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MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing

MADE3D: Ermöglichung der nächsten Generation von Windgeneratoren mit hoher Drehmomentdichte durch additives Design und 3D‑Druck

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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.

Zusammenfassung

Windkraftanlagen mit Direktantrieb werden dank der jüngsten Projektentwicklungen immer beliebter – insbesondere im Offshore-Bereich, wo Zuverlässigkeit und Effizienz die Hauptkostentreiber sind. Die hohen Kapitalkosten zwingen jedoch viele Erstausrüster dazu, leichte Generatoren mit hoher Drehmomentdichte für Multi-Megawatt-Turbinen der nächsten Generation in Betracht zu ziehen, die mit herkömmlichen Konstruktions- oder Herstellungsverfahren möglicherweise schwer zu realisieren sind. In dieser Studie stellen wir ein neues Design-Framework vor, das durch fortschrittliches maschinelles Lernen und additive Fertigung aus mehreren Materialien eine Optimierung der magnetischen Topologie ermöglicht, die das Drehmoment pro aktiver Rotormasse für einen 15-Megawatt-Permanentmagnet-Windgenerator mit Direktantrieb maximiert. Ein Vergleich des vorgeschlagenen Ansatzes mit der herkömmlichen Topologieoptimierung zeigte eine signifikante Steigerung der Recheneffizienz und Genauigkeit bei Leistungsvorhersagen. Ergebnisse unter Verwendung von Einzel- und Multimaterialzusammensetzungen für Rotorkern und Magnete identifizieren eine größere Auswahl an 3D-druckbaren Designs für eine bestimmte Spezifikation. Eine Hybridkombination aus gesinterten und dysprosiumfreien polymergebundenen Magneten zeigt ein gutes Potenzial für die Drehmomentleistung, indem Materialkosten von bis zu 8,75 % eingespart werden. Es wurde eine Verbesserung der Rotordrehmomentdichten um mehr als 30 % festgestellt, was die Gesamtdrehmomentdichte des Generators geringfügig verbessern kann. Mit der rasanten Entwicklung der Mehrpulver-Abscheidungstechnologien kann diese Studie ein neues Paradigma für die designorientierte Fertigung mit neuartigen Materialzusammensetzungen und leichten, kostengünstigen und hochfesten Multimaterialgeometrien inspirieren, die bisher für Generatoren mit Direktantrieb noch nicht erforscht waren.

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Notes

  1. DfAM refers to a design method whereby functional performance, manufacturability, and cost can be optimized to the capabilities of AM technologies [27].

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Acknowledgements

The authors gratefully acknowledge Mohammed Elamin and Eric Chavez from Altair for software support and troubleshooting. This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Wind Energy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

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Authors and Affiliations

  1. National Wind Technology Center, National Renewable Energy Laboratory, 80401, Golden, CO, USA

    Latha Sethuraman, Ganesh Vijayakumar, Shreyas Ananthan, Jonathan Keller & Ryan King

  2. Chemical Sciences Division, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA

    M. Parans Paranthaman

Authors
  1. Latha Sethuraman
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  2. Ganesh Vijayakumar
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  3. Shreyas Ananthan
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  4. M. Parans Paranthaman
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  5. Jonathan Keller
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Contributions

Latha Sethuraman led the research idea, designed and performed the numerical simulations, generated design data, performed regression-based optimizations, analyzed the results and wrote the manuscript; Ganesh Vijayakumar and Shreyas Ananthan developed the ML implementations within MADE3D-AML software; Ganesh Vijayakumar performed ML-based optimizations; Parans Paranthaman provided technical inputs on material selection and manufacturing; Jonathan Keller helped supervise the project and provide comments; and Ryan King provided technical inputs to improving the ML-based optimization. Thanks are also due to Garrett Barter for WISDEM modeling and his inputs on cost sensitivity.

Corresponding author

Correspondence to Latha Sethuraman.

Appendix

Appendix

Fig. 18
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Accuracy of fitness models for single-material case—rotor core obtained by regression and MADE3D-AML

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Fig. 19
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A comparison of accuracy of fitness models for multimaterial case—rotor core obtained by regression and MADE3D-AML

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Fig. 20
figure 20

A comparison of accuracy of fitness models for single material case—magnets obtained by regression and MADE3D-AML

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Fig. 21
figure 21

A comparison of accuracy of fitness models for multimaterial case—magnets obtained by regression and MADE3D-AML

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Sethuraman, L., Vijayakumar, G., Ananthan, S. et al. MADE3D: Enabling the next generation of high-torque density wind generators by additive design and 3D printing. Forsch Ingenieurwes 85, 287–311 (2021). https://doi.org/10.1007/s10010-021-00465-y

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