I am Linlin Zhong, an Associate Professor at the School of Electrical Engineering, Southeast University. My research interests are directed toward the interdisciplinary study of Plasma Physics, Electrical Engineering and Computer Science, including
Plasma Basic Data,
Multiphysics Modelling, and
Artificial Intelligence in Plasma Physics and Electrical Engineering.
Welcome any students who have strong interests in High Voltage Engineering, Plasma Engineering, and Artificial Intelligence to visit my lab and start your magical journey 😄.
PhD in Plasma Engineering, 2017
Université Toulouse III - Paul Sabatier
PhD in Electrical Engineering, 2017
Xi'an Jiaotong University
BSc in Electrical Engineering, 2012
Xi'an Jiaotong University
Aug. 4, 2021: We published a paper on the database of electron-impact ionization cross sections. This is an open database with tabular fitting parameters and an online web system. Currently there are 88 species composed of H, C, N, O, and F in this database. The same methods and basis set are used to ensure consistent ionization cross sections, which could provide plasma community self-consistent ionization cross sections in plasma modeling. All three reviewers highly rated this work and database, saying that “This database is instrumental and useful to researchers”, “This database would be useful for many applications, including plasma modeling”, and “The fitting parameters can help speed up the computation of ionization cross section within a plasma physics simulation.“. This paper is now available on Physics of Plasmas.
May. 10, 2021: We published a paper on the numerical study of graphite production in two-temperature non-LTE plasmas of C4F7N and C5F10O mixed with CO2, N2, and O2. This is part of the serial works on eco-friendly SF6 replacements. The novelty of this work is that we investigate the departure of thermodynamic equilibrium and the corresponding effects on the graphite production in the plasmas of C4F7N, C5F10O, and their mixtures with CO2, N2, and O2. The results show that neglecting the nonequilibrium effect can lead to a very inaccurate description of graphite condensation in the above plasmas. This paper is now available on Plasma Processes and Polymers.
Jul. 30, 2020: We published a paper on deep learning for thermal plasma simulation. In this paper we propose a deep learning method for solving the partial differential equations in thermal plasma models by constructing a deep feed-forward neural network to surrogate the solution of the model. This could bring us a new and prospective numerical tool for plasma modeling. The innovation of this work is highly rated by the reviewers. This paper is now available on Computer Physics Communications.
Feb. 06, 2020: We published two serial papers on compositions of two-temperature (2T) plasmas. In these two papers we compare two kinds of methods for calculating 2T plasma composition: the mass action law methods and extremum searching methods. The former methods include the two described by Potapov and van de Sanden et al mass action laws, respectively. The latter methods include those of searching minimum Gibbs free energy and maximum entropy of a plasma system respectively. The entropy maximization method is first reported in this work and has the same power as the commonly used Gibbs free energy minimization method. We demonstrate both mathematically and numerically that the method of 2T Gibbs free minimization is completely the same as the 2T Potapov mass action law, and the method of 2T entropy maximization is exactly consistent with the 2T van de Sanden et al mass action law if an assumption is used. We also extend these methods to the calculation of 2T multi-phase plasma composition. The part I and part II of the serial papers are now available on Journal of Physics D: Applied Physics and Journal of Physics D: Applied Physics respectively.
Nov. 15, 2019: We published a paper on the electron-impact ionization cross sections of new SF6 replacements, including C2F4H2 (R134), C3F4H2 (HFO1234ze), C4F8, C4F7N, C5F10O, and C6F12O. In this work we propose a new method of combining the Deutsch-Märk (DM) formalism at low electron energy and the Binary-Encounter-Bethe (BEB) formalism at high electron energy by using a dual sigmoid function. The Editors felt that this article is noteworthy, and have chosen it to be promoted as an Editor’s Pick. This paper is now available on Journal of Applied Physics.
Oct. 15, 2019: We published a paper on an improved method for fast evaluating arc quenching performance of a gas based on 1D arc decaying model. Compared to the previous method, the present method is improved mainly in the three aspects: the thermal recovery stage is featured by the average radial temperature instead of the axial temperature; the criterion of dividing the dielectric recovery stage into the pre- and postdielectric recovery stages is validated by the average electron number density instead of choosing arbitrarily; and the postdielectric recovery stage is characterized by the critical electric field strength Ecr instead of the reduced critical electric field strength (E/N)cr. This paper is now available on Physics of Plasmas.
Sep. 25, 2019: We published a paper on the calculation of plasma properties and evaluation of arc decaying characteristics for new eco-friendly gas C4F7N mixed with N2 and CO2. The plasma properties include compositions, thermodynamic properties, transport coefficients, and net emission coefficients. The arc decaying characteristics were determined based on the 1-D arc decaying model. The results could be helpful to the selection of SF6 new eco-friendly replacements as arc quenching medium. This paper is now available on Plasma Chemistry and Plasma Processing.
Aug. 25 to Aug. 28, 2019: I attended 19th National Conference on Plasma Science and Technology held in Dalian where I gave a presentation on the calculation of multi-phase composition of 2T plasmas.
Aug. 21 to Aug. 23, 2019: I attended 2019 Annual Academic Conference of High Voltage Technical Committee of Chinese Society for Electrical Engineering (CSEE) held in Shenyang where I gave a presentation on the fast evaluation of arc quenching ability for a gas based on 1-D arc decaying model. During this conference, I was honored a Excellent Paper Award.
Aug. 21, 2019: I was invited by the China Electrotechnical Society to give a report in the youth salon held in Beijing. The topic of the report is the application of artifical intelligence to predict molecular ionization cross sections.
Jul. 28 to Jul. 31, 2019: I attended 2019 1st International Symposium on Insulation and Discharge Computation for Power Equipment held in Xi’an where I gave a presentation on the machine learning based model for predicting molecular ionization cross sections.
May 09, 2019: We published a paper on fast prediction of electron-impact ionization cross sections of large molecules via machine learning. In this work we propose a machine learning based method to construct a model for predicting Qion of large molecules without the high-cost ab initio calculation. The reviewers highly rated this work as a very interesting idea and very innovative method. This paper is now available on Journal of Applied Physics.
Apr. 03, 2019: We published a paper on evaluation of arc quenching ability for a gas by combining 1-D hydrokinetic modeling and Boltzmann equation analysis. This is a new, time-saving and easy used method for evaluating arc quenching performance. The work was cooperated with Prof. Yann Cressault and Prof. Philippe Teulet from Laboratoire Plasma et Conversion d’Energie (LAPLACE) in France. This paper is now available on IEEE Transactions on Plasma Science.
To calculate basic data of various plasmas, including interaction potential, collision integrals, cross sections, particle compositions, thermodynamic properties, transport coefficients, diffusion coefficients, radiation coefficients, et al.
To perform magnetohydrodynamic (MHD) simulation of thermal plasmas e.g. high-pressure arc plasma in circuit breakers using finite volume method (FVM)
To evaluate the insulating and arc quenching ability of eco-friendly SF6 replacements
Deep learning is a powerful non-linear mapping tool which can express very complex non-linear relationships. We propose a novel method for solving the governing equations in plasma modeling by constructing deep neural networks to surrogate the solution of plasma models. This could bring us a new and prospective numerical tool for plasma modeling.
To apply AI technology or machine learning methods to predict condition of power apparatus or recognize abnormal objects in transmission lines from the videos captured by unmanned aerial vehicles (UAV)
I am a lecturer for the following courses at Southeast University
High Voltage and Insulation Technology
High Voltage Theory Applications and Development
Excellent Paper Award, 2019 Annual Academic Conference of High Voltage Technical Committee of Chinese Society for Electrical Engineering (CSEE).
Excellent Peer Reviewer, Journal of Global Energy Interconnection.
Outstanding Oral Report Award, 19th Asian Conference on Electrical Discharge.
Outstanding Oral Report Award, 2018 National Conference on High Voltage and Discharge Plasmas.
Outstanding Student Pacemaker, Xi’an Jiaotong University.
Outstanding Student Leader, Xi’an Jiaotong University.
Outstanding Graduates, Xi’an Jiaotong University.
National Scholarship for PhD Students, Ministry of Education, China.
China Scholarship Council (CSC) for joint-PhD Students, China Scholarship Council, China
Voith China Scholarship, Xi’an Jiaotong University, China.
National Encouragement Scholarship, Xi’an Jiaotong University, China.
Pengkang Scholarship, Xi’an Jiaotong University, China.
Meritorious Winnerin American Mathematical Contest In Modeling (MCM)
Second Prizein Chinese National Mathematical Contest in Modeling
Third Prizein Shaanxi Advanced Mathematics Contest
Second Prizein ACM Programming Contest