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Machine learning assisted models for understanding and optimizing boiling heat transfer on scalable random surfaces

Massachusetts Institute of Technology (MIT)
ARPA-E Award: 
Cambridge, MA
Project Term: 
04/03/2020 to 04/02/2022
Project Status: 
Critical Need: 
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: 
The Massachusetts Institute of Technology (MIT) will develop a machine learning (ML) approach to optimize surfaces for boiling heat transfer and improve energy efficiency for applications ranging from nuclear power plants to industrial process steam generation. Predicting and enhancing boiling heat transfer presently relies on empirical correlations and experimental observations. MIT's technology will use supervised ML models to identify important features and designs that contribute to heat transfer enhancement autonomously. If successful, MIT's designs will lead to more readily adopted scalable surfaces in energy applications, enhancing performance and shortening deployment timetables.
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:
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 Tew
Project Contact: 
Prof. Evelyn Wang
Release Date: