Modeling and calculation of electron beam diffusion based on adaptive Simpson integral

The diffusive behaviour of relativistic electron beams propagating is a central issue in high-energy physics research and the design of high-energy physics devices. Traditional numerical integration methods have the defects of low accuracy and insufficient efficiency in calculating the initial diffusion integral. Aiming at the accuracy and efficiency of the diffusion prediction of the beam spot radius in relativistic electron beam transport, a high-precision computational framework based on adaptive Simpson's integral is proposed to improve the shortcomings of the traditional Alfvén current theory that relies on the rectangular integration method. In this paper, by embedding the adaptive Simpson integration algorithm into the Alfvén theoretical model, the high-precision and high-efficiency solution of the diffusion parameter of the electron beam propagation radius is achieved. Numerical experiments show that compared with the conventional method, the error of the adaptive Simpson integral in the electron beam diffusion integral is reduced to the order of 10-7, the accuracy is improved by two orders of magnitude, and the computational time consumed is reduced to 9.06%. The analysis further shows that the increase in electron energy enhances the focusing effect of the magnetic field, suppresses the spread of the beam radius.

Authors

Institutions

Publication Details

Journal
IET conference proceedings.
Published
2025-10-13
DOI
https://doi.org/10.1049/icp.2025.2846
Primary Topic
Near-Field Optical Microscopy
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling and calculation of electron beam diffusion based on adaptive Simpson integral

Leran Chen, Zhigang Deng, Xiaojin Zhu, Hesheng Zhang et al.
IET conference proceedings.
Near-Field Optical Microscopy
article

Modeling and calculation of electron beam diffusion based on adaptive Simpson integral

Leran Chen, Zhigang Deng, Xiaojin Zhu, Hesheng Zhang, Chaoyang Chen
article en

Abstract

The diffusive behaviour of relativistic electron beams propagating is a central issue in high-energy physics research and the design of high-energy physics devices. Traditional numerical integration methods have the defects of low accuracy and insufficient efficiency in calculating the initial diffusion integral. Aiming at the accuracy and efficiency of the diffusion prediction of the beam spot radius in relativistic electron beam transport, a high-precision computational framework based on adaptive Simpson's integral is proposed to improve the shortcomings of the traditional Alfvén current theory that relies on the rectangular integration method. In this paper, by embedding the adaptive Simpson integration algorithm into the Alfvén theoretical model, the high-precision and high-efficiency solution of the diffusion parameter of the electron beam propagation radius is achieved. Numerical experiments show that compared with the conventional method, the error of the adaptive Simpson integral in the electron beam diffusion integral is reduced to the order of 10-7, the accuracy is improved by two orders of magnitude, and the computational time consumed is reduced to 9.06%. The analysis further shows that the increase in electron energy enhances the focusing effect of the magnetic field, suppresses the spread of the beam radius.

IET conference proceedings.Vol. 2025(23)
Shanghai University (CN)
Openalex Percentile: Top 72%
Near-Field Optical Microscopy
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.