Smarte Datentechnologien
In dem Forschungsschwerpunkt Smarte Datentechnologien beschäftigen wir uns mit der Erforschung und Entwicklung von Modellen, Methoden und digitalen Werkzeugen für Simulationen und Datenprozesse. In einer Zeit, in der digitale Zwillinge und Daten eine immer zentralere Rolle in der Wissenschaft und Technologie spielen, nutzen wir die Möglichkeiten, die uns moderne Technologien bieten.
Der Fokus liegt dabei auf multiphysikalischen Simulationen und der Integration von vielfältigen Datenquellen für eine ganzheitliche Herangehensweise. Dies beinhaltet auch den Einsatz von künstlicher Intelligenz für die Erstellung effizienter Vorhersagemodelle und die Kopplung von Modellen und Messdaten für multiphysikalische Problemstellungen. Damit schaffen wir neue Möglichkeiten für die Modellierung, datengestützte Simulation und Analyse von Plasmen und Plasmaprozessen sowie das Daten- und Informationsmanagement.
Ein Alleinstellungsmerkmal unseres Forschungsschwerpunkts ist die Zusammenführung von Daten, Metadaten und Fachinformationen für datengestützte Forschung und Entwicklung auf Grundlage moderner Forschungsdatenmanagement-Lösungen für die Plasmaforschung.
Abteilungen des Forschungsschwerpunkts:
Die Kombination von Modellierung, Datenintegration und datenwissenschaftlichen Methoden liefert Informationen und Daten über die Untersuchungsobjekte aus verschiedenen Blickwinkeln. Dadurch wird der Aufwand zur Erlangung eines ganzheitlichen Prozessverständnisses für Plasmaanwendungen und andere Technologien wesentlich reduziert und es werden neue Einblicke in komplexe Vorgänge ermöglicht.
Anwendungs- und Forschungsfelder
Im Bereich der thermischen Plasmen liegt unser Fokus gegenwärtig auf Plasmaspritzprozessen und Untersuchungen zu kleinskaligen Bogenplasmen. Dabei steht einerseits die Optimierung von Anwendungen im Vordergrund, andererseits werden auch fundamentale Untersuchungen durchgeführt, wie etwa zur Wechselwirkung von Plasmen mit den Elektroden. Die bei uns entwickelten Modelle ermöglichen eine konsistente Nichtgleichgewichtsbeschreibung von Plasma und Randschicht in thermischen Plasmen.

M Baeva et al 2022 J. Phys. D: Appl. Phys. 55 365202, https://doi.org/10.1088/1361-6463/ac791c
M Baeva et al 2021 J. Therm. Spray Tech. 30 1737–1750, https://doi.org/10.1007/s11666-021-01261-4
Untersuchungen zu nicht-thermischen Plasmen erstrecken sich bei uns vordergründig über Barrierenentladungen, Plasmaionenquellen und Plasma-Jets. Im Vordergrund stehen Studien zur Analyse von plasmachemischen Prozessen, zur Interaktion von Plasmen mit Oberflächen und zur Optimierung von Plasmaquellen für unterschiedliche Anwendungen. Unsere Modelle ermöglichen eine zeitabhängige und räumlich mehrdimensionale Beschreibung und detaillierte Analyse plasmaphysikalischer und reaktionskinetischer Effekte.

A Jovanović et al 2023 Plasma Sources Sci. Technol. 32 055011, https://doi.org/10.1088/1361-6595/acd2fc

Durch Gasentladungen induzierte Reaktionsmechanismen haben ein großes technologisches Nutzungspotenzial, etwa für die Aufspaltung von Kohlendioxid und die Bildung von grünem Wasserstoff. Die bei uns verfügbaren Modelle zur Analyse von plasmachemischen Prozessen und Reaktionspfaden werden zur Optimierung von Plasmaanwendungen und zur Erschließung neuer Technologiefelder eingesetzt. Dabei können sowohl thermische als auch nicht-thermische Plasmen zur Auslösung der Reaktionsmechanismen zum Einsatz kommen.
Am INP widmen wir uns dem Daten- und Wissensmanagement, um die Forschung und Entwicklung in der Plasmaforschung mittels moderner Methoden voranzubringen und den Einsatz datengestützter Methoden zu unterstützen. Wir entwickeln effektive Werkzeuge und Methoden für die strukturierte Erfassung, Dokumentation und Wiederverwendung von Forschungsdaten. Unser Ziel ist es, die FAIR-Prinzipien – das heißt, Forschungsergebnisse sollen Findable (auffindbar), Accessible (zugänglich), Interoperable (interoperabel) und Reusable (wiederverwendbar) sein – in die tägliche Praxis zu integrieren. Durch den Einsatz moderner Technologien und Datenmanagementlösungen ermöglichen wir eine effiziente Analyse und Nutzung von Daten und Fachinformationen, was entscheidend für die Weiterentwicklung von datengestützten Methoden und innovativen Plasmaanwendungen ist. Zur Veröffentlichung und Nachnutzung unserer Plasmaquellen, Patente und zugehöriger Forschungsdaten betreiben wir die INterdisziplinäre PlasmaTechnologie-DATenplattform INPDAT.

R Anirudh et al 2023 IEEE Trans. Plasma Sci. 51 1750, https://doi.org/10.1109/TPS.2023.3268170
S Franke et al 2020 Sci. Data 7 439, https://doi.org/10.1038/s41597-020-00771-0
Wir nutzen künstliche Intelligenz (KI) und maschinelles Lernen, um die Forschung und Entwicklung im Bereich Plasma und technologische Prozesse voranzutreiben. Durch die Entwicklung intelligenter Modelle und Algorithmen verbessern wir die Effizienz von Simulationen, schaffen neue Simulationsansätze, die Daten und Modelle integrieren, und unterstützen die Datenanalyse. Dies ist entscheidend, um komplexe physikalische Phänomene simulieren und besser verstehen zu können.

I Chaerony Siffa et al. 2024 Mach. Learn.: Sci. Technol. 5, 025031, https://doi.org/10.1088/2632-2153/ad4230
Projektthemen

The project is aimed at the construction of a robust solver for elliptic partial differential equations on adaptive unstructured meshes. The project combines different approaches such as domain decomposition techniques and the use of advanced elliptic solvers such as the hierarchical Poincaré-Steklov (HPS) scheme. In addition, the algorithms for operating with non-conformal triangular meshes are developed. The developed solver is intended to be applied for simulating different types of atmospheric pressure discharges such as pin-to-pin, pin-to-plane discharges and non-equilibrium atmospheric pressure plasma jets.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 504701852, https://gepris.dfg.de/gepris/projekt/504701852
Non-thermal plasmas are utilised across various applications due to their effective generation of reactive species through transient discharges, with dielectric barrier discharge (DBD) being a common method for plasma generation. The interaction between plasma and surface is crucial, making it essential to thoroughly understand how dielectrics influence plasma properties for the enhancement of applications in fields such as medicine, agriculture, and air purification. This project proposes a novel approach to data-driven diagnostics by integrating experimental and simulation data into a unified database, enabling systematic exploration of complex surface effects to address critical research questions. The database ensures consistent data formats for all measured and calculated discharge parameters, facilitating direct linking between experimental and simulation data. Through systematic data analysis, the project investigates how individual surface effects influence various discharge phases of the DBD, exploring their potential for optimising plasma properties alongside established volume effects. The data and metadata formats developed will contribute to enhancing the accessibility and reusability of research data, addressing a prominent challenge within the scientific community.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 535827833, https://gepris.dfg.de/gepris/projekt/535827833.

Analysis of mechanisms of ambient-pressure PECVD (plasma-enhanced chemical vapour deposition) using DBDs with short gas-residence times, particularly single-filament DBDs as well as plasma-sheet DBDs, in mixtures of argon with addition of small amounts of different molecular gases including HMDS and TMS as well as hydrocarbons (HCs), such as CH4, C2H2, C2H4 and C2H6. The main topics of the project include: role of ions in DBD-based PECVD from Ar-monomer mixtures; study of thin films grown by DBD-PECVD, i.e. composition, structure, properties; development of complex plasma-chemical models for DBDs in Ar with admixture of HCs or Si-containing precursors; development and execution of 1d-t or 2d-t simulations.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 504701852, https://gepris.dfg.de/gepris/projekt/504701852.
Partners: Institute for Surface Technology, Technische Universität Braunschweig

In spite of many efforts, open access for scientific publications has still not become fully established. One major reason for this is presumably that researchers in cutting-edge research often do not directly experience the immediate benefits of open access publication: access to paid literature is not a major obstacle for members of many research groups. However, since the open access transformation can only succeed with researchers, the Mehr-OA project relies on their intrinsic motivation by presenting the benefits of open access for their own research based on a specific use case: Researchers want to get answers to research questions, receive overviews of the state of research in a particular field, and obtain evaluations and analyses of the considerable number of publications. The Mehr-OA project is making a significant contribution to this by developing methods for efficient machine-based search and post-processing of open access content and testing them using the case of plasma research. To achieve this, modern machine learning methods are used to automatically extract content from centrally stored open access publications in the subject repository for natural sciences and technology (RENATE) and make it publicly available in structured form in the Open Research Knowledge Graph (ORKG).
Funded by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF), project numbers 16KOA013A, 16KOA013B, https://www.plasma-mds.org/project-mehr-oa.html.
Partners: TIB – Leibniz Information Centre for Science and Technoloy

In recent years, low-temperature plasmas have found wide technological applications, such as chemical processing and surface modification, biomedical applications, and flow control. Understanding the underlying physical and chemical processes relevant to plasma production is therefore of great interest. A common way to gain a deeper understanding of these processes is through numerical modelling and simulations. An accurate description of the plasma and the processes that occur during its production requires the consideration of many particle species and the reactions that occur between them, spanning over long temporal and spatial scales. This often results in a large system of partial differential equations (PDEs), whose solving can take days, weeks, or in some cases even months. Optimising the solution procedure for these equations is therefore of great importance. Recent developments in the field of machine learning (ML) and artificial neural networks provide a great basis for the combination of conventional numerical techniques and modern ML methods to achieve this. The aim of the project is to introduce junior researchers from two partner institutions to the field of plasma modelling, numerical solution of the PDEs and machine learning (ML), establishing a basis for further collaboration and joint advancement of the current state of research.
Funded by the Deutscher Akademischer Austauschdienst e.V. (DAAD), project number 57703239.
Partners: Faculty of Sciences and Mathematics, University of Niš

NFDI4BIOIMAGE is a consortium of the National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur, NFDI) in Germany. A consortium within the NFDI comprises legally independent partner institutions working together with the goal to advance the capacity and the capability of researchers in Germany and beyond to professionally handle, store, annotate, share, and reuse research data. Our focus is on all steps of the research data life cycle for microscopy and bioimage analysis. INP participates in NFDI4BIOIMAGE with research data management workflows for bioimaging in plasma medicine and beyond.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 501864659, https://gepris.dfg.de/gepris/projekt/501864659.
Partners: NFDI4BIOIMAGE consortium, https://nfdi4bioimage.de

The Confine project is aimed to investigate the influence of short-lived species generated by discharges in small volumes on the coupling of plasma and catalytic processes. Due to the complex chemistry in gas mixtures intended a plug flow model was established which includes the volume and surface reactions, the electron energy balance and the heat equation. In a first step a mixture of helium and water was investigated and validated by LIF measurements of OH number density by project partners at the University of Minnesota.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 509169873, https://gepris.dfg.de/gepris/projekt/509169873.
Partners: Department of Mechanical Engineering, University of Minnesota; Institut für Chemische Reaktionstechnik, Technische Universität Hamburg

Patents encapsulate a significant portion of humanity's technical knowledge, presenting invaluable insights into innovative solutions, key substances, methods, and processes that can address pressing scientific and technological queries. Despite their potential, research indicates that this treasure trove of information goes largely untapped in scientific contexts, primarily due to scientists' challenges in navigating complex patent structures and a lack of proper analytical tools. The existing solutions provide limited access and fail to incorporate semantic descriptions, hindering connections between patent data and scientific literature. To tackle these challenges, we are developing the Patents4Science-Information Infrastructure (P4SI), aimed at creating an intuitive and sustainable pathway to patent knowledge. By employing automatic data analysis and semantic linking with extensive knowledge bases, our project semantically enriches, indexes, and interconnects patent content with scientific literature, culminating in the creation of a patent-centric knowledge graph. Aligned with the Linked Open Data (LOD) and FAIR data principles, our innovative framework does not only broaden the scope of knowledge in the sciences but will also expand to include other research domains and collaborate with key initiatives like NFDI, GAIA-X, and EOSC in the future.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 496963457, https://gepris.dfg.de/gepris/projekt/496963457.
Partners: FIZ Karlsruhe – Leibniz Institute for Information Infrastructure; Leibniz-Institut für Werkstofforientierte Technologien – IWT; INM – Leibniz-Institute for New Materials gGmbH

The project aims to describe and understand low-current arcs (0.5 to 20 A) in low-voltage DC switchgears, which are increasingly important for applications like electro mobility and local grids with photovoltaic systems and batteries. Arc switching devices offer low-cost solutions combined with semiconducting devices in hybrid switchgears due to their ability to provide galvanic separation and high insulation levels. Despite decades of use, the fundamental understanding of physical processes in low-current switching arcs remains incomplete, particularly regarding unexpected behaviours of arc voltage and radiation in short gap lengths. The transfer of electric current from non-refractory cathodes, like copper alloys, to low-current arcs at atmospheric pressure poses theoretical challenges due to low thermionic electron emission. This project will tackle these issues through experimental and theoretical studies, focusing on arcs between flat cylindrical electrodes in ambient air. The investigation will evaluate the voltage-current characteristics of the breaking process, spatial arc structure, and the expected role of metal vapor and non-equilibrium conditions. A model switch will be established, employing high-speed imaging, tomography, and spectroscopy, alongside numerical simulations to create a comprehensive understanding of current transfer and arc attachment at non-refractory cathodes. The goal is to provide a self-consistent description that can explain the current-voltage characteristics and inform on switching performance, electrode erosion, and the longevity of low-voltage switching devices.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 524731006, https://gepris.dfg.de/gepris/projekt/524731006.
Partners: Institute of Electrical Power Engineering, University of Rostock

Baeva, M.; Cressault, Y.; Kloc, P. Comparative Studies on the Radiative Heat Transfer in Arc Plasma and Its Impact in a Model of a Free-Burning Arc. Plasma 2024, 7, 631–650. https://doi.org/10.3390/plasma7030033 Open Access

Baeva, M. Reversal of the electric field and the anode fall in DC arcs in air during contact opening, J. Phys. D: Appl. Phys. 57 (2024) 39LT01, https://doi.org/10.1088/1361-6463/ad5c73 Open Access
To enhance the application of vacuum arc-based technology, a comprehensive understanding of the physical processes governing arc plasma behaviour and its interactions with surrounding materials—such as electrodes and walls—is essential. Experimental findings have illustrated various attachment modes on both the anode and cathode, influenced by prevailing physical processes; however, crucial aspects of mode formation remain poorly understood. Additionally, there is a pressing need for methods to control mode appearance and transitions to optimize operations across diverse applications. Key knowledge gaps include: (1) the role of excited species near electrodes and in arc fringes; (2) dynamics between convection and diffusion of plasma species; (3) the influence of cathode and anode activity on mode formation; (4) the impact of electrode movement, variations in input power, and external magnetic fields; and (5) the accuracy of existing vacuum arc models in predicting these attachment modes. This proposed project aims to address these gaps by investigating the high-current vacuum arc constriction process and anode attachment mode transitions with specific objectives: (i) clarifying the relationship between attachment modes and species transport processes using a self-consistent DC vacuum arc model; (ii) examining the role of excited species and chemical reactions through a collisional-radiative model; (iii) exploring energy, momentum, and mass transfer processes in electrode regions during arc column constriction via parametric studies; (iv) analysing the influence of electrode motion and AC power changes using an extended transient numerical model; and (v) validating the predictive performance of the vacuum arc model through detailed experimental studies. The insights gained significantly advance the understanding of the fundamental mechanisms underpinning vacuum arc operations.
Funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), project number 548964199, https://gepris.dfg.de/gepris/projekt/548964199.