arXiv subjects

Klaus-Dieter Weltmann

Publications and source records attributed to Klaus-Dieter Weltmann.

4 recordsLinked to original sources

arXiv2026

Prediction of Re-Ignition Times in Dielectric Barrier Discharges

Discharge ignition events in dielectric barrier discharges (DBDs) self-organise into spatio-temporal patterns with varying degrees of order. The complex dynamics of a DBD and intricate structure of occurring patterns complicate the formulation of predictive, mechanistic descriptions. We present the formulation of a reduced-order model that describes the re-ignition dynamics between consecutive discharges appearing at the same position inside a DBD arrangement. The model is derived from an equivalent electric circuit and validated against fluid-Poisson simulations and experiments performed with a multi-filament arrangement in air-like gas mixtures at atmospheric pressure driven by sinusoidal high-voltage waveforms. The experimental scenarios include a highly ordered regime where discharges ignite at regular time and space intervals generating a pattern stable over several periods, and an unstable regime with discharges appearing at seemingly random positions and times. The model accuracy is assessed in both regimes and it is found that the associated prediction uncertainty provides a quantitative measure of the spatial order of the discharge pattern.

physics.plasm-ph↗

arXiv2023

Towards a Machine-Learned Poisson Solver for Low-Temperature Plasma Simulations in Complex Geometries

Poisson's equation plays an important role in modeling many physical systems. In electrostatic self-consistent low-temperature plasma (LTP) simulations, Poisson's equation is solved at each simulation time step, which can amount to a significant computational cost for the entire simulation. In this paper, we describe the development of a generic machine-learned Poisson solver specifically designed for the requirements of LTP simulations in complex 2D reactor geometries on structured Cartesian grids. Here, the reactor geometries can consist of inner electrodes and dielectric materials as often found in LTP simulations. The approach leverages a hybrid CNN-transformer network architecture in combination with a weighted multiterm loss function. We train the network using highly-randomized synthetic data to ensure the generalizability of the learned solver to unseen reactor geometries. The results demonstrate that the learned solver is able to produce quantitatively and qualitatively accurate solutions. Furthermore, it generalizes well on new reactor geometries such as reference geometries found in the literature. To increase the numerical accuracy of the solutions required in LTP simulations, we employ a conventional iterative solver to refine the raw predictions, especially to recover the high-frequency features not resolved by the initial prediction. With this, the proposed learned Poisson solver provides the required accuracy and is potentially faster than a pure GPU-based conventional iterative solver. This opens up new possibilities for developing a generic and high-performing learned Poisson solver for LTP systems in complex geometries.

physics.comp-ph↗

arXiv2012

Using a dual plasma process to produce cobalt--polypyrrole catalysts for the oxygen reduction reaction in fuel cells -- part II: analysing the chemical structure of the films

The chemical structure of cobalt--polypyrrole -- produced by a dual plasma process -- is analysed by means of X-ray photoelectron spectroscopy (XPS), near edge X-ray absorption spectroscopy (NEXAFS), X-ray diffraction (XRD), energy-dispersive X-Ray spectroscopy (EDX) and extended x-ray absorption spectroscopy (EXAFS).It is shown that only nanoparticles of a size of 3\,nm with the low temperature crystal structure of cobalt are present within the compound. Besides that, cobalt--nitrogen and carbon--oxygen structures are observed. Furthermore, more and more cobalt--nitrogen structures are produced when increasing the magnetron power. Linking the information on the chemical structure to the results about the catalytic activity of the films -- which are presented in part I of this contribution -- it is concluded that the cobalt--nitrogen structures are the probable catalytically active sites. The cobalt--nitrogen bond length is calculated as 2.09\,Å and the carbon--nitrogen bond length as 1.38\,Å.

cond-mat.mtrl-sci↗

arXiv2012

Using a dual plasma process to produce cobalt--polypyrrole catalysts for the oxygen reduction reaction in fuel cells -- part I: characterisation of the catalytic activity and surface structure

A new dual plasma coating process to produce platinum-free catalysts for the oxygen reduction reaction in a fuel cell is introduced. The catalysts thus produced were analysed with various methods. Electrochemical characterisation was carried out by cyclic voltammetry, rotating ring- and rotating ring-disk electrode. The surface porosity of the different catalysts thus obtained was characterised with the nitrogen gas adsorption technique and scanning electron microscopy was used to determine the growth mechanisms of the films. It is shown that catalytically active compounds can be produced with this dual plasma process. Furthermore, the catalytic activity can be varied significantly by changing the plasma process parameters. The amount of H$_2$O$_2$ produced was calculated and shows that a 2 electron mechanism is predominant. The plasma coating mechanism does not significantly change the surface BET area and pore size distribution of the carbon support used. Furthermore, scanning electron microscopy pictures of the produced films are presented and show the preference of columnar growth mechanisms. By using different carbons as the support it is shown that there is a strong dependence of the catalytic activity that is probably related to the chemical properties of the carbon.

physics.chem-ph↗