针对双聚焦系统中的电分析器,利用CST Studio Suite进行了结构设计与电磁场仿真。主要工作包括理论计算、柱面结构电分析器设计、弥散电场优化与屏蔽、电-磁分析系统电磁场及粒子运动轨迹模拟。研究结果表明:接地极与绝缘垫片的安装能...针对双聚焦系统中的电分析器,利用CST Studio Suite进行了结构设计与电磁场仿真。主要工作包括理论计算、柱面结构电分析器设计、弥散电场优化与屏蔽、电-磁分析系统电磁场及粒子运动轨迹模拟。研究结果表明:接地极与绝缘垫片的安装能有效降低弥散场对粒子运动状态影响;束流聚焦点与理论计算相对偏差小于1%;在紧凑、易安装结构下,在电、磁分析系统束流引出口分别实现能量聚焦及速度聚焦,且放大系数小于1。展开更多
利用CST studio suite程序对边耦合直线加速结构的加速腔及耦合腔进行建模仿真,并进行优化。加速腔结构由11个参数决定,优化过程需平衡多个参数,包括本征模式、高频特性、最大表面电场、有效分路阻抗及渡越时间因子;耦合腔的优化基于维...利用CST studio suite程序对边耦合直线加速结构的加速腔及耦合腔进行建模仿真,并进行优化。加速腔结构由11个参数决定,优化过程需平衡多个参数,包括本征模式、高频特性、最大表面电场、有效分路阻抗及渡越时间因子;耦合腔的优化基于维持本征模式3 GHz。结果表明,在26~120 MeV整个加速过程中,加速腔的有效分路阻抗为22.5~59.8 MΩ·m^(-1),加速梯度为11~14.7 MV·m^(-1)。展开更多
This paper develops a novel event-triggered optimal control approach based on state observer and neural network(NN)for nonlinear continuous-time systems.Firstly,the authors propose an online algorithm with critic and ...This paper develops a novel event-triggered optimal control approach based on state observer and neural network(NN)for nonlinear continuous-time systems.Firstly,the authors propose an online algorithm with critic and actor NNs to solve the optimal control problem and provide an event-triggered method to reduce communication and computation burdens.Moreover,the authors design weight estimation for critic and actor NNs based on gradient descent method and achieve uniformly ultimate boundednesss(UUB)estimation results.Furthermore,by using bounded NN weight estimation and dead-zone operator,the authors propose a triggering condition,prove the asymptotic stability of closed-loop system from Lyapunov stability perspective,and exclude the Zeno behavior.Finally,the authors provide a numerical example to illustrate the effectiveness of the proposed method.展开更多
文摘针对双聚焦系统中的电分析器,利用CST Studio Suite进行了结构设计与电磁场仿真。主要工作包括理论计算、柱面结构电分析器设计、弥散电场优化与屏蔽、电-磁分析系统电磁场及粒子运动轨迹模拟。研究结果表明:接地极与绝缘垫片的安装能有效降低弥散场对粒子运动状态影响;束流聚焦点与理论计算相对偏差小于1%;在紧凑、易安装结构下,在电、磁分析系统束流引出口分别实现能量聚焦及速度聚焦,且放大系数小于1。
基金supported by the National Natural Science Foundation of China under Grant Nos.61973002,62103003the Anhui Provincial Natural Science Foundation under Grant No.2008085J32+2 种基金the National Postdoctoral Program for Innovative Talents under Grant No.BX20180346the General Financial Grant from the China Postdoctoral Science Foundation under Grant No.2019M660834the Excellent Young Talents Program in Universities of Anhui Province under Grant No.gxyq2019002.
文摘This paper develops a novel event-triggered optimal control approach based on state observer and neural network(NN)for nonlinear continuous-time systems.Firstly,the authors propose an online algorithm with critic and actor NNs to solve the optimal control problem and provide an event-triggered method to reduce communication and computation burdens.Moreover,the authors design weight estimation for critic and actor NNs based on gradient descent method and achieve uniformly ultimate boundednesss(UUB)estimation results.Furthermore,by using bounded NN weight estimation and dead-zone operator,the authors propose a triggering condition,prove the asymptotic stability of closed-loop system from Lyapunov stability perspective,and exclude the Zeno behavior.Finally,the authors provide a numerical example to illustrate the effectiveness of the proposed method.
