JMAG Users Conference
Japan 2026

Conventional, parametric design approaches limit the solution space to predefined geometric parameters. Topology optimization expands this space through the free distribution of material within a design domain, enabling more capable designs. Since this requires significantly more designs to be calculated, a larger number of licenses (PSL vs standard) is needed to reach an optimal result within a reasonable optimization timeframe.

In the application scenario "Air Pocket Optimization," the optimal distribution of air pockets within the rotor is investigated with the magnet in a fixed position. The model setup, objective functions, and boundary conditions are presented, along with an evaluation based on a reduced-order model using version 24.2.

Finally, the topology-optimized design is compared with the parametric reference design based on key KPIs. The conclusion and outlook focus on the combined optimization of air pockets and magnet position.

Recent advances in reinforcement learning and imitation learning are accelerating the deployment of robots that operate in close proximity to humans. Toyota has been developing a variety of robotic systems, including the basketball robot CUE and ELEY, a mobile manipulator capable of learning and reproducing human tasks through demonstration. To enable safe and agile operation in human environments, these robots require actuators that simultaneously achieve high backdrivability for compliant physical interaction and high instantaneous torque for human-like dynamic motion.
This presentation introduces our motor development efforts aimed at meeting these challenging requirements. Focusing on Halbach-array outer-rotor motors, we discuss design approaches for balancing competing objectives such as torque output, rotor inertia, cogging torque, and resistance to demagnetization. Practical design examples, prototype evaluation results, and lessons learned through electromagnetic analysis are presented. In addition, future directions for motor technologies supporting next-generation human-centric robots are discussed.

In recent years, the practical adoption of next-generation power devices using SiC and GaN technologies has accelerated the increase of switching frequencies in power converters. This trend is expected to improve converter efficiency and reduce the size.
On the other hand, transformer design for power converters faces issues such as the impact of parasitic capacitance on EMI characteristics and design constraints due to miniaturization. Considering these factors during the design process often requires significant engineering effort.
This presentation introduces a design exploration case study that utilizes optimization techniques incorporating transformer parasitic capacitance, together with coupled electromagnetic, thermal, and structural analyses, to achieve an optimized transformer design.

In electric motor development, with the growing risk of rare earth supply shortages, design risks related to the impact of demagnetization on performance are increasing. Meanwhile, conventional design processes are limited to “verifying the performance impact of switching to alternative magnets,” and optimizing designs using these alternatives requires significant effort.
This presentation will introduce a case study demonstrating how to perform optimization calculations for magnet shapes based on the characteristics of each magnet grade by utilizing JMAG’s optimization and thermal analysis functions.
In addition, we defined how to use optimization functions during the concept and detail design phases as part of our efforts to standardize our motor design process. We will also present a case study on the development of an optimization design process.

N-T characteristics are a critical design benchmark in motor development. However, the design process typically relies on engineers in charge of designing the magnetic circuit to not only run the magnetic field analyses but also organize and post-process the analysis results to obtain the N-T characteristics. To simplify this process, we configured an environment using JMAG scripts that automates N-T characteristic calculations. Our environment takes advantage of JMAG-Express rather than JMAG-Designer as well to enable even those new to JMAG to specify the condition settings and run evaluations. The framework launches the JMAG scripts to automatically calculate the N-T characteristics when JMAG-Express loads the analysis results. One single workflow from running evaluations to calculating the N-T characteristics minimizes the workload and makes calculations previously limited to engineers responsible for the magnetic design accessible to other designers and engineers. This case study provides an overview of the calculation environment, the configuration process, and specific use cases.

Axial flux motors offer high design flexibility due to their three-dimensional structure, making the application of optimization analysis challenging. This presentation introduces case studies of electromagnetic and optimization analyses for axial flux motor design. In addition, a material selection study using a newly available feature that treats materials as discrete variables is presented, and the potential of applying optimization analysis to material selection is discussed. Furthermore, examples of optimization studies that consider not only motor performance but also factors such as cost as objective functions are introduced.

Iron loss properties of materials alone cannot precisely predict losses in DC-biased reactors because the DC magnetization distorts the hysteresis loop. This case study uses a play model to estimate the DC-biased B-H characteristics of our MH20D pure iron powder core material as well as run hysteresis loss analyses. These analysis results that closely match measurements prove the effectiveness of our approach for loss prediction in DC-biased reactors. This case study also took advantage of this analysis method to evaluate and optimize reactor designs capitalizing on the unique characteristics of pure iron powder magnetic cores. Our presentation will show some examples of the material selections and reactor designs used for these analyses as well.

In recent years, optimization has become important in induction motor design to improve efficiency and satisfy various design requirements. However, FEA-based optimization requires significant computation time, and the computation time increases rapidly as the number of design variables increases. In addition, nonlinear characteristics caused by magnetic saturation and leakage flux can reduce the prediction accuracy of surrogate models and make optimization results less reliable. This presentation presents an approach to accelerate the optimal design of induction motors by developing a surrogate model trained using historical design assets. It also presents methods for reducing prediction errors caused by nonlinear characteristics. The improvement in surrogate model accuracy and the effectiveness of the surrogate model in optimization is evaluated.

We are engaged in the research and development of high-temperature superconducting induction motors featuring squirrel-cage rotor winding made of high-temperature superconductors. To date, we have demonstrated outstanding characteristics, including a peak efficiency exceeding 99.5% and rotational stability during rapid acceleration and deceleration. Furthermore, we are advancing multiple application development projects based on these fundamental research findings through industry-academia collaborations; for instance, our high-temperature superconducting submerged pump for liquid hydrogen has reached a practical level of viability.
　　To facilitate the practical implementation of high-temperature superconducting induction motors, precise analysis and design technologies must be established. The characteristics of high-temperature superconductors are defined by a constitutive equation relating electric field and current density; therefore, it is essential to establish an analysis method that accurately incorporates this nonlinear equation.
　　This presentation introduces electromagnetic field analysis results for a practical high-temperature superconducting induction motor, obtained using JMAG®-Designer while accounting for nonlinear electric field vs. current density characteristics. It also compares these analysis results with experimental data and discusses their quantitative accuracy. Finally, based on these findings, the presentation offers an outlook on the potential and future of high-temperature superconducting induction motors.

Stationary-type magnetic refrigeration systems for hydrogen liquefaction are attractive because they operate without moving components. However, the requirement to energize superconducting coils with alternating current results in AC losses that significantly affect the thermal load and overall system efficiency. This presentation reports the evaluation of AC losses in superconducting coils for a magnetic refrigerator using JMAG. The validity of the analysis methodology was verified through comparison with experimental coil results, and a conceptual study of coil models for a future-scale system is also presented.

［TBA］

The conventional method to analyze the operation of machines that leverage electromagnetic force uses JMAG to obtain the electromagnetic force of parts in specific orientations for import into RecurDyn multi-body dynamic analysis software. However, this approach cannot precisely capture variations in the electromagnetic force as the orientation of the parts changes, lowering the analysis accuracy. Our solution to maintain accuracy configures a coupled analysis that shares the electromagnetic force and part orientation between JMAG and RecurDyn through small time steps. This case study outlines that analysis method and demonstrates how it improves the analysis accuracy.

In superconducting coils, accurate evaluation of transient phenomena such as current decay, heat generation, and temperature rise after a quench is important for coil protection and the design of operating conditions. In this study, the quench transient behavior of a superconducting coil was numerically analyzed using JMAG. An analysis model was developed to evaluate the temporal changes in coil current, dissipation of electromagnetic energy, heat generation, and temperature. The validity of the numerical approach was also examined through comparison with experimental results. In addition, as an extended study, a system including a magnetically coupled secondary coil was analyzed to investigate the influence of energy transfer on the transient response during a quench.

Motor requirements for next-generation robots and mobility systems are first identified. A concentrated-winding SPMSM is then optimized for minimum weight under multiple combinations of continuous- and peak-torque requirements, considering electromagnetic, thermal, and demagnetization characteristics. The relationship between the torque requirements and the minimum achievable motor weight, together with the governing constraints, is clarified and summarized as a motor weight characteristic map for machine and motion design.

Recently, demand for Synchronous Reluctance Motors (SynRMs) has been growing to achieve high efficiency and mitigate rare-earth price volatility and supply risks. Although parameter and topology optimization are widely applied to flux barrier shapes governing SynRM performance, the impact of manufacturing variations is often neglected. Conventional optimization considering variations requires repeated Finite Element Analysis (FEA) via Monte Carlo simulations, incurring prohibitive computational costs.
To overcome this challenge, we propose an offline optimization workflow that combines a Gaussian Process Regression surrogate model with Polynomial Chaos Expansion. Using SynRM design as a case study, this presentation reports an optimization that accounts for manufacturing variations while reducing computation time to less than 1/10 of the conventional Monte Carlo method.