What if you deployed a time- and money-saving software application, but people in your company were reluctant to use it because they felt they lacked the necessary expertise?
As technology becomes more complex, this scenario is unfolding in businesses worldwide. In-depth understanding of design simulation software, for example, is often only available in a single department or from one or two experts, and convincing others to use new platforms can be a tough sell. Even engineers and designers experienced with other sophisticated programs might push back, preferring to work only with the technology they know best.
But it isn’t necessary to be an authority on the underlying technology to take advantage of the benefits that simulation software offers. That’s what democratization is about: making software accessible in such a way that all users can run it successfully without deep knowledge.
Ten times more simulations in less time
Schaeffler Group designs advanced systems and components for the automotive industry, including powertrains for electric and hybrid cars. A recent democratization effort by the e-machine engineering methods and simulation team – which supports other Schaeffler departments during new tool deployment – centered on creating a computationally efficient workflow that will enable multiphysics, multi-objective design optimization of the electric machine.
Specifically, the group coupled Ansys Motor-CAD, an electric motor design software tool, with Ansys optiSLang, a platform for process integration and variation analysis. The result was a ready-to-use, duty-cycle-based optimization methodology that will help product designers identify the relationships between the e-machine’s behavior and specific geometrical parameters under real-world conditions.
Now Schaeffler designers and engineers enterprise-wide have a better design-to-validation workflow for electric machines, which should reduce reliance on expensive physical prototyping. A meta-model enables a substantial increase in the number of optimization iterations that can be performed, in a fraction of the time required when performing simulations on a finite-element model directly. In an era of energy transition, where electrification initiatives are on the rise and demand for powerful yet affordable e-machines is growing alongside them, this will enable Schaeffler to satisfy customer demand faster and better than ever.
Overcoming barriers to software adoption
Schaeffler products represent a staggering array of processes and disciplines: different aspects of physics, various thermal states, mechanical engineering and electrical engineering, among many others. With the ability of Ansys Motor-CAD to produce multiphysics simulations across the full-torque operating range, it is easy to see how the software could fit into the Schaeffler toolkit. In particular, it would allow designers who have expertise in one area – electromagnetics, for example – to performance map the entire electrical and thermal behavior of electric machine designs without having to acquire additional knowledge.
Among other activities, the Schaeffler simulation department develops standalone tools and workflows that help acquisition teams quickly perform calculations and display specific results. It is also involved in evaluating the potential of existing software and promoting it within the company.
A few years ago, the department turned its attention to Ansys Motor-CAD. Since the tool’s potential was evaluated and its potential promoted within the company, the number of users at Schaeffler has increased. Part of Motor-CAD’s popularity can be attributed to a meta-model-based approach developed by Schaeffler simulation engineers that coupled Ansys Motor-CAD and Ansys optiSLang to optimize the placement of magnets within an electric drive in the early design phase. Magnets help determine the mechanical power within the drive, so optimizing their volume, shape and other variables serves two purposes. First, magnets are extremely expensive; using the smallest magnets that meet performance requirements helps to reduce the overall cost of the electric machine. Second, geometry optimization helps to minimize performance-altering iron, magnet and AC or DC winding losses in the electric drive’s rotor and stators during specific drive cycles.
Evaluating 100 different geometries
Regardless of the algorithms engineers choose, running an optimization involves multiple iterations, and that can take considerable computational time. It’s not unusual for a single finite-element model simulation to take as long as 35 minutes.
To generate results far more quickly, Schaeffler computed a meta-model in Ansys OptiSLang and then used the software to also run the optimization. This enabled the team to run thousands of optimization iterations within minutes.
Schaeffler simulated 100 different geometries in all, evaluating the effect of each design variable, including magnet thickness, at every operating point. The meta-model also accounted for total loss of energy over a given duty cycle, which was one of the optimization objectives. Using one of the duty cycles embedded in Ansys Motor-CAD, simulation engineers were also able to assess integrated heat losses over the entire duty cycle.
The confidence to do more, including satisfying customers
During pre-processing, the goal was to define the relationship between shaft torque and rotor speed as design variables (input values or parameters) changed during different duty cycles and during peak or continuous operation.
Schaeffler then used Ansys Motor-CAD to model changes in torque at defined operating points against changes in various input parameters: slot depth, magnet thickness and width, bridge thickness, magnet array angle and pole arc. Using one of the duty cycles embedded in Ansys Motor-CAD, Schaeffler’s simulation engineers were also able to assess integrated losses, including those in the form of heat, over the entire duty cycle. Because customers will expect each of the operating points to deliver at least a certain value of torque, the input parameters provided constraints for the optimizer.
At this point, the simulation engineers analyzed the relationships between input and output values in Ansys optiSLang. As the simulation software allowed them to generate meta-models with accuracy greater than 95%, it gave Schaeffler the confidence needed to compute all the optimization samples for the required algorithms, and to do so without having to repeat any simulations in Ansys Motor-CAD.
The simulations group also verified the best magnet design to fulfill performance requirements while minimizing total losses and magnet volume. The result, a combination of specific magnet dimensions, represented a 23% improvement in magnet volume compared with the initial design.
In the end, this work will translate directly into greater customer satisfaction: when an automotive manufacturer requests optimization of a particular operating parameter, Schaeffler can deliver the most promising concept faster than ever.
Staying up to speed
By coupling front-loading methodologies, Schaeffler created an efficient and flexible tool that enables product designers and engineers to use the same optimization workflow for any electric machine concept – and to have results in minutes. Instead of it taking 50 hours to simulate 100 samples using FEA, the meta-model-based optimization can simulate 10,000 samples within minutes.
The meta-model also eliminates the usual steep learning curve associated with new software, so it helps engineers and designers be more productive right away – and stay up to speed with their automotive customers.
