Electromagnetic Compatibility (EMC)

Active Learning for Optimization of EMC processes

With the ever increasing operating frequencies and powers, EMC has now become a major consideration on any project involving the design, construction, manufacture and installation of electrical and electronic equipment and systems. An important step in the design of components and prediction of EMC related problems is the modeling and simulation. The complexity and performance of electrical and electronic devices as well as the number and range of variables in the design spaces means that many of the Physics-Based (PB) used tools are either too slow or too inaccurate for effective design and optimization. Recently, machine learning (ML) tools and techniques have been increasingly used in the EMC domain either to improve PB approaches or to replace them. In ML, computers are used to probe vast amounts of data for structure. One requirement for the effective ML model building is the availability of these large datasets for the training and testing processes. The generation of simulation data of EMC systems using PB tools is more than often quite expensive and very time consuming.

The main goal of this project is the adaptation and extension of active learning schemes such as Bayesian Optimization (BO) to the realm of EMC. In active learning, the expensive to evaluate samples used for training and prediction are intelligently collected at each iteration to reduce the needed number of simulation runs. The active learning methods can be used for optimization tasks, modeling of systems or both at the same time. The main working points can be summarized as:

Adaptation and foundation-laying of BO-based optimization and model building in the fields of EMC, SI, PI and bio-EMC.
Establishment of a framework to evaluate the certainty in the solutions provided by the active learning scheme.
Contribution to the available databases with the generated datasets for the service of the electrical engineering and EMC community.

Bayesian active learning and optimization scheme used to intelligently select the next samples to be simulated and added to the available data. The data is fed to the Bayesian model that predicts the optimal parameters while actively building an accurate model. Source: TET, TUHH

Animation of Bayesian active learning to approximate and maximize a 1D function f(x). The utility function is used at each iteration to pick the next best sample x to observe. The observations are used to create a more accurate approximation of the function and find the maxima. Source: TET, TUHH

Funding: Scholar of the Konrad Adenauer Foundation
Contact: Youcef Hassab
Start date: 02.11.2022

Analysis of Orbital Angular Momentum (OAM) antennas in Complex Environments

Electromagnetic waves can have a form of moment called the Orbital Angular Momentum (OAM). In waves with this property, the area perpendicular to the direction of propagation does not have a constant phase, as is the case with plane waves. Rather, the phase varies around the center of the wave and this phase distribution rotates in the direction of propagation. From the possible phase distributions and the direction of rotation different orthogonal modes result. This means that the modes do not influence each other and can therefore be used well for communication purposes.

In the past some properties of communication with OAM antennas have been investigated. The properties of OAM antennas shall now be further analyzed from the point of view of EMC.

Dipolar array of eight elements with which the first OAM mode is excited by operating the individual dipoles with a phase difference of ∆Φ=45°. The operating frequency is f=100 MHz. Source: TET, TUHH

Phase of the z-component of the E-field on the surface perpendicular to the direction of propagation. The phase was evaluated in three different distances to the antenna. As antenna a dipole array of eight elements was used, which excites the first OAM mode. The circular sections show how the phase rotates exactly 360° in the azimuthal direction. You can also see how this phase distribution rotates in the direction of propagation. Source: TET, TUHH

Funding: DFG
Contact: Michael Wulff, M Sc.
Start date: 01.12.2020

EMC of Complex Systems

Ph. D. Thesis Fabian Happ. 01.04.2012 – 01.09.2017

In this thesis a numerical method based on the Method of Moments and analytical formulations are developed to calculate electromagnetic fields in the vicinity of Carbon Fiber Composite (CFC) materials. The methods are applied to predict the shielding effectiveness of different CFC materials, to calculate lightning transfer functions for the coupling into an aircraft geometry made of CFC, and to estimate the influence of CFC on the crosstalk between transmission lines.

Apertures in a Computer casing (Source: TET, TUHH).

Current distribution on a field illuminated Computer casing (Source: TET, TUHH).

Related Publications:

Fabian Happ, Heinz-Dietrich Brüns, Frank Gronwald

An efficient implementation of the periodic method of moments for shielding effectiveness calculations of thin-wire grids Proceedings Article

In: URSI AT-RASC, Gran Canaria, Spain, May 28 – June 1, 2018.

Active Learning for Optimization of EMC processes

Analysis of Orbital Angular Momentum (OAM) antennas in Complex Environments

EMC of Complex Systems

HIRF Protection Using Energy Selective Diode Arrays

Analysis of Electromagnetic Interference in Server Casings

Application of the Characteristic Mode Analysis to Antenna Design and Electromagnetic Compatibility

Accurate and Efficient Algorithms in the Method of Moments for the Analysis of High Intensity Radiated Field Coupling into Aircraft

Development of a Full-Wave Module for the High Intensity Radiated Field (HIRF) Synthetic Environment