INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

Description

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this project.

Resources

Name Format Description Link
33 A brief description of the capabilities and impact of the inverse design tool generated using the INTEGRATE project. https://data.openei.org/files/5703/ARPA-E_Summit_Flyer.pdf
0 Publication describing the Invertible Neural Networks for Airfoil Design https://doi.org/10.2514/1.J060866
21 Grassmannian Shape Representations for Aerodynamic Applications https://openreview.net/pdf?id=1RRU6ud9YC
21 Separable shape tensor for aerodynamic applications https://github.com/NREL/G2Aero
21 This repository contains code for the model described in "Glaws, A., King, R. N., Vijayakumar, G., & Ananthan, S. (2022). Invertible Neural Networks for Airfoil Design. AIAA Journal, 1-13." https://github.com/NREL/INNfoil
0 Publication on design-space exploration for inverse-design of wind turbine blades using data-driven methods https://doi.org/10.2514/6.2022-1293
21 Publication on a local correlation-based transition models for high-Reynolds-number wind-turbine airfoils https://doi.org/10.5194/wes-7-603-2022
0 Publication on regularizing invertible neural networks for airfoil design through dimension reduction https://doi.org/10.2514/6.2022-1098

Tags

  • inverse-design
  • energy-cost
  • wind-turbine
  • airfoil
  • design
  • neural-network
  • artificial-intelligence
  • turbine
  • axial-turbine
  • python
  • neural-networks
  • levelized-cost-of-energy
  • power
  • ai
  • technology
  • cfd
  • computational-fluid-dynamics
  • aerodynamics
  • machine-learning
  • energy

Topics

Categories