A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing: Preprint

Research output: Contribution to conferencePaper


Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as 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 International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The free-form boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.
Original languageAmerican English
Number of pages10
StatePublished - 2022
Event2022 Joint MMM-INTERMAG Conference - New Orleans, Louisiana
Duration: 10 Jan 202214 Jan 2022


Conference2022 Joint MMM-INTERMAG Conference
CityNew Orleans, Louisiana

Bibliographical note

See NREL/CP-5000-84010 for paper as published in proceedings

NREL Publication Number

  • NREL/CP-5000-81583


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


Dive into the research topics of 'A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing: Preprint'. Together they form a unique fingerprint.

Cite this