JMAG provides a variety of features to powerfully support optimized design.
Large numbers of design proposals can be evaluated efficiently using JMAG’s parametric analysis and optimization functions.
In addition, results analysis and sensitivity analysis functions offer points where design can be improved.
- Large numbers of design proposals can be evaluated efficiently using JMAG’s parametric analysis
- Confirmation of effects to the evaluation value when changing the design variables and characteristics evaluation are possible.
- Run calculation with only two steps: selecting design variables and registering the design proposal.
- Create a response graph and evaluate results.
Example 1:Parametric analysis with design variables set as geometry dimensions
Verify the results by response graph
Inductance analysis of choke coil
Example 2:Characteristics evaluation
Verify characteristic of multiple cases by graph
Evaluation of torque characteristicsof IPM motor
Procedures of parametric analysis
JMAG Function Videos
- Better optimization calculations in JMAG-Designer without sacrificing usability!
- Optimization calculations can be done simply by utilizing the settings in parametric analysis without taking difficult steps
- An easy setup by selecting only the design variables, objective functions, constraints, and optimization engine
- From setup to result verification in three simple steps
- Verify the case and value of the optimum solution from the solution distribution on the spot
- Easily move from there to the the analysis case
- It is also possible to directly verify the response surface of the objective function for the design variable, pareto curve and correlation matrix
Results displayed on response surface
- Optimization algorithms can be chosen to match the features and environment of the optimization design
- Quadratic response surface method, Genetic Algorithm, Multi-Objective Generic Algorithm or MATLAB global optimization can be chosen
- An algorithm appropriate for optimization which matches the unimodal/multimodal and single-objective/ multi-objective characteristics of the design space can be chosen
- The Global Optimization Toolbox in MATLAB can now be used as an optimization engine
Motor torque optimization based on Genetic Algorithms
Explores design proposals that minimizes torque ripple and maximizes average torque
Confirms correlation between design parameters and objective functions