{"title":"A Study on the Eigen Properties of the Coaxial Waveguide of Cylindrical Form","authors":"","doi":"10.14738/tecs.115.15656","DOIUrl":null,"url":null,"abstract":"In this study, the eigen properties of the coaxial waveguide of cylindrical form is investigated by using finite element method. The eigenmatrix equation constructed from the Helmholtz vector equation is too large to derive the results using a personal computer. Therefore, the eigen equations are compressed using the Arnoldi algorithm and after that the results are derived using the Krylov-Schur iteration method. The similarity transformation matrix used during this process contains the desired eigenmode pair. The eigenmodes are simultaneously included in the column matrix components of the transform matrix. These are represented with the pairs of the electric field and electric potential. The eigenmodes have been divided into two classes: transverse magnetic modes and transverse electric modes. As results, in order to more clearly reveal the characteristics of the eigenmodes, these results are shown in the figure.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Machine Learning and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14738/tecs.115.15656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, the eigen properties of the coaxial waveguide of cylindrical form is investigated by using finite element method. The eigenmatrix equation constructed from the Helmholtz vector equation is too large to derive the results using a personal computer. Therefore, the eigen equations are compressed using the Arnoldi algorithm and after that the results are derived using the Krylov-Schur iteration method. The similarity transformation matrix used during this process contains the desired eigenmode pair. The eigenmodes are simultaneously included in the column matrix components of the transform matrix. These are represented with the pairs of the electric field and electric potential. The eigenmodes have been divided into two classes: transverse magnetic modes and transverse electric modes. As results, in order to more clearly reveal the characteristics of the eigenmodes, these results are shown in the figure.