IMPACT: Design of Integrated Multi-physics, Producible Additive Components for Turbomachinery
The DIFFERENTIATE program seeks to leverage the emerging artificial intelligence (AI) revolution to help resolve the energy and environmental challenges of our time. The program aims to speed energy innovation by incorporating machine learning (ML) into the energy technology development process. A core part of AI, ML is the study of computer algorithms that improve automatically through experience. This approach is expected to facilitate a rapid transition to lower-carbon-footprint energy sources and systems. To organize the proposed efforts, the program uses a simplified engineering design process framework to conceptualize several ML tools that could help engineers execute and solve these problems in a manner that dramatically accelerates the pace of energy innovation.
Project Innovation + Advantages:
GE Research will develop design optimization tools for the laser powder bed fusion based additive manufacturing of turbomachinery components. The team will integrate the latest advances in multi-physics topology optimization with fast machine learning-based producibility evaluations extracted from large training datasets comprising high-fidelity physics-based simulations and experimental validation studies. The integrated methodology will be used to demonstrate simultaneous improvements in the producibility and thermodynamic efficiency of a multi-physics turbomachinery component. Improved turbomachinery efficiency is a competitive advantage for U.S. industry and will help ensure the nation's energy security. The proposed manufacturing producibility-aware, multi-physics detailed design optimization tools will advance the use of additive manufacturing within the U.S.
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:
Seek U.S. technological competitive advantage by leading the development of machine-learning enhanced engineering design tools.
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.
Reap the economic productivity benefits associated with the commercial adoption of the resulting higher-value energy technologies and associated products.
ARPA-E Program Director:
Dr. David TewProject Contact:
Press and General Inquiries Email:
ARPA-E-Comms@hq.doe.govProject Contact Email:
Palo Alto Research Center
Oak Ridge National Laboratory