Recently, resolvers have been placed close to the coil end of the motor to save space.
Therefore, leakage magnetic flux from the motor reaches the resolver, and it is necessary to accurately understand the effect of leakage magnetic flux.
It is difficult to actually measure the very small magnetic flux called leakage magnetic flux, and it is hoped that the phenomenon will be understood through magnetic field analysis.
However, a modeling method for leakage magnetic flux to the resolver has not been established, and a modeling method needs to be established.
In order to detect leakage magnetic flux, we will set a search coil in the resolver and introduce the progress of confirming the modeling method.

Linear motors are used in a variety of situations and forms, including industrial conveyance equipment.
For example, it may be used not only for linear motion but also for conveyance paths that include curves.
Additionally, since speed and position control is required, it is also necessary to consider whether the control logic will operate as intended, taking into consideration the characteristics of the equipment.

JMAG-Designer has the ability to handle arbitrary motion, and can also evaluate transport paths that include curves.
It also has a function to generate a plant model for control study, making it possible to perform highly accurate speed and position control that takes thrust pulsation into account.

This poster introduces an example of evaluating linear motor characteristics on a conveyance path that includes curves.
We will also confirm the accuracy of the plant model based on a study example of speed control of a linear motor, and introduce examples of how the plant model can be used.

Traditionally, the characteristics evaluation of induction machines has been mainly done in 2D due to the large number of steps.
As a result, the effects of skew and end rings were approximated, and the effects of the coil ends were not taken into account, causing errors with the actual machine.

In recent years, as the performance of JMAG’s highly parallel solver has improved, the use of 3D analysis has become commonplace in induction machine design. This makes it possible to evaluate the skew, end ring, and coil end in a short time even if they are reflected in the model as CAD.

This poster shows how the use of detailed 3D models in the analysis of induction machines affects the analysis results.
We will also show you how you can use the latest JMAG-Designer features such as coil templates and parallel solvers to quickly create 3D models and perform calculations.

Electrically excited synchronous machines (EESMs) offer variable magnetic flux characteristics by adjusting the field current, making them ideal for applications that demand a wide operating range.
This poster explores the unique torque, inductance, and loss characteristics of EESMs that are crucial considerations during magnetic design. A comparative analysis with PMSMs further deepens our understanding of these characteristics. Additionally, the poster highlights specific aspects of EESMs that require attention during multi-physical evaluations. Examples include an evaluation of the field winding’s temperature rise and an analysis of the stress on the rotor teeth induced by the field winding’s weight.
All the examples of this poster are designed to be easily replicated, so that you can start your own journey in EESM analysis.

Optimization calculations are essential for design automation and topology optimization is particurlarly suitable because it has a high degree of freedom in determining the shape (topology) to the design object. On the other hand, topology optimization results can be difficult to manufacture.
In this poster, we will introduce the advantages and problems of topology optimization with various analysis exsamples. We hope to disucsss the solutions presented to the topology optimization ploblems with you.

In virtual prototyping, detailed geometry modeling without simplifications is necessary to perform analysis with accuracy close to that of the actual machine.
For example, in the case of a motor, combining detailed modeling such as modeling each laminated steel sheet of the core and the peripheral components is expected to result in a number of elements reaching hundreds of millions.
The analysis of induction heating and transformers is also expected to involve similarly large scale modeling.
The pre/post processor of JMAG improves its performance to smoothly handle such large scale models.
This poster will introduce the trends in performance improvements being made to handle models ranging from tens of millions to 200 million elements.

Electrical machine design encompasses more than just electromagnetic considerations.
Depending on the intended application, thermal, structural, and electrical aspects also play a crucial role in the design process.
Despite this, the design focus often remains primarily on the magnetic aspect, especially during the initial stages.
This can lead to unnecessary iterations later on during the detailed design analysis, when checking for the machine’s thermal behaviour, demganetization, etc..

JMAG_Express offers a straightforward, ‘quick,’ and comprehensive multi-discipline optimization tool for electrical machines, even with a basic understanding of finite element analysis (FEA).
These characteristics make it an ideal platform for early-stage design.

This study will highlight the difference in Pareto fronts when neglecting the multi-discipline aspects of machine design for a specific traction machine design case.
The comparison will illustrate the necessity of this multi-discipline approach and demonstrate Express’s position as the most suitable framework for this purpose.
Moreover, the ability to seamlessly integrate the obtained results into the same interface for in-depth analysis establishes it as an indispensable tool for your entire design workflow.

Have you ever experienced a design proposal that carefully considered magnetic, thermal, and even vibration characteristics (electromagnetic force, torque, and vibration mode) of a motor, only to have it rejected in the system (the product itself with the motor built in) evaluation?
Vibration characteristics are one of the characteristics that can change significantly depending on the boundary conditions and the influence of peripheral equipment when the motor is designed as a single unit and when it is incorporated into a system.
By considering vibration characteristics at the system level during the design phase of a motor design proposal, design studies can be conducted that take into account the requirements of subsequent processes.
In this poster, we will show how system level vibration calculations are realized in JMAG.

In order to effectively utilize electromagnetic field analysis tools in design, it is necessary to ensure the reliability of the analysis.
As a means of ensuring reliability, there are two methods: checking the accuracy of the analysis settings (verification) and checking the validity of the analysis using actual measurements as a reference (validation).
During this verification process, we confirmed the torque sensitivity and accuracy of the analysis setting parameters. As a result, we found that the sensitivity of the magnetic properties of the magnet and the convergence judgment value of ICCG is high.
Additionally, during the validation process, we discovered that the delay in the control system of the actual machine and the delay in the encoder affected the target torque value.
This poster shows these contents.

JMAG has not only magnetic field analysis but also thermal analysis functions.
There is a modeling function for contact thermal resistance and cooling based on actual measurements and CFD results.
Therefore, you can proceed with thermal design even if you have no experience in thermal design.
In order to utilize simulation tools in design, it is necessary to ensure the reliability of calculation results.

This poster introduces examples of accuracy verification of contact thermal resistance and cooling models, which are elemental technologies for thermal analysis.
It will also introduce an example of comparing the results of thermal analysis and actual measurements to evaluate the temperature of a motor.

JMAG offers new functions that are more useful in design and development than ever before. This poster introduces a selection of the new features in Ver. 23.0.
Do you use JMAG-Express?
Enhanced features in JMAG-Express will let you obtain efficient design plans before running a detailed analysis. We invite both current and new users to take a look at the new features that will enable you to explore the vast design space and obtain more beneficial design plans.
Do you limit the number of cases for calculation?
Faster solvers and better distribution efficiency will let you run more calculations than before. In addition, the introduction of surrogate models allows for faster optimization calculation.
Do you have the need to account for magnetic circuits while evaluating vibration?
With the new JMAG, you can immediately check the effects of system-level vibration caused by changes in the magnetic circuits.

Let us offer better modellings of permanent magnets, which can bring improved accuracy to your electromagnetic simulations.
If you find gaps between measurements and simulations, modelling of magnets may be the factor.
The modelling might have failed to express demagnetizations of thermal or field cause, or to capture the state of magnetization in real machines.
Some applications require to even simulate the degaussing and the remagnetization in the middle of their operation.
This poster presents how to precisely model magnetization and demagnetization phenomena.

In order to calculate AC loss and take into account the magnetic flux due to the coil end, it is necessary to accurately model the coil end shape and winding arrangement.
Drawing a high-density coil end shape is difficult and coili parts tend to interfere with each other, so creating the shape is a time-consuming task.
In those cases, we recommend to use JMAG coil template. By specifying parameters, you can create a winding shape for a rotating machine.
In this poster, we will show specific examples to introduce what kind of coil end shapes can be created using JMAG coil template.
In addition, the latest JMAG has improved performance for coil end modeling response. We will also introduce it.

When you start designing a motor, you probably have already decided on the type of motor, such as a permanent magnet motor. I think this is probably determined based on past experience and existing motors.
However, there is a risk of narrowing the design space. Since you probably don’t have experience designing all types of motors, we would like to use JMAG-Express to select the appropriate motor from the many motor models.
This poster shows how JMAG-Express can be used to evaluate multiple types of motors at the same time and not only select a motor, but also smoothly lead to optimal design.

When using custom geometry in JMAG-Express, the geometry must be registered in the library in advance.
Many users have given up because the settings required for library registration are difficult to understand.
In JMAG Ver. 23.0, a function has been added to guide users through the settings required for library registration on the GUI.
By following the guide, users will be able to register custom geometries to the library without hesitation.
This poster introduces how to register a custom shape to the shape library using the guide function.
We would appreciate your direct feedback on how easy it actually is to use and whether it lacks any necessary functions.

Induction motors gather attentions recently because of the advantages that they need no rare earth materials and there are no losses at no load.
They are getting to be employed even for traction motors as control technology is progressing.

By using JMAG, you can generate efficiency maps for induction motors with realistic modelling of the control circuits which is necessary to estimate losses with high accuracy.
JMAG supports parallel computing and its generation time can be reduced with the help of HPC.

In the early stage of the design process, the speed could be more essential in order to compare many draft designs in a short time.
JMAG provides speed priority options also for such purpose.
We hope you will make full use of JMAG’s efficiency map features for designing induction motors among all design process.

Efficiency maps are crucial for evaluating PMSM performance, and JMAG can generate highly accurate efficiency maps that take into account control circuits and manufacturing deterioration. By leveraging HPC, JMAG can generate these maps in a short time.
In the initial design stage, it is necessary to quickly compare the performance of multiple draft designs. To address this need JMAG provides an efficiency map generation function that enables rapid evaluation of different design options. Multiple examples will illustrate the minimal effort and time required to produce efficiency maps for each draft design.
We hope you will leverage JMAG’s PMSM efficiency map generation capabilities to create tailored maps for each design stages.

The 6-phase pole-changing induction motor(PCIM) can expand the constant power operating range without changing the motor size by pole-changing.
Therefore, it is expected to be applied to the traction motor for EV.
The 6-phase PCIM has been added to JMAG-RT model, and it can be used for control circuit simulation.
This poster shows the JMAG-RT 6-phase PCIM model with examples.

Inverters for battery-powered motor drives use a DCDC converter to boost the inverter voltage in order to improve motor efficiency.
On the other hand, this system is large in scale, increasing costs and decreasing system efficiency.
To solve this problem, a method has been proposed in which a battery is connected to the neutral point and the inverter alone can perform both boost and inverter operation.

The JMAG-RT model of a 3-phase PMSM can now accept voltage input to the neutral point, making it suitable for such cases.
This poster presents an example that simulates both feeding power to the neutral point and charging a battery.

Do not blindly move on to results evaluation when the calculation results are available. If you do not confirm that the model is set up correctly and the calculations were performed as intended, you may end up reworking the results later. In particular, when a warning message is output at the end of a calculation, the user must judge the contents of the message and confirm that the calculation was performed as intended, rather than immediately moving on to result evaluation. This will prevent unnecessary rework and make the simulation more efficient.
In this poster, we will discuss the warning messages that users often encounter, explain the causes of these messages, and explain how to determine if a warning message has occurred.

Many of the inquiries JSOL support receives are about how to use JMAG.The purpose of analyses with JMAG is product design.
So it means that you pay costs to understand and interpret how to use JMAG.
It is not what you originally would like to do.

Also, there may be some people who have given up without asking JMAG support, or who are worried that the settings are correct even though the error no longer occurs.
On the other hand, many frequently asked questions can be answered in the Technical FAQ, allowing you to get answers without having to contact JMAG support.

For example, “The finer the mesh, the higher the stress.” or “When assigning FEM coil condition to a component with branches and energize it, current flows only one side.” In this poster, we will introduce answers to frequently asked questions, along with examples of FAQ searches.

EV shift is progressing worldwide, and an increasing number of companies are entrying the motor manufacturing business.
Accordingly, even if your major is unrelated to motor design, there is a possibility that you will be in charge of motor design after joining a company.
Characteristics such as the geometry, magnetism, temperature, etc. of the motor can be estimated by the rules of empirial, theoretical formula, and experimental formula.
However, EV drive motors are required to have unprecedented high power density and efficiency.
It may be difficult to consider using empirical rules or formulas. 　　　　
JMAG-Express performs analysis based on equations for electromagnetic fields, heat, etc., so it is possible to design motors without relying on empirical rules.
How about learning the flow of motor design at JMAG-Express before joining a company?

In addition to evaluating transfer efficiency and Q-value when using stranded wires, JMAG can also capture losses that occur in peripheral components.
Computation time has been an issue, but is solved by using direct methods and massively parallel processing (MPP).
Consideration of misalignment, which affects transmission efficiency and coupling coefficients, is also made hassle-free by geometry parametrics and passing of response values.
Increasingly, they have magnetic cores to reduce leakage inductance. JMAG can automatically optimize of core shape.
This poster introduces key features when evaluating wireless power transfer devices through analysis case studies.
On the other hand, when the number of strands is in the thousands or tens of thousands, there are challenges in modeling the geometry for Q-value calculations, handling large data sets, and calculating with a solver.
We would like to discuss whether we should overcome this issue by software performance, or should devise a homogenization method, or should consider using something other than JMAG.

Since induction heating coils are produced in small quantities, it is more effective to design using CAE rather than making prototypes each time.
There are several modeling points to keep in mind when performing induction heating analysis.
Typical examples are the following.
1) Temperature dependence of materials
2) How to generate mesh in order to capture eddy current
3) Mechanical behavior such as thermal expansion of workpiece
4) Handling of power supply

JMAG-Designer has capability to analyze induction heating with these points in mind.
The importance and modeling points for 1) to 4) are explained in this poster.

Since transformers require long-term operation, it is necessary to reduce running costs due to losses.
Thermal deterioration of insulating paper due to high temperatures will greatly affect its lifespan, so thermal considerations are also required.
In addition, it is necessary to reduce noise in consideration of the surrounding environment, so it is necessary to consider vibration.
This poster will introduce examples of iron loss evaluation, temperature evaluation, and vibration evaluation in transformers.

As high-frequency electronic components such as reactors and converters become smaller, the issue of heat generation becomes more important. Heating in a specific area occurs because the heat sources which is copper loss and iron loss have a distribution, but this is difficult to confirm through experiment, so temperature evaluation by simulation is required.

In addition, current-voltage characteristics must be satisfied depending on the application, and in order to accurately evaluate it, it is important to strictly consider the effects of skin effect, proximity effect, and magnetic flux leakage from the core gap.

Inductance is also required to have stable inductance characteristics, but for highly accurate evaluation it is necessary to consider the effects of magnetic hysteresis.

In this poster, we will introduce an example of evaluating the above issues using JMAG.

In busbar through which high-frequency current flows, the influence of skin effect cannot be ignored, and increased resistance and loss become a problem.
Excessive heat generation can cause deterioration of efficiency and damage to equipment,
so it is important to design with consideration to heat generation, temperature distribution, and the resulting thermal stress.
Additionally, electrical devices such as inverters have problems such as element destruction due to the generation of surge voltage.
The inductance of the bus bar is one of the factors, so there is a need to reduce the inductance.

In this poster, we will introduce an example of predicting loss in a busbar and the resulting temperature distribution and thermal stress.
We will also introduce an example of finding inductance.

By considering control at the magnetic circuit design stage, it is possible to consider design proposals with lower loss.
JMAG-Designer has many pre-prepared control circuit models such as vector control elements.
Therefore, it is possible to perform coupled magnetic-control without having to perform control circuit modeling from scratch.
This poster shows the control circuit elements installed in JMAG-Designer, and explains how to use them and their setting parameters.

Stricter equipment performance requirements demand a large number of calculations and models that are faithful to the actual equipment.
It becomes necessary to accelerate optimization calculations to obtain solutions in practical time.
In this poster, two approaches will be presented: acceleration by multi-stage optimization and acceleration of calculations by proxy models.
The former approach combines global and detailed exploration, while the latter suppresses computational cost by replacing some FEA with a surrogate model.
Each is effective for 3D analysis with a larger search area or tighter constraints, and for 3D analysis with a larger FEA model size.
Through several examples, we compare the performance of optimization calculations and the results of optimization calculations.
Optimization calculation with proxy models is also a new feature, and we will present the results of accuracy verification of proxy models and guidelines for settings such as the ratio of FEA to proxy models.
We would be happy to discuss the effectiveness of these approaches for actual operations.

Because electrical device design must satisfy a multitude of requirements, it requires multi-objective, multi-constraint optimization.
Although multi-point search such as genetic algorithm (GA) is effective, it is computationally expensive if the number of design variables is large.
And if the number of design variables is badly reduced, a sufficiently optimal solution cannot be obtained.
The feasible region (the design space that satisfies all constraints) is narrow and complex, and its exploration is very difficult.
This poster presents numerical experimental results on the relationship between design variables, population size, and maximum number of generations when solving multi-objective, multi-constraint problems, and demonstrates on the basis of actual cases that adequate search cannot be performed when the number of computational cases is reduced.
It is shown that the obtained design proposal fully satisfies the requirements of multi-objective and multi-constraints without relying on the knowledge and experience of skilled designers by using JMAG’s optimization functions.
Based on examples of speedups exceeding 85% at using 100 cores for many models, we show that calculations that used to take weeks or months can now be completed in hours or days.

Accurate exploration of the design space requires accuracy in each analysis. This requires a three-dimensional model, and the problems to be solved can be large.
However, the time allowed for design is limited, and many analyses must be performed within that time.
From a practical point of view at present, it is also an urgent issue to improve the speed of medium-scale problems with about 1 million elements.
This poster will present the performance of parallel computations for large- and medium-scale problems and the degree of parallelism required to evaluate them within the expected time.

Calculation accuracy and speed are strongly depend on meshes.
For example, in order to estimate the eddy current loss with high accuracy, fine meshes need to be created around surface.
However, calculation time could increase if whole parts are finely meshed.
JMAG provides meshing features for capturing the eddy currents with moderate meshes.
We demonstrate them through case studies of motor, transformer, induction heating, busbar and solenoid.

Product design using CAE increasingly involves large-scale parametric studies and optimization calculations, encompassing anywhere from 1,000 to 100,000 individual cases.
For these multi-case calculations, leveraging distributed execution across multiple cores can significantly reduce computation time. This has spurred the adoption of HPC clusters and cloud computing environments, which provide access to vast computational resources.
While they provide significant benefits for multi-case calculations, the associated intricacies of resource allocation and progress management cause many to shy away from their use.
JMAG has introduced significant enhancements not only in the core calculation process but also in simplifying the complex tasks involved in the construction and management of large-scale computation environments.
This poster will dispel any apprehensions associated with HPC cluster deployment by introducing JMAG’s features for setting up and managing HPC clusters for multi-case computations.

There is a need to improve the efficiency of electrical equipment such as motors. At that time, if we are trying to discuss a 1% improvement in efficiency in analysis, a calculation accuracy of 10% is required for loss analysis.
Based on your past experience, don’t you think that the loss does not match the actual measurement and it is difficult to calculate it accurately?
Also, have you ever tried to improve accuracy but found yourself having problems with complicated settings and calculation time?
This poster introduces techniques for improving accuracy based on loss factor analysis.
In addition, the poster shows the efforts to implement these techniques in JMAG and the easy-to-use functions.
We would like to hear your valuable opinions on the gap between the actual issues and the issues JSOL has raised, and if you have used the function, how it felt when using it.

When the wire is thin enough for the frequency, calculations can be made with high accuracy even if the current of the wire is treated uniformly.
When trying to calculate motor harmonics, kHz band wireless power transfer, and IH characteristics, eddy currents in the windings cannot be ignored.
JMAG has FEM conductor as a function to calculate eddy current in winding.
may mean is the convergence of ICCG will deteriorate, which will cause the calculation time to increase.
In this poster, we will introduce three countermeasures.
If you are calculating winding eddy currents, please come visit us.

The Play model is the effective method to calculate minor loop loss due to PWM control and DC flux superposition.
Its accuracy depends on the resolution of symmetric B-H loops.
When the loops with narrow spacing are required in the measurement, they are more likely to intersect with each other.
In this poster, we discuss the effect of loops intersection on hysteresis loss.
JMAG provides the tool to interpolate and create finer B-H loops and its effect will be introduced.

Do you have a problem that the results of electromagnetic field analysis do not match the actual measurements?
For motors with narrow air gaps and transformers with no gaps, material properties have a significant impact on the analysis results.
If ideal conditions are used for magnetic properties, the forces and currents obtained in the analysis will be larger than the actual measurements.
In this section, we will discuss modeling techniques for magnetic properties such as anisotropy, occupancy of electromagnetic steel, building factors, and B-H property extrapolation in order to represent conditions closer to those of the actual device in the analysis.
In addition, a new material database framework for obtaining material data will be presented.

JMAG-RT is a fast and accurate plant model based on FEA, and is being increasingly used in model-based design.
In this poster, we will show through examples that JMAG-RT can be utilized for processes of model based design (component design, control calibration and system-level simulation) according to their respective purposes.

Simulations have become an essential part of the design and engineering process for electrical machines.
However, there are still lingering doubts regarding the reliability of simulation results.
This is especially true for the simulation of traction machines, as exemplified by the HEFSM example in this study.
Achieving an efficiency map of the HEFSM with an error margin of less than 1% compared to the experimental map is not an easy task.

In this case study, we will concentrate on the 3D effect on the FEA results.
The results comparsion over multiple points of the effiency map show that the neglecting 3D, because of its FEA cost, is not valid approch for accurate validation of a machine.
Each loss contribution is examined seperatly and its error against the measurements and 2D FEA are evaluted.

By addressing these issues, we can improve the reliability of simulation results and ensure that they are a valuable tool for the design and engineering of electrical machines.

Efficiency maps play a crucial role in assessing the performance of electrically excited synchronous motors (EESM) across a broad range of operating conditions. Unlike PMSMs, which rely on fixed permanent magnets for field excitation, EESMs employ adjustable field currents, granting them a broader range of control options. JMAG can generate EESM efficiency maps that can include realistic modelling of the control circuits and manufacturing deterioration. By leveraging HPC, JMAG can generate these maps in a short time.
In the initial design stage, it is necessary to quickly compare the performance of multiple draft designs. To address this need JMAG provides an efficiency map generation function that enables rapid evaluation of different design options. Multiple examples will illustrate that like for PMSM, the effort and time required to produce efficiency maps for each draft design is minimal.
We hope you will leverage JMAG’s EESM efficiency map generation capabilities to create tailored maps for each design stage.

One factor that determines the upper limit of motor output is temperature.
Temperature is determined by heat generation and cooling, so it is important to consider these factors when designing electrical equipment.
If you perform magnetic design and thermal design separately, you may end up with a design that satisfies one requirement but not the other.
In that case, it may be necessary to rework the design or make it difficult to push the limits of the design.
Therefore, we believe that it is necessary to narrow down design proposals while simultaneously evaluating magnetic properties and temperature from the initial design stage.

Temperature estimation is greatly influenced by contact thermal resistance and cooling (air cooling, water cooling, oil cooling).
Contact thermal resistance and cooling had to be determined empirically or using CFD for calculation.
JMAG has a modeling function for contact thermal resistance and cooling based on actual measurements and CFD results.
Even if you have no experience in thermal design, you can use it just by inputting the design specifications. This allows thermal design to proceed at an early stage of development.
This poster will introduce modeling functions for contact thermal resistance and cooling, and examples of their accuracy verification.

One factor that determines the upper limit of motor output is temperature.
Temperature is determined by heat generation and cooling, so it is important to consider these factors when designing electrical equipment.
JMAG has not only magnetic field analysis but also thermal analysis functions.
In order to utilize simulation tools in design, it is necessary to ensure the reliability of calculation results.

In this poster, we will introduce an example of comparing JMAG’s thermal analysis results and actual measurement results regarding motor temperature evaluation.
Let’s discuss the reliability of JMAG’s thermal analysis with us.

Structural analysis of an electrical machine is essential for optimal design of the machine. This is particularly true for high-speed or large rotary machines where the stress due to the centrifugal force could lead to the failure of a poorly designed machine. In addition, the evaluation of mechanical stress is needed for accurate prediction of iron loss in the machine.
Accurate evaluation of stress in permanent magnet (PM) motors requires a nonlinear structural analysis by applying contact condition between the magnets and the rotor core.
Furthermore, when there is an interest in the material behavior after the yield or when the occurrence of plastic strain is a concern, the nonlinearity of the material should also be taken in to consideration.
JMAG with its advanced features can be used for accurate structural analysis, enabling the users to carry out magnetic and structural analysis of the machine without switching to other software products for mutliphysics calculation.

In this study we introduce contact condition and material nonlinearity features of JMAG and present the structural analysis of some typical motor models with contact condition and nonlinear material.
We will compare the results of a model with linear material and contact condition with the same model in which the contact condition is replaced with rigid connection, to show the importance of applying contact condition.
Similarly, the result of a model with nonlinear material (without contact) will be compared with that of a linear material.
Moreover, the results and the calculation times will be compared with those of a commercial software to demonstrate that JMAG is in par with famous commercial softwares.

At the conceptual design stage of a motor, it is desirable to conduct a global search for a design proposal that satisfies requirements beyond magnetic design alone, but due to unfamiliarity with various types of analysis, experience in using analysis tools and time constraints, multifaceted evaluation from this stage is not yet widely practised.
In fact, if magnetic design alone is used, some may have experienced rework due to design proposals that do not meet the requirements in terms of heat and stress.
We believe that in the conceptual design phase, it is necessary to be able to carry out multifaceted evaluations simultaneously with a single analysis tool, to be able to consider the necessary requirements and to be able to carry them out with a certain degree of accuracy and without spending a lot of time.
In this poster, we present the JMAG-Express Stress Scenario, which allows analyses to be performed simultaneously with magnetic design and calculates the maximum stresses that can lead to plastic deformation in around one second.
It will be shown that even customers who have not been able to tackle multidimensional evaluations including stress due to lack of tools and experience can easily do so using JMAG-Express.
In addition, the calculation accuracy and time results will be shown and the mechanism that enables such fast calculations to be achieved will be explained in detail.
We would like to receive your honest feedback on how the functionality and performance of JMAG-Express Stress Scenario can contribute to your conceptual design of motors.

In motor development, it is necessary to pay attention not only to magnetic characteristics but also to insulation breakdown.
JMAG-Designer is equipped with functions for rapid insulation evaluation, such as a geometry modelling function that easily creates the insulation coating required for insulation evaluation and an electric field analysis(Speed Priority Mode) that also instantly executes the analysis.
This poster introduces methods for fast insulation evaluation using these functions, together with calculation time and accuracy. In addition, the details of the functions behind them will also be explained.
We would appreciate your feedback on the usefulness of these functions in JMAG-Designer for insulation evaluation in motor design work.