MADE3D: Enabling the Next Generation of High-Torque Density Wind Generators by Additive Design and 3D Printing

Latha Sethuraman, Ganesh Vijayakumar, Shreyas Ananthan, M. Paranthaman, Jonathan Keller, Ryan King

Research output: Contribution to journalArticlepeer-review

5 Scopus Citations

Abstract

Direct-drive wind turbine generators are increasing in popularity, thanks to recent project developments—especially offshore, where reliability and efficiency are major cost drivers. Yet, high capital costs are forcing many original equipment manufacturers to consider lightweight, high-torque density generators for next-generation multi-megawatt turbines that may be difficult to realize by traditional design or manufacturing methods. In this study, we present a new design framework enabled by advanced machine learning and multimaterial additive manufacturing to perform a magnetic topology optimization that maximizes the torque per rotor active mass for a 15-megawatt direct-drive permanent magnet wind generator. A comparison of the proposed approach against conventional topology optimization demonstrated a significant increase in computational efficiency and accuracy in performance predictions. Results using single and multimaterial compositions for rotor core and magnets identify a wider choice of 3D printable designs for a given specification. A hybrid combination of sintered and dysprosium-free polymer-bonded magnets shows good potential for torque performance by saving material costs up to 8.75%. More than 30% improvement in rotor torque densities is identified which can marginally improve the overall generator torque density. With the rapid evolution of multipowder deposition technolgies, this study can greatly inspire a new paradigm for design-driven manufacturing with novel material compositions and lightweight, low-cost, high-strength multimaterial geometries that were previously unexplored for direct-drive generators.

Original languageAmerican English
Pages (from-to)287-311
Number of pages25
JournalForschung im Ingenieurwesen/Engineering Research
Volume85
Issue number2
DOIs
StatePublished - Jun 2021

Bibliographical note

Publisher Copyright:
© 2021, Springer-Verlag GmbH Deutschland, ein Teil von Springer Nature.

NREL Publication Number

  • NREL/JA-5000-78502

Keywords

  • additive manufacturing
  • cost model
  • direct drive generators
  • lightweighting
  • machine learning
  • topology optimization

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