JMAG Designer | Integration Node
JMAG-Designer analyzes physical phenomena including complicated geometry, material properties and heat and structure characterization of electromagnetic fields. Using the JMAG node enable users to handle input and output scalar parameters and, in particular, to accurately set and enhance the following dimensions related to the model:
- Specific JMAG parameters handling: mesh props (size), boundary conditions (current frequency, c. amplitude and c. phase, torque, revolution speed), study properties (steps, time step, step divisions);
- Material management: besides physical properties such as density, conductivity and resistivity, each material component can be changed;
- Geometrical parameters: JMAG offers both an internal CAD tool and a bridge to external CAD software (JMAG Communicator); during the introspection step, modeFRONTIER reads both kinds of CAD parameters and allows for the optimization of the electromagnetic and the geometric models at the same time;
By integrating the JMAG-Designer solver into the automation and optimization workflow of modeFRONTIER, the user can exploit both software capabilities and efficiently reach the design objectives specific of the electromagnetic analysis. Electrical motor system enhancement is one of the more suitable applications: coupling the two software enable maximization of efficiency and torque and minimization of noise, weight and torque standard deviation and losses such as joule loss, hysteresis loss or iron loss. Beside the parallel execution capability, modeFRONTIER is able to manage multiple models and discipline analyses by inserting a unique JMAG integration node, thus allowing for an in depth comprehension of complex systems.
- Improving product performance Increase quality and robustness despite uncertainties.
- Understanding crucial cost/performance factors Implement rational decision process between trade-off solutions.
- Reducing product development time Exploit CAD/CAE tools already in use together with high quality response surface models using experimental data.
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