A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Research output: NRELPresentation


Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.
Original languageAmerican English
Number of pages15
StatePublished - 2022

Publication series

NamePresented at the 2022 MMM-Intermag Conference, 10-14 January 2022, New Orleans, Louisiana

NREL Publication Number

  • NREL/PR-5000-81582


  • additive manufacturing
  • Bezier curves
  • parametric design
  • topology optimization


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