News

[Back]

Explaining FEA Effectiveness of FEA in the Development Process

Issue 2 Reproducing Phenomena Authentically using FEA

Electromagnetic field finite element analysis (FEA) has been rapidly expanding as a tool used in the development process over the last 15 years.
The application of FEA varies based on the needs of each development process, but why has FEA expanded so rapidly as a tool for development? In addition, what are the advantages of using FEA in the development process?
Impact of FEA on the Design Process will introduce how FEA has effected the development process from multiple perspectives over the next year.

1. Preface

In the last issue, the prevalence and background for finite element analysis (FEA) showing the amazing advances in recent years was introduced. The ability to authentically reproduce physical phenomena using analysis calculations is one of the reasons FEA has so widely penetrated the design process.
This issue looks at the features of FEA that are capable of achieving this high reproducibility.

2. Achieving a High Reproducibility from the Perspective of the Resolution

FEA expresses models as mesh, which is a collection of elements dividing the analysis target. In addition, FEA can express the data required for analyses including point sequences for input waveforms varying by time and material properties that have nonlinear characteristics. FEA is able to run analysis with a high resolution by increasing the detail of the mesh and input waveforms allowing the physical phenomena to be reproduced highly authentically.
This section delves into the reasons FEA is capable of attaining this high resolution from the following 4 aspects of the analysis:

  1. Creating the model geometry
  2. Defining the governing equations
  3. Specifying the material properties
  4. Specifying the drive conditions


2-a. Creating the Model Geometry

The geometry of the electromechanical machines is wrapped in innovations to obtain the desired output while considering the various restrictions. For example, the gap structure between the rotor and stator have tremendous effects on the output characteristics of motors and this is one aspect of the design requiring a vast amount of experience. The characteristics are also largely affected by small geometrical differences in the primary magnetic pathways of motors using magnetic saliency, such as reluctance motors. The cogging torque of a motor that has pits in the tooth ends is largely reduced when compared to geometry without pits, as indicated by Fig.1.
The magnetic resistance for each part making up the magnetic circuit is obtained using integral calculations in the magnetic circuit method often used in simplified design, but the number of calculations greatly increases for the parts required to gain higher accuracy if the geometry is complicated. Therefore, the intuition and experience of the thermal designer is indispensable when selecting the parts required for the preliminary calculation. There are also restrictions to the geometry that can be handled because there is geometry that makes calculating the magnetic resistance challenging in elementary integral calculations for complex geometry.
On the other hand, FEA can use the geometrical data from the CAD diagram to create models.
FEA defines the geometry as mesh that is a collection of elements divided into the finite element space for the analysis target (see Fig.2). The mesh model of the analysis target does not rely on selecting the geometry of the analysis target or the skill of the engineer because mesh can be generated using automatic mesh generation features if the geometrical data is available.

Fig.1. Comparing cogging torque for geometry with and without pits on the teeth ends
Fig.1 Comparing cogging torque for geometry with
and without pits on the teeth ends



Fig.2 Geometry of an electromagnet model and mesh after discretization
Fig.2 Geometry of an electromagnet model
and mesh after discretization



Fig.3 Magnetic flux density distribution of each element for the electromagnet model in Fig. 2.
Fig.3 Magnetic flux density distribution of each element
for the electromagnet model in Fig. 2.

next    Defining the Governing Equations





Contents

  1. Implementing JMAG
  2. Product Report
  3. Model-based Development
  4. Explaining FEA Effectiveness of FEA in the Development Process
  5. Fully Mastering JMAG
  6. Exhibition Report


Contact US


Free Trial
JMAG-RT Motor Model Library

Application Catalog

Video