Invertible Neural Networks for Airfoil Design

Research output: Contribution to journalArticlepeer-review

13 Scopus Citations

Abstract

The airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using lowfidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with welldefined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

Original languageAmerican English
Pages (from-to)3035-3047
Number of pages13
JournalAIAA Journal
Volume60
Issue number5
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 by the American Institute of Aeronautics and Astronautics, Inc.

NREL Publication Number

  • NREL/JA-2C00-79387

Keywords

  • airfoil design
  • deep learning
  • invertible neural network

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