Description
Leader of the group Multiscale Dynamics: Ute Ebert.
Nature and technology are full of dynamics, often involving multiple scales in length, time and energy. To model these processes, we combine scientific computing with model reduction and machine learning, with particular focus on plasma dynamics in lightning and space weather, and in high voltage and plasma technology.
Our research addresses questions in nature such as start and propagation of lightning strokes, terrestrial gamma-ray flashes and space weather, and closely related technological problems such as switch gear for long-distance electricity nets, air purification and disinfection with corona reactors, and protection of satellites and electricity nets from space weather.
Within national and European projects, we collaborate with colleagues in applied plasma physics, electrical and mechanical engineering, atmospheric electricity, and cosmic particle and space science, and with non-academic partners such as ABB, DNV-GL, ESA-ESTEC and NASA.
Find more about our work (including publications) on the personal homepages of the staff scientists:
- Ute Ebert (group leader)
- Enrico Camporeale
- Jannis Teunissen
and on the page with our numerical codes for Multiscale Plasma Dynamics.
View a photo of the Multiscale Dynamics group in January 2020 and in January 2018.
Vacancies
No vacancies currently.
News
Nature: AI for understanding extreme physics
Researchers of the University of Oxford, CWI and other organizations published a paper in Nature reviewing the use of artificial intelligence and machine learning for understanding 'extreme physics' – the physics of matter at extremely high temperatures and densities.
CWI and INRIA use AI to better predict harmful solar storms
Extreme solar storms can have destructive effects on communications and electrical power grids. To improve space weather forecasting systems Mandar Chandorkar combined AI and data from space missions. He defends his thesis on 14 November.
Plasma for Plants technology project granted
Recently researchers from CWI and TU/e were awarded an OTP technology grant to understand the effect of plasma on plants.
CWI joins ESCAPE project to build European Open Science Cloud
CWI will participate in the ESCAPE project, which is part of the effort to build a European Open Science Cloud (EOSC), which will allow universal access to scientific data through a single online platform.
Members
Associated Members
Publications
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Bouwman, D.D, Martinez, A, Braams, B.J, & Ebert, U. (2021). Neutral dissociation of methane by electron impact and a complete and consistent cross section set. Plasma Sources Science and Technology, 30(7). doi:10.1088/1361-6595/ac0b2b
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Abbas, M.F, Sun, G.Y, Yuan, X.C, Li, X, Zhang, X, Sun, A.B, & Zhang, G.J. (2021). The influence of O2 on positive streamer initiation in supercritical CO2 with field ionization using a 3-D particle model. IEEE Transactions on Plasma Science, 49(6), 1786–1793. doi:10.1109/TPS.2021.3080038
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Li, W, Zhao, D, He, C, Shen, Y, Hu, A, & Zhang, K. (2021). Application of a multi-layer artificial neural network in a 3-D global electron density model using the long-term observations of COSMIC, Fengyun-3C, and Digisonde. Space Weather, 19(3). doi:10.1029/2020SW002605
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Hu, A, Sisti, M, Finelli, F, Califano, F, Dargent, J, Faganello, M, … Teunissen, H.J. (2020). Identifying magnetic reconnection in 2D Hybrid Vlasov Maxwell simulations with Convolutional Neural Networks. Astrophysical Journal, 900(1). doi:10.3847/1538-4357/aba527
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Wang, L, He, C, Hu, A, Zhao, D, Shen, Y, & Zhang, K. (2020). A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network. Advances in Space Research. doi:10.1016/j.asr.2020.07.032
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Li, X, Sun, A.B, & Teunissen, H.J. (2020). A computational study of negative surface discharges: Characteristics of surface streamers and surface charges. IEEE Transactions on Dielectrics and Electrical Insulation, 27(4), 1178–1186. doi:10.1109/TDEI.2020.008880
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Li, X, Sun, A.B, Zhang, G.J, & Teunissen, H.J. (2020). A computational study of positive streamers interacting with dielectrics. Plasma Sources Science and Technology, 29(6). doi:10.1088/1361-6595/ab8f75
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Chandorkar, M.H, Camporeale, E, Furtlehner, C, Poduval, B, & Sebag, M. (2020). Dynamic Time-Lag Regression: Predicting what & when. In Eighth International Conference on Learning Representations.
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Trinh, T.N.G, Scholten, O, Buitink, S, Ebert, U, Hare, B.M, Krehbiel, P.R, … Winchen, T. (2020). Determining electric fields in thunderclouds with the radiotelescope LOFAR. Journal of Geophysical Research: Atmospheres, 125(8). doi:10.1029/2019JD031433
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Li, Y, Dijcks, S, Sun, G, Wen, J, Xu, Y. (), Y, Zhang, G.J, … Nijdam, S. (2020). Characterizing streamer branching in N2-O2 mixtures by 2D peak-finding. Plasma Sources Science and Technology, 29(3). doi:10.1088/1361-6595/ab73de
Software
Afivo: A framework for adaptive mesh refinement with geometric multigrid methods
Afivo, short for Adaptive Finite Volume Octree, is a framework for adaptive mesh refinement on quadtree (2D) and octree (3D) meshes with shared memory parallelization (OpenMP) and geometric multigrid methods.
PumpKin: Analyzing complex chemistry models
PumpKin, short for Pathway Reduction Method for Plasma Kinetic Models, is a tool for post-processing results from zero-dimensional plasma kinetics solvers.
Current projects with external funding
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Making plasma-assisted combustion efficient ()
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Artificial Intelligence Data Analysis (AIDA)
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European Science Cluster of Astronomy & Particle physics ESFRI research infrastructures (ESCAPE)
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Let CO2 spark! Understand breakdown dynamics for high voltage technology and lightning Abstract Sparks, (Let CO2 spark!)
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Plasma for Plants: Towards controlled and efficient plasma-activated water generation for a cleaner environment (Plasma for Plants)
Related partners
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CNRS
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Universita di Pisa
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Katholieke Universiteit Leuven
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Technische Universiteit Eindhoven