Machine Learning Assisted Models for Understanding and Optimizing Boiling Heat Transfer on Scalable Random Surfaces

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Cambridge, Massachusetts
Project Term:
04/03/2020 - 01/02/2023

Technology Description:

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
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Project Contact Email:

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