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

Default ARPA-E Project Image

Program:
DIFFERENTIATE
Award:
$1,800,000
Location:
Golden, Colorado
Status:
ALUMNI
Project Term:
04/08/2020 - 08/31/2023
Website:

Technology Description:

The National Renewable Energy Laboratory (NREL) will develop a novel wind turbine design capability that enables designers to explore advanced technology concepts at a lower cost. This capability will harness the power of a deep neural network (DNN)-based inverse design methodology. To overcome challenges with the use of traditional DNNs in this application, NREL will develop innovative techniques to sparsify the neural network using active subspaces that will ensure that the model is invertible and can quickly zoom in on relevant designs at minimal cost. The models will be trained using data from computational fluid dynamics simulations, running on NREL’s supercomputers, which in turn use machine learning assisted turbulence models to predict flow separation and stall observed in wind turbine flows.

Potential Impact:

DIFFERENTIATE aims to enhance the productivity of energy engineers in helping them to develop next-generation energy technologies. If successful, DIFFERENTIATE will yield the following benefits in ARPA-E mission areas:

Security:

Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.

Environment:

Use these tools to solve our most challenging energy and environmental problems by facilitating an economically-attractive transition to lower carbon-footprint energy sources and systems.

Economy:

Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.

Contact

ARPA-E Program Director:
Dr. David Tew
Project Contact:
Ganesh Vijayakumar
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.gov
Project Contact Email:
ganesh.vijayakumar@nrel.gov

Partners

University of Maryland

Related Projects

• Carnegie Mellon University (CMU) - High-fidelity Accelerated Design of High-performance Electrochemical Systems
• Carnegie Mellon University (CMU) - Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials
• General Electric (GE) Global Research - IMPACT: Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery
• General Electric (GE) Global Research - Probabalistic Machine Learning for Inverse Design of Aerodynamic Systems (Pro-ML IDeAS)
• IBM T.J. Watson Research Center - Model-based Reinforcement Learning with Active Learning for Efficient Electrical Power Converter Design
• Iowa State University (ISU) - Context-Aware Learning for Inverse Design in Photovoltaics
• Julia Computing - Accelerating Coupled HVAC/Building Simulation with a Neural Component Architecture
• Lawrence Berkeley National Laboratory (LBNL) - Deep Learning and Natural Language Processing for Accelerated Inverse Design of Optical Metamaterials
• Los Alamos National Laboratory (LANL) - Machine Learning-Based Well Design to Enhance Unconventional Energy Production
• Massachusetts Institute of Technology (MIT) - Machine Learning Assisted Models for Understanding and Optimizing Boiling Heat Transfer on Scalable Random Surfaces
• Massachusetts Institute of Technology (MIT) - Global Optimization of Multicomponent Oxide Catalysts for OER/ORR
• National Renewable Energy Laboratory (NREL) - End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning
• National Renewable Energy Laboratory (NREL) - INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
• Northwestern University - Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials
• Pacific Northwest National Laboratory (PNNL) - Machine Learning for Natural Gas to Electric Power System Design
• Princeton University - MLSPICE: Machine Learning based SPICE Modeling Platform for Power Magnetics
• Stanford University - Energy Efficient Integrated Photonic Systems based on Inverse Design
• United Technologies Research Center (UTRC) - MULTI-source Learning-Accelerated Design of high-Efficiency multi-stage compRessor (MULTI-LEADER)
• United Technologies Research Center (UTRC) - Learning Enabled Network Synthesis (LENS)
• University of Maryland (UMD) - Invertible Design Manifolds for Heat Transfer Surfaces (INVERT)
• University of Michigan, Dearborn - ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters
• University of Missouri - Deep Learning Prediction of Protein Complex Structures
• University of Texas at Austin (UT Austin) - Learning Optimal Aerodynamic Designs

Release Date:
04/05/2019