期刊文献+
共找到3篇文章
< 1 >
每页显示 20 50 100
PTC: Full and Drift Particle Orbit Tracing Code for α Particles in Tokamak Plasmas
1
作者 Feng Wang Rui Zhao +4 位作者 Zheng-Xiong Wang Yue Zhang Zhan-Hong Lin Shi-Jie Liu CFETR Team 《Chinese Physics Letters》 SCIE CAS CSCD 2021年第5期36-40,共5页
Fusion born α particle confinement is one of the most important issues in burning plasmas,such as ITER and CFETR.However,it is extremely complex due to the nonequilibrium characteristics,and multiple temporal and spa... Fusion born α particle confinement is one of the most important issues in burning plasmas,such as ITER and CFETR.However,it is extremely complex due to the nonequilibrium characteristics,and multiple temporal and spatial scales coupling with background plasma.A numerical code using particle orbit tracing method(PTC)has been developed to study energetic particle confinement in tokamak plasmas.Both full orbit and drift orbit solvers are implemented to analyze the Larmor radius effects on α particle confinement.The elastic collisions between alpha particles and thermal plasma are calculated by a Monte Carlo method.A triangle mesh in poloidal section is generated for electromagnetic fields expression.Benchmark between PTC and ORBIT has been accomplished for verification.For CFETR burning plasmas,PTC code is used for α particle source and slowing down process calculation in 2D equilibrium.In future work,3D field like toroidal field ripples,Alfven and magnetohydrodynamics instabilities perturbation inducing α particle transport will be analyzed. 展开更多
关键词 CFETR Full and Drift particle Orbit Tracing Code for particles in Tokamak Plasmas PTC Tokamak
在线阅读 下载PDF
Calibration and uniqueness analysis of microparameters for DEM cohesive granular material 被引量:4
2
作者 Songtao Ji Jurij Karlovšek 《International Journal of Mining Science and Technology》 SCIE EI CAS CSCD 2022年第1期121-136,共16页
The differential evolution(DE)algorithm was deployed to calibrate microparameters of the DEM cohesive granular material.4 macroparameters,namely,uniaxial compressive strength,direct tensile strength,Young’s modulus a... The differential evolution(DE)algorithm was deployed to calibrate microparameters of the DEM cohesive granular material.4 macroparameters,namely,uniaxial compressive strength,direct tensile strength,Young’s modulus and Poisson’s ratio,can be calibrated to high accuracy.The best calibration accuracy could reach the sum of relative errors RE_(sum)<0.1%.Most calibrations can be achieved with RE_(sum)<5%within hours or RE_(sum)<1%within 2 days.Based on the calibrated results,microparameters uniqueness analysis was carried out to reveal the correlation between microparameters and the macroscopic mechanical behaviour of material:(1)microparameters effective modulus,tensile strength and normal-to-shear stiffness ratio control the elastic behaviour and stable crack growth,(2)microparameters cohesion and friction angles present a negative linear correlation that controls the axial strain and lateral strain prior to the peak stress,and(3)microparameters friction coefficient controls shear crack friction and slip mainly refers to the unstable crack behaviour.Consideration of more macroparameters to regulate the material mechanical behaviour that is dominated by shear crack and slip motion is highlighted for future study.The DE calibration method is expected to serve as an alternative method to calibrate the DEM cohesive granular material to its peak strength. 展开更多
关键词 Discrete element method(DEM) particle flow code(PFC) Differential evolution(DE) Parameter calibration Uniqueness analysis Post-peak behaviour
在线阅读 下载PDF
Fast determination of meso-level mechanical parameters of PFC models 被引量:4
3
作者 Guo Jianwei Xu Guoan +1 位作者 Jing Hongwen Kuang Tiejun 《International Journal of Mining Science and Technology》 SCIE EI 2013年第1期157-162,共6页
To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal test... To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models. 展开更多
关键词 particle flow code Meso-level mechanical parameter Macroscopic property Orthogonal test Intelligent prediction
在线阅读 下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部