in Josephson Junction Barrier Layer

As indispensable components of superconducting circuit-based quantum computers,Josephson junctions determine how well superconducting qubits perform.Reverse Monte Carlo(RMC)can be used to recreate Josephson junction’s atomic structure based on experimental data,and the impact of the structure on junctions’properties can be investigated by combining different analysis techniques.In order to build a physical model of the atomic structure and then analyze the factors that affect its performance,this paper briefly reviews the development and evolution of the RMC algorithm.It also summarizes the modeling process and structural feature analysis of the Josephson junction in combination with different feature extraction techniques for electrical characterization devices.Additionally,the obstacles and potential directions of Josephson junction modeling,which serves as the theoretical foundation for the production of superconducting quantum devices at the atomic level,are discussed.

基于神经网络-Monte Carlo法的结构系统可靠性分析

针对传统结构可靠性求解方法存在应用局限性和计算误差较大的缺陷,提出了一种基于神经网络-Monte Carlo的结构系统可靠性分析方法,利用神经网络模型预测方法降低Monte Carlo法计算结构可靠性指标的工作量,并与传统可靠性分析方法对比,验证了该方法对结构系统可靠性分析的精度要求及计算效率。结果表明,十五层框架结构系统可靠性分析方法比Monte Carlo法直接计算的失效概率降低了0.03%,且计算工作量减少为1%,该方法的计算结果满足精度要求且计算效率大幅提升。

基于BRICH聚类和Copula-Monte Carlo模拟的光伏发电功率概率预测

光伏发电功率受气象的混沌特性影响,其随机性、波动性、间歇性对电力系统的运行影响较严重。针对原始光伏发电数据维度大及发电功率易受天气状况影响的问题,提出了基于主成分分析和BRICH聚类的数据处理方法,从而降低模型输入变量维度,易于统计建模;其次,搭建了基于Copula-Monte Carlo的光伏发电功率概率预测模型,计算光伏出力在给定未来点预测值的情况下实际出力的概率区间预测,并基于区间覆盖率、预测区间平均宽度对模型进行评价。最后,以实际光伏电站夏季数据为例进行验证分析。结果表明,Copula-Monte Carlo方法可直观展示光伏发电功率的波动区间和期望值,且优于其他功率预测模型。