The mixing process is an integral component of the process industry, with a wide range of applications utilized to create tailored products that meet the diverse needs of various industries and customers.
Computational Fluid Dynamics (CFD) simulation products are for engineers who needs to make better and faster decisions and can help reducing the development time and efforts while improving your product’s performance and safety.
Materials information is crucial in engineering and manufacturing as it enables informed decisions. In simulation and modeling, precise materials data is needed to accurately predict real-world behaviour.
Since day one, our customers have been at the centre of our focus. Whether we’re taking care of our existing users or onboarding new customers into our yearly care cycle – quite simply – nothing is more important to us than you, our customer.
At EDRMedeso you learn from some of the industries top experts in their respective fields. With over 1500 collective years of experience in simulation, we provide a host of training sessions to suit your organizations needs
Electric motors come in all sizes — from the enormous motors that propel ships, to the miniature motors used in medical devices. Whether designing an electric motor for an electric vehicle that needs to be small, efficient, and quiet; or for an industrial application where size and sound are not of major concern, it is critical to simulate electric motors early in the design process. Simulation enables design engineering teams to better understand potential electro-mechanical thermal challenges before investing in expensive prototyping and manufacturing.
Electrification is Hot, But Engineers Need to Keep Motors Cool
An electric motor is a complex multiphysics system that requires simulation from the start to achieve an optimized design. The demand for smaller motors capable of greater power output is driving this complexity. One of the main concerns with providing more power in a smaller form factor is thermal management.
If the temperature is not controlled, materials can exceed their normal operating temperatures and experience phase change, softening, melting, or other forms of degradation. Beyond the obvious safety risks of thermal stresses that can cause fatigue, cracking, and material deformation, modern materials can be expensive. For example, some electric motors use rare earth magnets that can overheat to the point that they become demagnetized.
Use and Integrated Engineering Workflow for Multiphysics
Ansys offers an engineering workflow that progresses from electric motor design sizing options to detailed electromagnetics and thermal and mechanical analyses of the motor. The workflow encompasses mechanical and thermal simulation software, along with Ansys optiSLang process integration and design optimization (PIDO) software.
With optiSLang, engineers can:
Combine different physics into a multidisciplinary approach to investigate phenomena more holistically.
Standardize and share simulation processes across teams, allowing simulation novices to gain more direct access to simulation.
Accelerate time-consuming manual searches for the best and most robust design configuration, enabling engineers and designers can gain a better understanding of their design via interactive visualization and artificial intelligence (AI) technologies.
The optiSLang platform connects Ansys, third-party and in-house tools to automated simulation workflows.
Case in Point: Electric Traction Motor Simulation
Thermal simulation of electric traction motors requires more than one solver, which typically provides greater accuracy at the expense of speed. Co-simulation performance can be significantly improved by introducing metamodels to the solver, which only marginally increases the time to obtain results while improving the quality of the model.
Register for the upcoming webinar “Improvement of Thermal CFD EM-Models by Introduction of FMU Sub-Models” to learn how ZF Friedrichshafen AG applies co-simulation to more precisely evaluate electric traction motor system design. The generation of metamodels is performed by introducing simplified sub-models into an optiSLang sensitivity study and extracting a functional mockup (FMU) directly from optiSLang. The FMU can then be implemented directly into the thermal simulation.