Projects
Project area A: Electromagnetics
Modern electric machines typically operate within a wide range of torque-speed operating points. To evaluate the overall performance of a given drive over a given drive cycle, time-consuming model-based time-stepping analyses or experimental investigations of the cycle-over-time can be carried out. Alternatively, the performance can be evaluated based on torque-speed performance maps that provide the steady-state performances for the individual operating points. This project will address the fundamental questions on the accuracy of the use of such performance maps, and expand into alternative approaches drawing from the collaboration within the CRC.
Project leader: Annette Mütze
Location: TU Graz
Researcher(s): Kourosh Heidari Kani, Pawan Dhakal
This project enables fast, high-fidelity transient simulations of electric machines using a domain decomposition method based on isogeometric analysis. Parallelisation with effective, machine-learning-assisted preconditioners accelerates repeated computations, making uncertainty quantification and optimisation feasible in three spatial dimensions, including hysteresis and complex geometric features to capture high-frequency effects.
Project leaders: Sebastian Schöps, Peter Gangl
Location: TU Darmstadt
Researcher(s): Mario Mally, Devin Balian
This project will enable transient machine simulation with the same predictive power as standard 3D finite-element models, but at a computation cost reduced from several hours to a few minutes. This will be achieved by error-controlled adaptation in space and time and by tailored hybrid modelling techniques. The adaptive resolution will be exploited to construct high- and low-fidelity models which, combined with an appropriate model management, will accelerate outer-loop algorithms such as, e.g., optimisation.
Project leaders: Herbert De Gersem, Annette Mütze
Location: TU Darmstadt
Researcher(s): Max Schaufelberger, Cecilia Pagnozzi do Nascimento
Insulation systems in electric machines limit the permissible operating range and significantly determine the service life. Therefore, a complete knowledge of the electrothermal insulation system problem is essential for the mission of the CRC of developing holistic simulation design routines. This project models machine insulation systems as a coupled electric and thermal problem including realistic variabilities of the insulation material parameters and geometry. Transient or cycling operating stresses are simulated to predict insulation failures and to assess novel cooling strategies.
Project leader: Yvonne Späck-Leigsnering
Location: TU Darmstadt
Researcher(s): Christian Bergfried
To increase the efficiency of electrical machines, it is essential to comprehend the magnetic loss mechanism and its dependency on the local defects and grain structure of the magnetic core materials. In this project, we will apply the micromagnetics model based on the Landau-Lifshitz-Gilbert equation, computational homogenisation and Machine Learning to calculate macroscopic hysteresis and losses, which are informed by both local defect features at grain boundary and by microstructural grain structure. Based on simulation data of a large number of local structure samples and grain structure samples, which are either measured or synthetically generated, surrogate models for the linkage between local features and reverse field and for prediction of macroscopic hysteresis and loss will be also obtained by Machine Learning. The multiscale and data scheme will be validated via experimental data and applied for case studies related to cutting and aging.
Project leader: Bai-Xiang Xu
Location: TU Darmstadt
Researcher(s): Patrick Kühn
To optimise today’s electrical machines, an accurate and time efficient prediction of the performance, efficiency, thermal and noise behaviour in inverter operation is needed. This project will deliver a methodology to predict the high frequency currents and subsequent effects when operated with pulsed voltage signals. Starting from a FE-based coupled motor-inverter simulation with time-parallel solver, advanced approaches, which are the harmonic balance method, FE-based co-simulation with optimised time-stepping and a reduced order model, will be examined in detail, of which the most promising one will be further implemented. To validate the simulation methods, holistic measurements including performance, efficiency, thermal behaviour and NVH will be conducted at the TUDa Traction motor of phase 1.
Project leader: Yves Burkhardt
Location: TU Darmstadt
Researcher(s): Leon Blumrich
Project area B: Fluid mechanics
Cooling the rotor of electrical machines is still a challenge due to poor thermal connectivity. In the first funding phase, for a heat pipe-based cooling strategy, the necessary numerical methods were developed and tested within the highly accurate BoSSS framework. In the second phase, this concept will be continued with two central approaches. First, the heat pipes will be further optimised by modelling their wick structure as a porous medium with a capillary-like surface layer, including the investigation of centrifugal effects and the off-axis distribution of multiple heat pipes. Second, direct liquid cooling by injecting coolant into the rotor-stator gap is investigated. Different rotor speeds lead to different flow states, including chaotic foam flow and layered structures. These will be modelled and simulated with BoSSS and validated by experiments in project B05.
Project leaders: Martin Oberlack, Günter Brenn
Location: TU Darmstadt
Researcher(s): Irina Shishkina
Overheating of the rotor in electric motors may alter the magnetic properties of rotor-integrated surface magnets or damage electric insulation layers. The present study aims to significantly enhance current airflow based direct surface cooling principles by adding small liquid drops to the flow passing through the rotor-stator gap. The interaction between airflow flow, droplet dynamics and substrate properties on the resulting heat flux will be studied in detail in a generic rotor test section. The objective of the planned research is to experimentally identify driving cooling mechanisms to develop an experimentally validated model for aerosol based thermal load peak treatment in a rotor-stator gap.
Project leaders: Jeanette Hussong, Ilia Roisman
Location: TU Darmstadt
Researcher(s): Samaneh Abdi Qezeljeh
Classical cooling methods are challenged by high heat fluxes and complex geometries found in compact electric motors. End windings, with their tightly bundled wires and insulating materials, are particularly difficult to cool due to limited fluid access, risk of vapour trapping and drop bouncing. Compound drops – coolant droplets encapsulated in a thin oil shell – offer a promising solution. The oil shell suppresses bouncing and ensures effective mobility, enabling the coolant core to fully utilise its latent heat. The project will experimentally explore compound drop generation, dynamics and cooling performance, and provide in collaboration with project A04 a model to support the design of advanced cooling systems.
Project leaders: Carole Planchette
Location: TU Graz
Researcher(s):
The aim of this project is to gain a better understanding of spray-cooled electric motors through detailed experimental investigations of a generic electric motor where the cooling oil is introduced into the end spaces through holes in a hollow shaft. The optical access allows the use of advanced laser optical measurement techniques to measure oil film thickness, oil temperature, oil film dynamics, rotor surface temperature and flow dynamics. The influence of fluid dynamic processes on the heat transfer at the end windings and in the air gap and their effect on the torque losses will be investigated. The experimental data will support the development of a comprehensive model of spray-cooled electric motors.
Project leaders: Andreas Dreizler
Location: TU Darmstadt
Researcher(s): Matthias Bonarens
Project area C: Numerics
This project deals with the analysis and time domain simulation of the heterogeneous coupled system of differential algebraic equations arising from the multiphysical description of an electric machine. A system-level approach allows to couple existing models for the different physical subsystems that are involved and successively improve their predictive capability by including improved models that arise from other projects. The aim is to allow for a full drive cycle simulation by means of efficient multirate methods as well as parallelisation in space and time.
Project leaders: Idoia Cortes Garcia, Herbert Egger
Location: TU Darmstadt
Researcher(s): Michael Wiesheu
This project addresses the systematic derivation and analysis of coupled field models describing the complex physical behaviour of electric machines and their components during transient operation. An energy-based variational paradigm is used, in which the energy-dissipation of the resulting coupled nonlinear dynamical systems is incorporated explicitly through the specific form of the mathematical models. Approriate structure-preserving discretisation strategies are devised and analysed for the reliable and accurate simulation in the presence of nonlinear material response, strongly varying length and time scales, complex and moving geometries as well as various kinds of uncertainties.
Project leader: Herbert Egger
Location: JKU Linz
Researcher(s): Felix Engertsberger
An accurate geometric representation is an absolute must for reliable computational simulations, especially for electrical drives where the air gap between the stator and rotor is crucial. The project will use the isogeometric paradigm combined with level-set functions to enable an advanced discretisation framework that addresses this challenge. Thereby, it provides fundamental routines for the novel space-time and optimisation methods developed in other projects of this CRC. Finally, the exchange and subsequent editing of the optimised shapes is enabled by generating analysis-suitable boundary representations compatible with current CAD standards.
Project leader: Benjamin Marussig
Location: TU Graz
Researcher(s): Andreas Grendas, Guilherme Henrique Teixeira
An efficient, reliable, fast, parallel and accurate direct numerical simulation tool for the solution of time-dependent partial differential equations is a mandatory ingredient for the design and optimisation for electrical machines. Space-time finite element methods and related domain decomposition methods allow an adaptive resolution and parallelisation strategies simultaneously in space and time. Applications involve general parabolic evolution equations, and in particular the eddy current approximation of the time-dependent Maxwell equations.
Project leader: Olaf Steinbach
Location: TU Graz
Researcher(s): Michael Reichelt, Christian Köthe
Noise and vibration (NVH) of electric drives is one of the design criteria for effective modern designs. Due to the broad band of exciting frequencies a time domain formulation for the simulation of structural vibrations and sound will be developed. The focus of the project will be on the efficient realisation of an IGA based boundary element method in time-domain, including variable time step sizes (generalized convolution quadrature method) and data sparse representations (generalized adaptive cross approximation) with respect to the spatial and temporal variables.
Project leader: Martin Schanz
Location: TU Graz
Researcher(s): Vibudha Lakshmi Keshava
This project develops an advanced isogeometric analysis framework for accurately simulating multiphysical interactions in electric machines, including rotor dynamics, magnetostriction and contact phenomena. By leveraging exact geometry representation, high regularity of the solution, adaptive refinement techniques and efficient coupling methods, the framework will significantly enhance predictive capabilities, facilitating efficient, reliable and robust electric machine designs, particularly addressing the mechanical challenges posed by axial flux machines.
Project leader: Melina Merkel
Location: TU Darmstadt
Researcher(s): Boian Balouchev
Project area D: Optimisation
This project will develop data-driven surrogate modelling methods to enable uncertainty quantification (UQ) studies within the context of electric machine design. First, material and geometrical uncertainties in the form of random fields will be stochastically modelled. Next, machine learning regression algorithms will be designed to approximate the dependence of electric machine quantities of interest on uncertain design parameters. Last, surrogate-based UQ methods for failure probability estimation and multivariate sensitivity analysis, two important and computationally demanding UQ tasks, will be developed to quantify the impact of uncertainty, provide novel insights, and facilitate improved machine designs.
Project leaders: Dimitrios Loukrezis, Sebastian Schöps
Location: TU Darmstadt
Researcher(s): Aylar Partovizadeh
This project deals with the topology optimisation of electric machines considering not only electromagnetic fields, but also thermal fields and their interaction. The underlying problem is modelled as a system of nonlinear, time-dependent partial differential equations that is considered in both two and three space dimensions. The optimisation is carried out by means of multi-material density-based and level set methods. Practical feasibility and mechanical stability of the designs are ensured and methods for efficient design space exploration are investigated.
Project leader: Peter Gangl
Location: RICAM/JKU, Linz
Researcher(s): Nepomuk Krenn, Michael Winkler
Production tolerances, material variations, usage scenarios and other influences lead to uncertainty in the optimal design of electric machines. The project considers the development, analysis and application of efficient derivative-based methods for the robust shape and topology optimisation under uncertainty. In the second phase more complex motor models including time-dependence, magnetic hysteresis models as well as thermal aspects will be addressed. To deal with the increased complexity, nonlinear manifold reduced order models with error control will be integrated in the optimisation methods. Moreover, the optimisation methods for optimal design of experiments will be extended to the accurate identification of local magnetic properties in the newly developed hysteresis and coupled magneto-mechanical material models of project D04.
Project leader: Stefan Ulbrich
Location: TU Darmstadt
Researcher(s): Theodor Komann
For the design of new electric motors, accurate knowledge of the local magnetic properties and a hysteresis model for accurate estimation of losses are of paramount importance. This project develops a combined method based on measurements, 3D simulations and inverse schemes to locally determine the magnetic properties of electrical steel sheets and permanent magnets as used in electrical machines. The results will enable numerical simulations with accurate material models to calculate local and global losses, a key element for reliable new designs of electrical machines through advanced topology optimisation.
Project leader: Manfred Kaltenbacher
Location: TU Graz
Researcher(s): Andreas Gschwentner, Lukas Domenig
Efficient performance evaluation, design exploration, optimisation and uncertainty quantification for design and digital twins of electric machines require computationally fast, low-dimensional nonlinear surrogate and reduced-order models Thus, the objective of this project is to develop data-driven but physics-aware surrogate modelling techniques that can provide accurate, efficient, reliable and robust predictions, are trainable in small data regimes, and include parameter dependencies. For this purpose, we combine the physical interpretability and mathematical rigour of port-Hamiltonian systems with the approximation capabilities and flexibility of machine learning. These models will be able to replace high-fidelity simulations and facilitate uncertainty quantification and design optimisation.
Project leader: Oliver Weeger
Location: TU Darmstadt
Researcher(s): Fabian Roth
Project area Z: Central projects
In the second funding period of the CRC, the research data management project will advance knowledge management through the use of artificial intelligence – primarily in the form of LLMs (Large Language Models). A second focus will be on high-performance computing, in particular coupled multiphysics simulations and parallel time integration. Furthermore, the workflows created in the first phase for the quality assurance of data and codes (continuous integration) will be further developed.
Project leaders: Florian Kummer
Location: TU Darmstadt
Researcher(s): Teoman Toprak
The CRC's integrated research training group (RTG) will provide the scientific key competences to all junior researchers for a successful career within the CRC and beyond – in industry as well as academia. The integrated RTG will coordinate a scientific training programme bringing together the necessary knowledge from electric engineering, numerical mathematics and fluid mechanics. Therefore, it offers a specific scientific training tailored to the interdisciplinary field of research and development, thereby complementing the transferable skills programme of the existing doctoral schools and initiatives for computational engineering.
Location: TU Darmstadt
The Central Administrative Project Z03 is dedicated to support the manager and Spokesperson of the CRC with central administrative tasks in the Darmstadt part of CRC and several activities for the whole CRC. These tasks include proper handling and execution of the accounting and reporting of the CRC, organizing all kind of events, handling correspondence at CRC in different languages, and managing travel and accommodation logistics for international guests. The project Z03 focuses on providing the infrastructure required for efficient events and project execution, promoting early career researchers and providing them equal opportunities.
Location: TU Darmstadt
The Z04 coordination project supports the Spokesperson and Deputy Spokesperson with respect to all administrative, legal, and financial duties within the CRC. It is serving as the central platform and interface for all communication among members and external collaborators. It provides the required administrative infrastructure and support to ensure decision-making processes, scientific exchanges and regular meetings are carried out in the most efficient way. Z04 also implements measures for equal opportunities. Furthermore, it maintains the most up to date research data management practices, ensuring proper documentation, storage, and public accessibility of research data respecting FAIR data principle.
Location: TU Graz
