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Early Registration closes 16 Feb 24
Discrete physical phenomena are modelled in quite different ways usually using wholly discrete software packages. Accurate modelling of electromagnetic systems requires multiple distinct physical phenomena to be considered simultaneously.
Testing of systems is often limited to reduced scope component testing, we will look at how these reduced scope component tests can be integrated into whole system models.
This Seminar will focus on the latest developments in the multiphysics & multiscale modelling field, as related to modelling of magnetic systems. Targeting the following contemporary areas:
- Cloud based simulation
- Coupling methods
- Machine learning
- Hardware in the loop
The event will run from 09:00-18:00 (UK time) and will include talks and a tour of ZF Labs.
ZF Automotive UK Limited
Active Safety Systems Div
The Hub, Central Boulevard,
Blythe Valley Park, Shirley, B90 8BG
Our reduced rate offer has now expired but you can still book rooms at:
Village Hotel Solihull
The Green Business Park,
Dog Kennel Ln, Shirley,
Solihull B90 4JG
Nearest Airport: Birmingham International
Nearest Train station: Widney Manor or Shirley
Bus Routes: A7, A8, X20 stop right outside the site
By Car: From the M42: Exit M42 Junction 4 From northbound, get into far left lanes on exit slip road signposted Blythe Valley Business Park, taking first exit at roundabout. From southbound, get into lane marked Blythe Valley Business Park and follow motorway roundabout around to third exit.
Both: Once you have left the motorway junction follow the long left hand bend until you reach the roundabout with the park security lodge on, second exit straight over this roundabout (to the right of the security lodge.) Follow the road until the next roundabout, take the first exit (left) and you are now on central boulevard. You will be able to see the ZF logo from the moment you turn onto central boulevard. Please use the second entrance onto the site if you’re a visitor or the third entrance if you are a staff member
Parking: Head to the main visitor entrance and park in any empty spaces available in front / side of the building (just not in the tenant parking area (this is clearly marked). Any issues, security will be able to advise you.
Please let us know as soon as possible if you have any dietary requirements we need to be aware of.
The dress code for the event is business attire / smart casual.
CONTINUING CONTACT / GDPR
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We thank our sponsors
Ferrofluid Cooling for High Power Dense PM Machines
by Guang-Jin Li of University of Sheffield
In this presentation, we explore key findings derived from a recently concluded EPSRC project, focusing on the innovative application of ferrofluid for machine cooling. Ferrofluid, an oil-based liquid containing nano-sized ferrimagnetic particles, was introduced into the end space (end-windings) of permanent magnet (PM) machines. Leveraging the magnetic body force generated by the end-winding leakage flux, the ferrofluid autonomously circulates, eliminating the need for external pumps. This unique circulation mechanism establishes an efficient heat transfer pathway from the end-windings to the housing equipped with a water jacket. Consequently, the incorporation of ferrofluid enhances the heat transfer rate, thereby improving the overall thermal performance of the machine. To simulate the cooling effects of ferrofluid in electrical machines, we developed multiphysics models that account for the intricate coupling between electromagnetic (EM) fields, fluid dynamics, and heat transfer. These models facilitated an in-depth exploration of the impact of various winding structures, such as concentrated double and single layer windings, and distributed overlapping windings, on the cooling efficiency of ferrofluid. Our findings revealed that distinct magnetic fields in the end space, generated by diverse winding structures, result in varying cooling efficiencies. Nevertheless, all machines employing ferrofluid cooling demonstrated a significant enhancement in both electromagnetic and thermal performance. This research sheds light on the promising potential of ferrofluid as a transformative cooling agent, providing valuable insights into the nuanced interplay between winding structures and the resulting cooling efficiency in electrical machines.
Design optimization of a Synchronous. Reluctance Electric Motor using Deep Learning Technology
by Nicolas Riviere of ANSYS
The automotive landscape has developed rapidly in recent years, due to the electrification trend to
support the transition towards a climate-resilient, energy-efficient, and low-carbon economy. Most of
passenger EVs today are using PM motors with NdFeB material in their rotor to boost performance
across the full operating range and exhibit high efficiency levels out of a limited space envelope.
However, since the dramatic surge of rare-earth materials’ price in 2011, car manufacturers are actively looking into viable alternative options. As a PM-free motor, the Synchronous Reluctance (SyncRel) machine is a cheap and attractive candidate, although it cannot outperform PM-based technologies in any way.
To get the most out of the SyncRel motor, an efficient optimization strategy is required. As of today,
traction motors are mostly optimized through FEA-based parametric optimization procedures, either
directly or from surrogate models. Such strategies are not suitable for the optimization of SyncRel
motors giving the high variety of possible shapes for the rotor flux barriers.
In this study, it is proposed to optimize the rotor of a SyncRel motor leveraging the proprietary deep
learning technology of Neural Concept. The optimization workflow uses CAD and CAE data generated from the Ansys Motor-CAD multi-physics integrated tool to train sophisticated neural networks. The
calculated predictive models are then used to rapidly optimize the electric motor for maximum output power within stress and torque ripple requirements.
This novel optimization strategy has the unprecedented benefits of generating out-ot-the-box design shapes by going beyond the original parameter space, while reducing computation times and
preserving a high level of accuracy with respect to the latest FEA-based parametric optimization
|Machine Learning Applied to Electromagnetic Device Analysis and Design
|Cris Emson, Infologic Design Ltd
|Ferrofluid cooled EM machine, multiphysics(required) simulaiton applicaiton. - novel cooling system CFD and EM
|Guang-Jin Li, University of Sheffield
|Design optimization of a Synchronous. Reluctance Electric Motor using Deep Learning Technology
|Nicolas Riviere, ANSYS
|Improved Understanding of Noise and Vibration Issues in Electric Machines
|ZF Group and Hexagon, System Dynamics
|Tour of ZF Group
|PM Motors in High Performance Applications: Modelling Challenges
|Alex Michaelides, HiSPEED Ltd
|Digital twin development for transportation electrification
|Tao Yang, University of Nottingham
|Bilquis Mohamodhosen, DASSULT Systemes UK Limited, Cris Emson, Infologic Design Ltd, Jonathan Godbehere, ANSYS