Yunpeng Liu (Chair, Vice President of North China Electric Power University)
Prof. Zhuoxiang Ren Sorbonne Université
9:10 - 9:20
Welcome Speech
Tao Qin (Deputy Director General of China Association for International Exchange of Personnel)
9:20 - 9:50
Presentation: Machine learning based digital twins in the design of electromagnetic devices
Designing electromagnetic devices requires models of the performance of candidate solutions. It is an iterative process which gradually increases in complexity with the final model, ideally, being a full multi-physics digital twin. Making design decisions requires information which allows competing designs to be evaluated as well as ensuring that the final system meets all the user requirements. The earlier design decisions are made, the more effective the design process. Complete, traditional, numerical models are both expensive to construct and to analyze, although, usually, significantly cheaper than physical prototypes. Generative machine learning systems have demonstrated the potential to increase the amount of information at the start– effectively “front-loading” the design process. In turn, this can significantly reduce the time needed to create a final design. The presentation will address the application of both machine learning based digital twins as well as more conventional numerical approaches.
David A. Lowther (Professor, McGill University, Canada)
David Lowther received his Ph.D. degree in 1973 from Brighton Polytechnic in the United Kingdom before moving to Imperial College, London where he spent 6 years in a post-doctoral position. In 1979 he moved to McGill University in Montreal, Canada as an Associate Professor in the Department of Electrical Engineering and was promoted to Full Professor in 1986. His research relates to the design and analysis of low frequency electromagnetic devices, including applications of artificial intelligence. In 1978, he co-founded Infolytica Corporation which developed computer aided design tools for low frequency systems. The company was acquired by Siemens DISW in 2017 and he is currently a Technical Director in Technology and Innovation at Siemens. He has published over 400 conference and journal papers and is a Fellow of the Institution of Engineering Technology, the Institute of Electrical and Electronic Engineers and the Canadian Academy of Engineering.
9:50 - 10:25
Group Photo + Tea/Coffee Break
10:25 - 10:55
Presentation: Spatial multi-scale modeling and numerical computation for electric power equipment
Spatial multi-scale refers to the coexistence of geometrically or physically distinct scale characteristics with significant differences within a system, such as microscopic defects, inclusions, voids, and crack tips in macroscopic equipment. In spatial domains, it requires revealing correlation mechanisms between local details and the global system through crossing multiple scales, and resolving nonlinearities, abrupt transitions, and multi-physics coupling problems that cannot be captured by single-scale approaches, via multi-scale theories and numerical models. Different from traditional numerical algorithms, the introduction of multi-scale algorithms can appropriately handle the nonlinear cross-scale integration of different physical laws at distinct spatial scale levels. In the presentation, the current research status of spatial multi-scale problems and fundamental principles of the algorithms will be introduced. Then, cases of multi-scale problems in practical engineering will be briefly introduced, such as cracks in fracture mechanics, multiphase flow in fluid mechanics, and dense thin-layer issues in silicon steel sheets for electromagnetic fields. Finally, highlighted discussions will be conducted based on typical multi-scale issues existing in current electric power equipment.
Shuhong Wang (Professor, Xi'an Jiaotong University)
Professor Wang Shuhong serves as a member of the Teaching Guidance Subcommittee for Fundamental Electrical and Electronic Courses under the Higher Education Institution Committee of the Ministry of Education, a member of the Curriculum Working Committee of the Shaanxi Provincial Higher Education Teaching Guidance Commission, a committee member of both the Applied Superconductivity Technology Committee and the Electrotechnical Theory and New Technology Committee of the China Electrotechnical Society, an IEEE Senior Member, and a participating expert in CIGRE Working Group WG2.60 "Thermal Behavior of Transformers". He has taught two undergraduate specialized foundational courses and one graduate course, receiving the Wang Kuancheng Education Award, two Shaanxi Provincial Teaching Achievement Awards, one Shaanxi Provincial Science and Technology Award, and three university-level teaching achievement awards. His sustained research focuses on simulation, analysis, optimization, and development of electrical equipment, having led two National Natural Science Foundation General Projects, participated in two NSFC Key Projects, directed one sub-project of the National Science and Technology Major Special Program, and overseen a sub-task of the National Key R&D Program. With over 100 peer-reviewed publications including more than 50 SCI-indexed papers, his work appears in IEEE Transactions on Power Systems, Applied Physics Letters, and other prominent journals. The main research interests are: Application of Multiscale Numerical Computing to Power Equipment; Application of Intelligent Algorithms in Power Equipment.
Prof. Zhuoxiang Ren Sorbonne Université
10:55 - 11:25
Presentation: Case studies on topology optimization and its acceleration with machine learning
In this presentation, an overview of topology optimization will be provided, including a comparison of the NGnet (normalized Gaussian network) method based on population-based stochastic algorithm with the density and level-set methods based on gradient-based search. The focus will then be on the NGnet method, which is applied to the design of a permanent magnet motor for air mobility and microwave devices. The efficacy of this approach is demonstrated by the successful discovery of a novel electromagnetic structure, which has led to an AI-based patent. An improvement in the NGnet method will also be presented. I then present the approaches that utilize machine learning to accelerate the optimization process. The analysis reveals that the online method, which involves updating the surrogate model during the optimization procedure, is more appropriate for general-purpose optimization compared to the offline method.
He received the B.E. and M.E. degrees in electrical engineering and the Ph.D. degree in engineering from Hokkaido University, Sapporo, Japan, in 1982, 1984, and 1992, respectively. From 1995 to 1997, he worked with Prof. Arnulf Kost at the Technical University of Berlin with the support of the Humboldt Foundation. He has been a Professor with the Graduate School of Information Science and Technology, Hokkaido University, since 2004. His research interests include computational electromagnetism, design optimization, and artificial intelligence (AI)-based design. He is the Vice President of the International Compumag Society. He is the author of “Topology Optimization and AI-based Design of Power Electronic and Electric Devices”, published by Academic Press in 2024.
11:25 - 11:55
Presentation: Hysteresis modelling methods of soft magnetic materials based on deep neural network
Hysteresis modelling is a fundamental and hot issue in the field of electrical engineering. Traditional hysteresis models are usually fail to balance computational accuracy and generality. While deep neural networks (DNNs) have shown significant advantages in hysteresis models in handling complex nonlinear relationships, high-dimensional data, and dynamic characteristics. In this report, deep neural networks are integrated into hysteresis modeling for magnetic materials, including forward and inverse models, as well as scalar and vector models. Based on these newly established models, the magnetization behavior of magnetic materials can be described more accurately, addressing the complexity of inverse problems and enabling more accurate simulation of complex three-dimensional magnetization phenomena. Compared to traditional models, the new models offer improvements in both computational accuracy and efficiency.
Yongjian Li (Professor, Hebei University of Technology)
Yongjian Li, Ph.D. is a Professor at the School of Electrical Engineering, Hebei University of Technology, renowned for his expertise in electromagnetic analysis and simulation of high-power electrical systems. With a career dedicated to advancing electrical engineering research, he has led over 20 major national projects, including key initiatives under the National Natural Science Foundation of China and national key R&D programs. His scholarly contributions encompass 100+ SCI publications, three monographs, and 30 authorized invention patents, three of which have achieved commercial success. Recognized for his groundbreaking work, Dr. Li has been awarded the First Prize for Scientific and Technological Progress by Hebei Province and the Chinese Society of Electrotechnical Technology, alongside honors such as the Hebei Outstanding Youth Fund, Hebei Provincial Outstanding Expert, and Tianjin Youth Science and Technology Award. He serves as Director and Vice Chairman of the Professional Committee, China Electrotechnical Society, Director at the Hebei Key Laboratory of Flexible DC Transmission Equipment and Technology, and Vice Chairman of the Hebei Society of Electrical Engineers. The main research interests are: Deep Belief Networks; Multi-Hysteresis Model Fusion; Three-Dimensional Spatial Hysteresis Model.
11:55 - 14:00
Lunch (Buffet) + Break
14:00 - 14:30
Presentation: Data-based approaches for electromagnetic problems
Machine-Learning (ML) approaches are getting more and more popular and effective for the resolution of direct and inverse problems in computational electromagnetics. The nature of the two classes of problems makes suited approaches quite different for each class. While direct problems are usually well-posed, inverse problems suffer from the curse of ill-posedness, requiring suited countermeasures to achieve satisfactory results. ML approaches cannot escape this problem, and dedicated paradigms must be devised. In the presentation, with reference to low and high frequency electromagnetic inverse problems, some ML approaches will be discussed, highlighting advantages and pitfalls. In particular, Shallow Neural Networks (SNN), Physically-Informed Neural Networks (PINN), Generative Adversarial Networks (GAN) and Deep Neural Networks (DNN) will be briefly revised and an assessment of performance using benchmark problems will be presented, both for direct and inverse electromagnetic problems.
Alessandro Formisano (Professor, the Seconda Università di Napoli, Italy)
Prof. Alessandro Formisano received the Ph.D. degree in Electrical Engineering from the University of Naples Federico II, Naples, Italy, in 1997. He is currently a Full Professor of Electrical Sciences at the University of Naples Federico II, with six years of seniority in the role. Since joining the Engineering Faculty in 1996, he has been actively teaching both undergraduate and Ph.D. courses in Electrical Circuits and Electromagnetism.
The main research interests are:
-Development of numerical models for electromagnetic coupled problems, plasma identification, and 3D magnetic field analysis in Tokamaks.
-Advanced methods and algorithms for optimal design and inverse problem resolution in quasi-stationary electromagnetism, incorporating both deterministic and stochastic approaches.
-Investigation of the effects of uncertainties in production processes on the performance of electromagnetic devices.
-Application of Artificial Intelligence and Neural Networks to solve direct and inverse problems in electromagnetism.
Dr. Zuqi Tang University of Lille
14:30 - 15:00
Presentation: Artificial intelligence surrogate model for electromagnetic device simulation
Electromagnetic field numerical simulation technology has become an essential tool in the design of electromagnetic devices. The advantage of numerical calculation is high accuracy, but the disadvantage is long computing time. Especially for three-dimensional electromagnetic field problems, it often requires solving algebraic equations with hundreds of thousands or even millions of unknowns, greatly limiting the application of numerical methods. This report introduces our research group's use of artificial intelligence method to develop surrogate models for commonly used electric motors in electromagnetic devices. This method has gradually been applied in the optimization design and control of electric motors, greatly saving computation time.
Weinong Fu (Professor, Shenzhen University of Advanced Technology)
Prof. Fu received his Ph.D. degree in electrical engineering from The Hong Kong Polytechnic University (PolyU), Hong Kong, SAR, China, in 1999. He is a Professor with Shenzhen University of Advanced Technology, Shenzhen, China. He worked as an Associate Professor and a Full Professor at PolyU about 13 years. He was one of the key developers with Ansoft Corporation, Pittsburgh, PA, USA. He has about seven years of working experience at Ansoft, focusing on the development of commercial software Maxwell. He has made many contributions to the theory and application of electromagnetic field computation and electric device design, including the publication of over 290 refereed journal papers. His research interests include computational electromagnetics, optimal design of electric devices, applied electromagnetics, and novel electric machines.
15:00 - 15:30
Presentation: High frequency electromagnetic field analysis of inductor for power electronics
In inductors excited by the inverter using Gallium Nitride power devices, the ringing phenomenon, which is the oscillation in the current waveform, increases the noise and loss. The ringing occurs due to the multi-resonances at the switching. To clarify the mechanism of the multi-resonances, the high frequency (HF) behaviors of ring core inductors with solid and laminated iron cores in frequency domain are measured and simulated by using the electromagnetic field finite element analysis (FEA) taking account of the stray capacitance and the frequency dependance of magnetic permeability. First, the measured HF response of the inductor with solid iron core is represented by FEA taking account of the stray capacitance of winding, and the mechanism of the multiple resonance phenomena is explained by a distributed-element circuit model. Next, the measured HF response of the inductor with laminated iron core is represented by FEA taking account of not only the stray capacitance but also the frequency dependance of magnetic permeability of the steel plate. It is also suggested that the original permeability without the skin effect decreases with frequency.
Kazuhiro Muramatsu is a Professor of Department of Electrical and Electronic Engineering at Saga University, Japan since 2008. He was educated at Okayama University and received B.E., M.E., and Ph.D. degrees in 1988, 1990, and 1993, respectively. He was with ALPS Electric Co., Ltd. from 1990 to 1994. He moved to Okayama University from 1994 and worked as an Assistant Professor until 2001. He was a Visiting Researcher at University of Bath, U.K. in 1999 and Grenoble Institute of Technology, France in 2000. He moved to Saga University as an Associate Professor in 2001. His major research topics are the finite element methods for 3-D magnetic and coupled field analyses and their applications on electrical machines and magnetic devices. He has been a member of the ICS Board since 2016 and the chair of IEEJ Investigating R&D Committee of Innovative Technology Development Using Electromagnetic Field Analysis since 2022. He served as a Chinese High-end Foreign Expert.
15:30 - 16:00
Presentation: Development and Key Technologies of Digital Twin Platform for Power Transformers
Digital Twin technology is one of the key enablers to achieve the digitalization and intelligent development of power transformers, and its realization becomes an urgent need to be addressed. However, the implementation of digital twin of power transformer faces multiple restrictions. The complexity of transformer geometry, limited methods for internal state assessment, and the demand on real-time simulation, all poses significant obstacles to the practical applications of transformer digital twin. This presentation will provide a through and detailed discussion on the key technologies and platform development method for transformer digital twin. The fundamental components and encapsulation strategies of transformer digital twin will be outlined, with a particular focus on the challenges and research progress in 3D visualization, multiphysics calculation and rapid model order reduction techniques. Finally, we will present the latest achievements in transformer digital twin flatform construction of our research team.
Fan Yang (Professor, Chongqing University)
Fan Yang is a Professor and Ph.D. Supervisor, currently serving as the Dean of the Graduate School at Chongqing University. He is a recipient of the National Young Talent Program and holds multiple prestigious academic positions, including Vice Chair of the Electrical Theory and New Technology Committee under both the China Electrotechnical Society and the Chinese Society for Electrical Engineering. He also serves as the Secretary of the Electrical Engineering Teaching Advisory Committee of the Ministry of Education, a member of the IEEE PES China Council Education Committee, a member of the CIGRE-C3 China National Committee (Power System Environmental Performance), and Vice Chair of the Electrical Engineering Discipline Teaching Committee of the China Electric Power Education Association. Additionally, he has been recognized as a Young and Middle-aged Key Educator in Chongqing. His research focuses on electromagnetic imaging, multiphysics field computation for power equipment, and digital twin technology. He has received multiple national and provincial awards for his contributions to education and research, including one First Prize and one Second Prize in National Teaching Achievement Awards, as well as one First Prize and one Second Prize in Chongqing Teaching Achievement Awards. Moreover, he has been honored with a First Prize and a Second Prize in Provincial and Ministerial Science and Technology Awards.
16:00 - 16:15
Tea/Coffee Break
16:15 - 16:45
Presentation: Characterization and modelling of silicon-steel under multi-axial magnetic and mechanical loading
In this presentation, we expose different aspects related to the characterization and modelling of Silicon-Steel. Indeed, the Silicon-Steel is one of the most used materials for the construction of the magnetic core of electrical machines and transformers, among others. The design and analysis of these devices require deep knowledge of the magnetic and mechanical properties of the material as well as adequate models to be incorporated in the analysis software, e.g., finite element simulation programs. The main interests of the presentation are the vector-properties characterization and modelling as well as the magneto-mechanical coupling in the material. Furthermore, issues such as the effect of plastic deformation and residual stress on the permeability and losses in the material are very important to characterize and model. We will present the developed characterization setups and models as well as some challenges related to the incorporation of these models in the finite element simulations of electrical machines and the validation of these models through experimental setups. The experimental work, besides giving important input for the simulations, can also help gaining better understanding of the underlaying physical phenomena, which might help developing better materials as well.
Anouar Belahcen (Senior Member, IEEE) received the M.Sc. (Tech.) and Ph.D. (Tech.) degrees from Aalto University (former Helsinki University of Technology), Espoo, Finland, in 1998 and 2004, respectively. He is currently Professor of power and energy with Aalto University, where he has been the Vice Dean of education within the School of Electrical Engineering since 2020. His research interests are numerical modeling of electrical machines, characterization and modeling of magnetic materials, coupled magneto-mechanical problems, magnetic forces, magnetostriction, and fault diagnostics of electrical machines. A. Belahcen has published more than 160 journal papers and 150 referred conference papers. He is a member of the steering committees or administrative board of several international conferences COMPUMAG (ICS), CEFC, ICEM, EPNC, he has chaired the editorial board of CEFC 2024 edition and served as Editor in Chief for the post-conference publication in the IEEE Transactions on Magnetics. He received several research grants from the Research Council of Finland (RCF) and is now leading a Center of Excellence on High-Speed Electromechanical Energy Conversion Systems (HiECSs), funded by the RCF. He has a substantial experience in research cooperation with academic and industry partners and startups.
Dr. Yanhui Gao Oita University
16:45 - 17:15
Presentation: Self-sensing technology based on multiphysics collaboration and its application in online monitoring of overhead transmission lines
Although artificial intelligence is rapidly becoming powerful, and is being widely applied to condition monitoring and diagnosis of electrical equipment. There are still many situations where sensors cannot be installed, such as in extreme environments with strong electric fields and limited space. For commercial devices, most data are from normal condition. The effective data under defects or malfunctions are insufficient to support the analysis by artificial intelligence. Therefore, during a certain period of time, numerical methods still play important role. With the help of numerical model of multiphysics field, a sensor can monitor multiple parameters, the range of monitoring is expanded, and even some components of device itself can be used as sensors. With the help of numerical model , the demand for data can be significantly reduced, and the physical meaning can be clarified. In this presentation, an application of the numerical model of multiphysics field in the online monitoring of overhead transmission lines will be introduced. With the help of numerical model of multiphysics field, a multifunctional self-monitoring method is developed that uses electromagnetic induced signals in ground wires of overhead transmission lines to comprehensively detect multiple abnormal states, such as lightning strikes, short circuits, icing, ice shedding, gallop, and dynamic line rating. Therefore, the ground wires are not only used for lightning protection, but can also serve as multifunctional sensors. The proposed method can offer comprehensive advantages, including multifunctional monitoring capabilities, wide monitoring range, cost-effectiveness, simplified installation/maintenance, unaffected by harsh weather, and self-powering functionality.
Bo Zhang (Professor, Tsinghua University)
He obtained his B.S. in 1998 and Ph.D. in 2003, both in electrical engineering from North China electric Power University. From 2003 to 2005, he was a post doctor at Tsinghua University, China. And then, he became assistant professor, associate professor and professor at the department of Electrical Engineering, Tsinghua University. Bo Zhang is interested in the numerical calculation of electromagnetic problems in high voltage engineering, intelligent monitoring and diagnosis for power equipment, and EMC in power system. He published more than 100 peer reviewed papers in SCI journals. He is the IEEE Fellow, IET Fellow, convener of CIGRE WGs C4.50 and B2.89, vice chair of IEEE WGs P2869 and P2970. He is the secretary of Steering Committee of Asia-Pacific International Conference on Lightning. He was awarded the Excellent Youth Fund of National Natural Science Foundation of China in 2013, the 34th Scientific Committee Award of the International Conference on Lightning Protection in 2018, and the IEEE EMC Technical achievement award in 2019.
17:15 - 17:45
Presentation: Viscoelastic modeling of magnetic losses using fractional derivative operators
Modern ferromagnetic materials (GO electrical steel, nanocrystalline, amorphous, etc.) have emerged as advanced solutions for improving magnetic performance, offering reduced core losses and high permeability. However, despite their superior properties, magnetic losses remain critical, particularly under high-frequency conditions. Existing simulation methods, including empirical models, time-dependent hysteresis models, and space-discretized approaches, often fail to accurately capture their complex viscoelastic magnetic properties over wide frequency ranges and amplitudes. This study evaluates the use of fractional derivative operators as innovative tools to predict magnetic losses in ferromagnetic cores. Four simulation approaches are analyzed: an analytical expression of the magnetic losses, two time-dependent hysteresis models (using first-order and fractional-order differential equations), and a space-discretized method coupling Maxwell’s equations with a fractional-order material law. The analytical method provides simplicity and reliable results for total losses but cannot capture temporal or spatial distributions. The lumped hysteresis models, particularly the fractional-order variant, offer improved accuracy by accounting for dynamic effects and frequency dependencies. The space-discretized method is the most robust, achieving the highest precision and providing detailed insights into the local distribution and contributions of magnetic losses.
Benjamin Ducharne (Associate Professor, Tohoku University, Japan)
Benjamin Ducharne received his MSc and PhD degrees in electrical engineering from Claude Bernard University Lyon 1, Villeurbanne, France, in 2001 and 2003, respectively. In 2004, he joined the Montefiore Institute in Liège, Belgium, as a postdoctoral researcher. In 2005, he was appointed as an Associate Professor at INSA Lyon, France. From 2018 to 2019, he was a Visiting Scholar and mid-term Lecturer at Purdue University, West Lafayette, IN, USA. Since 2020, he has been a full-time researcher at ELyTMaX, Tohoku University, Sendai, Japan. His research focuses on magnetic non-destructive testing, ferromagnetic and ferroelectric materials, hysteresis modeling, fractional operators, and multiphysics coupling.
17:45 - 18:00
Break
18:00 - 20:30
Dinner (Banquet)
March 26, Wednesday
8:30 - 9:00
Presentation: Application of CLN method to study magnetoquasistatic and electroquasistatic problems
The Cauer Ladder Network (CLN) method proposed at the origin by Kameari et al. enabled to reduce a numerical model based on a 2D vector potential FE formulation of the magnetoquasistatic (MQS) problems. Since then, extensions have been proposed to other formulations in MQS and electroquasistatic (EQS) problems. The main advantage of this method is that the reduced problem relies on an equivalent electrical circuit. It means that once the reduced basis is built in an offline stage, the online stage consists in solving an electrical circuit. The field distributions in the FE space are then reconstructed from currents and voltages. Using the CLN method, the coupling of other circuits is totally natural, which is of great interest in many applications since the device, modelled by the FE method, is very often electrically connecting to other devices represented also by equivalent circuit. In this communication, we propose to show how the CLN method can be used to study MQS and EQS problems. In the MQS case, we show briefly how the CLN method enables to construct reduced models from Finite Element (FE) potential formulations (A-φ, T-Ω and A-T). These reduced models are based on equivalent electrical circuits associating resistances and inductances. The method is applied to construct a reduced model of a Printed Board Circuit which is coupled with models of power switches in order to study the behavior of a power converter. In the EQS case, the CLN method can also be used to derive, from a FE scalar potential formulation, an equivalent circuit associating resistances and capacitances. To illustrate the possibility offered by the CLN method, it is applied to study a resin-impregnated paper bushing (component of High voltage transformer) and the influence of default of insulation.
Stéphane Clénet (Professor, Arts et Métiers Institute of Technology in Lille, France)
Since 2002, Stéphane Clénet is professor of electrical engineering at Arts et Métiers Institute of Technology in Lille, France. His is the member of the research team L2EP (https://l2ep.univ-lille.fr/en/) . His research focuses on computational electromagnetics and its application to study and design electrical devices. He is co-author of 120 international journal papers. He always has had strong collaborations with companies (EdF, Valeo, E-motors…) and is currently the coordinator of the EU project MAXIMA on the design a modular axial flux machine, its digital twin and its manufacturing process flow for automobile (https://maxima-he.eu/).
Dr. Yanhui Gao Oita University
9:00 - 9:30
Presentation: Electromagnetic properties modeling of soft magnetic material and application of harmonic balance finite element method
The accurate modelling of dynamic hysteresis loops of soft magnetic materials is important in simulating the iron loss of electrical steels and cores, especially to predict the hysteresis loops and losses under arbitrary excitations. Furthermore, more efficient numerical method coupling with the dynamic hysteresis effect should be continuously investigated. The harmonic balance finite element method (HBFEM) is efficient for the simulation of nonlinear electromagnetic field; however, it is difficult for HBFEM to directly integrate the dynamic hysteresis model. In the presentation, a dynamic hysteresis model, considering the skin effect, to predict the hysteresis loops under arbitrary excitations only with the measured sinusoidal data is introduced and validated by specimens with various types of magnetic materials. On the other side, the issues when coupling the hysteresis model into the harmonic balance finite element method are discussed. An amorphous transformer core is implemented in analyzing the effect by integrating the hysteresis model into the frequency-domain FE analysis.
Xiaojun Zhao (Professor, North China Electric Power University, China)
Xiaojun Zhao (Member, IEEE) received the B.Sc. degree in electrical engineering from North China Electric Power University, in 2006, and received the Ph.D. degree in electrical engineering from North China Electric Power University, in 2011. He is currently a professor and a vice dean in Department of Electrical Engineering, North China Electric Power University, and the vice director of Hebei Province Key Laboratory of Green and Efficient Advanced Electrical Materials and Equipment. The main research interests are: Modelling of soft magnetic materials; Computational electromagnetics; Vibration and noise simulation of magnetic materials.
9:30 - 10:00
Presentation: Some considerations on the evolution of the usage properties of magnetic materials for energy conversion devices: an overview of magnetic aging
Ferromagnetic materials are one of the key elements in the performance and energy efficiency of energy conversion devices, as in modern industrial applications and electric mobility. Precise consideration of the behaviour of these materials within these devices is necessary from the design phase, but also during their operation, particularly in a digital twin context. The presentation will focus on the irreversible effects of temperature on the magnetic properties of electrical steels. Experimental characterisation of this so-called magnetic ageing effect and an initial modelling approach of the iron loss evolution with ageing time will be presented.
Abdelkader Benabou (Professor, University of Lille, France)
Abdelkader Benabou graduated in material sciences in 1999 and received his Ph.D. degree in electrical engineering in 2002 from the University of Lille in France for research related to the modelling of magnetic hysteresis. After 2 years of post-doctoral position, in 2004 he was nominated Associate Professor in electrical engineering at University of Lille where he continued his research on the magnetic materials within the context of energy conversion devices. Since 2023, he is Full Professor at University of Lille. His current research activities are mainly focused on the characterization and modelling of magnetic materials while accounting for the impact of the manufacturing processes and electrical machine operating conditions on their properties.
10:00 - 10:20
Tea/Coffee Break
10:20 - 10:50
Presentation: Enhancing multiphysical field computation methods for industrial applications
Multiphysical fields analysis is crucial for electric power apparatus such as various kinds of motors and transformers to work as expected and free of damage. In the design and operation phases, numerical computation methods offer effective way for virtual prototyping and online monitoring. It is hence of paramount importance for the accuracy of computed field results. Although there is faster and faster hardware, the underlying numerical algorithms for field computation are also indispensable for better utilizing the hardware and provide faster simulation results. In the presentation, with reference to low frequency electromagnetic forward field analysis problems, some enhanced numerical techniques will be discussed, highlighting state of the art computational techniques and the opportunities toward digital twin technique. In particular, the detailed implementation with self-developed software packages will be presented for practical industrial applications.
Yanpu Zhao (Professor, Wuhan University)
Yanpu Zhao (Senior Member, IEEE) majored in computational mathematics and obtained his B.S. degree in 2006 from Hebei University of Technology and M.S. degree in 2009 from Nankai University. He obtained the PhD degree from PolyU in Electrical Engineering in 2015. In May 2016, he joined ANSYS Inc. (Canonsburg) as a Senior Research and Development Engineer, developing ANSYS Maxwell 3D electromagnetic field simulation software. Since July 2019, he has been a full professor in the School of Electrical Engineering and Automation of Wuhan University. His research interests include computational electromagnetics and multiphysics, optimal design of electromagnetic devices, high order and flexible finite element methods, low-frequency stable full wave Maxwell solvers, and numerical techniques for field-circuit-motion coupled analysis problems. He has published 40 journal papers and authored one monograph on low-frequency computational electromagnetics.
Prof. Xiaojun Zhao North China Electric Power University (NCEPU)
10:50 - 11:20
Presentation: Towards purely parameter-specific surrogate model construction in the context of digital twins: a tensor decomposition-based approach
In the context of digital twins, surrogate model reconstruction is essential for real-time control, fault diagnosis, and performance prediction. While data-driven techniques are widely used, their accuracy heavily depends on the availability of large, high-quality datasets. In contrast, projection-based model order reduction (MOR) techniques, such as Proper Orthogonal Decomposition (POD), preserve the structure of the physical model, ensuring greater interpretability. However, these methods often lack robustness when handling multiple varying parameters, which can compromise their accuracy and reliability in multi-parameter electromagnetic problems. To address these challenges, a hybrid approach leveraging tensor decomposition has recently been proposed. This method enables the identification of an optimal low-dimensional representation of the parametric problem, thereby enhancing the robustness of surrogate models in both data-driven and model-driven techniques. Applied to multi-parameter electromagnetic problems across various scenarios, the approach has demonstrated superior performance compared to classical methods. These results highlight its potential for advancing digital twin applications in electrical engineering.
Zuqi Tang (Associate Professor, University of Lille, France)
Zuqi Tang is an Associate Professor in the Department of Electrical Engineering at the University of Lille, France. He received his B.S. in Pure Mathematics from Wuhan University, China, in 2007, followed by an M.S. in Applied Mathematics in 2009 and a Ph.D. in Electrical Engineering in 2012, both from the University of Lille 1. After completing postdoctoral fellowships at Électricité de France (EDF) and INRIA Paris-Rocquencourt (2012–2015), he served as an Assistant Researcher at CNRS (2015–2017). Since 2017, he has held a tenured position at the University of Lille, and in 2023, he obtained the French Habilitation à Diriger des Recherches (HDR), which qualifies him to supervise doctoral research. His research focuses on numerical modeling and analysis in computational electromagnetism, with a recent emphasis on developing numerical tools for the Darwin model and exploring various numerical techniques in the context of digital twins, particularly the application of deep learning techniques in surrogate model construction.
11:20 - 11:50
Presentation: Regulation, measurement and modeling of magnetic properties of soft magnetic material and the fast computation method of electromagnetic field
Electrical steel sheets are crucial magnetic materials that constitute the magnetic circuit structure of power equipment, such as transformers. Their hysteresis and magnetostrictive properties have a profound impact on the loss and vibration performance of devices like transformers and motors. In addition, in order to develop iron core materials with lower costs and a better match between magnetic characteristics and the performance requirements of devices, magnetic powder cores have been attracting increasing attention. At the same time, as the complexity of the models and the nonlinearity of materials increase, improving the computational efficiency of electromagnetic fields becomes of great significance. In the presentation, the domain wall movement of electrical steel sheets is observed and modeled. The magnetic characteristics within a certain temperature range and under external stress are measured and estimated. Additionally, a microstructural model of magnetic powder cores is constructed based on the simulation data of the powder densification process. The effect of the regulation degree of inter-particle metallic insulation on the eddy current loss is studied. Finally, to reflect the physical characteristics of in-service electrical equipment in real-time through simulation, an adaptive model order reduction algorithm for three-dimensional nonlinear magnetic fields and a GPU-based parallel computation method are proposed.
Yanli Zhang (Professor. Shenyang University of Technology)
Yanli Zhang obtained her B.Eng., M.Eng., and Ph.D. degrees from Shenyang University of Technology in Shenyang in 1998, 2001, and 2006 respectively. From 2007 to 2008, she served as a Post-Doctoral Fellow in the Department of Electrical Engineering at Chungbuk National University (CBNU) in Korea. From 2018 to 2019, she worked as a Visiting Professor in the Department of Electrical and Computer Engineering at Florida International University (FIU) in Miami, Florida, USA. Since 2013, Dr. Yanli Zhang has been a professor at the School of Electrical Engineering of Shenyang University of Technology. Currently, she is a committee member of the Electrical Engineering Theory and New Technology Special Committee of the China Electrotechnical Society. Her current research interests encompass the theory, numerical analysis methods, as well as the design, simulation, and optimization of power conversion and transmission equipment. The main research interests are: Measurement and modeling of electromagnetic properties of ferromagnetic materials; Numerical simulation of electromagnetic field and multi-physics field; Design, simulation, and optimization of power conversion and transmission equipment.
11:50 - 14:00
Lunch (Buffet) + Break
14:00 - 14:30
Presentation: Robust design optimization: uncertainty quantification, problem formulation and numerical methodology
In traditional design optimizations, the goal is to find the global optimal solution of a performance (objective) parameter. However, imprecision and uncertainty are often inevitable in an engineering problem. It is thus possible that a slight perturbation in the optimized variables could result in either a significant performance degradation or even infeasible solutions if the performance on this point in issue is very sensitive to the decision parameters. In this regard, it is equally important to explore robust optimal methodologies in the studies of inverse and optimization problems under conditions of uncertainties in electromagnetics. This presentation will give a brief overview on uncertainty quantification and problem formulation, and report in details some advancements of our research group on the numerical solution methodologies for robust design optimizations.
Shiyou Yang (Professor, Zhejiang University)
Shiyou Yang received his M. Eng and PhD degrees in electrical engineering in 1990 and 1995, respectively. He is currently a full professor at the College of Electrical Engineering, Zhejiang university, China. His research interests include Computational Electromagnetics in both high and low frequency domains, and the application of numerical techniques in performance analysis and optimization of electronic and electromagnetic devices.
Dr. Shuai Yan Institute of Electrical Engineering, Chinese Academy of Sciences (IEECAS)
14:30 - 15:00
Presentation: Accelerating topology optimization of interior permanent magnet synchronous motor using deep generative models
The presentation reports on the simultaneous parameters and topology optimization (PTO) method for rotating machines using a deep generative mode. The search space is compressed in low dimension using a variational autoencoder to achieve acceleration of PTO. This presentation describes the results of applying the proposed method to an interior permanent magnet synchronous motor and future prospects.
Hidenori Sasaki (Dr., Hosei University, Japan)
Hidenori Sasaki received the Ph.D. degree in engineering from Hokkaido University, Sapporo, Japan in 2021. He was an Engineer with the Advanced Technology Research and Development Center of Mitsubishi Electric, Amagasaki, Japan. He has been a Lecturer with Hosei University, Tokyo, Japan since 2021. His research interests include the design optimization of electrical systems, topology optimization, and machine learning for electromagnetics.
15:00 - 15:30
Presentation: Efficient and high-fidelity physical state evaluation: a survey of meta-modeling techniques for digital twins
Real-time monitoring and predictive analysis of physical systems are essential for the realization of digital twins, yet achieving a balance between computational efficiency and model fidelity remains a critical challenge. In this presentation, drawing on case studies from our research group, we review state-of-the-art meta-modeling frameworks tailored for efficient and high-fidelity physical state evaluation, including model order reduction and different kinds of machine learning frameworks. The pros and cons of different frameworks will be discussed. Furthermore, open challenges will be analyzed and future research directions will be outlined.
Shuai Yan (Associate Professor, Institute of Electrical Engineering, Chinese Academy of Sciences)
Shuai Yan is currently an associate researcher in Institute of Electrical Engineering, Chinese Academy of Sciences. She received B.S. and M.S. degree in Mathematics from Beijing Normal University, China in 2007 and 2010 respectively. In 2014, She received her Ph.D. degree in Computational Engineering in Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany. She has authored over 30 peer-reviewed papers in international journals, and serves as a reviewer for several academic journals in the computational electromagnetics community. She also serves as a key developper for the software platform for electromagnetic and multi-physics coupling analysis, EMPbridge. The main research interests are: Algorithms and software in computational electromagnetics; Model order reduction; Artificial intelligence in scientific computing.
15:30 - 15:45
Tea/Coffee Break
15:45 - 17:15
Roundtable forum Panelists:
- Zhuoxiang Ren (Professor, Sorbonne University, France) (Moderator) - David A. Lowther (Professor, McGill University, Canada) - Hajime Igarashi (Professor, Hokkaido University, Japan) - Stéphane Clénet (Professor, Arts et Métiers Institute of Technology in Lille, France) - Shiyou Yang (Professor, Zhejiang University) - Weinong Fu (Professor, Shenzhen University of Advanced Technology) - Shuhong Wang (Professor, Xi'an Jiaotong University)
Themes: Roles, challenges and trends of numerical modeling and the impact of machine learning in the creation of digital twins
Prof. Zhuoxiang Ren Sorbonne Université
17:15 - 20:00
Dinner (Buffet)
March 27, Thursday
08:30 - 12:00
Visiting: Xiong'an New Area Jucun 220 kV Smart Substation
