Designing a modern electric motor for an electrified automobile requires striking the perfect balance between cost, weight, and performance. From a modeling and simulation standpoint, predicting the overall performance of the motor requires multiple multi-disciplinary analyses, including electromagnetic, thermal, and stress analyses.
The traditional CAE process, which consists of setting up a model, running the simulation, post-processing data, reviewing results, extracting the outputs of one simulation that are inputs into another, and iterating until an acceptable design is achieved is simply not possible when the validation of the design requires this many models and types of solvers.
ModelCenter’s powerful integration capabilities provide engineers with the ability to quickly build automated multidisciplinary workflows. In this example ModelCenter was used to solve the multidisciplinary problem of the design of an electric motor for an automobile and brought together electromagnetic, thermal, and structural performance criteria in a single workflow so that all the performance aspects and constraints can be simultaneously considered to design an efficient motor. The individual disciplines are evaluated using Altair solvers – Flux Motor (initial baseline motor design), Flux (EMAG), and OptiStruct (thermal & stress analyses).
As a brief introduction, ModelCenter is a framework for model based engineering and allows users to:
- Automate any modeling and simulation tool
- Integrate these tools together to create repeatable simulation workflows
- Set simulation parameters
- Automatically execute workflows
- Streamline the development of complex systems by connecting systems architecture and requirements to modeling and simulation tools
The general design and optimization process includes the following steps;
- Generate a baseline design using FluxMotor
- Define the Design of Experiments (DoE) to explore the design space
- Run the DoE (using Flux for EMAG, and OptiStruct for thermal & mechanical analyses)
- From the results of the design space, create reduced order models that will replace the physics-based in the multi-disciplinary analyses so the workflow can be executed much faster, thus making optimization feasible
- Set-up the optimization problem
- Run the optimization problem to come up with the optimum design;