MULTI-source Learning-Accelerated Design of high-Efficiency multi-stage compRessor (MULTI-LEADER)

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East Hartford, Connecticut
Project Term:
06/19/2020 - 12/18/2022

Technology Description:

The United Technologies Research Center (UTRC) will work to accelerate the design of high-efficiency multi-stage compressors, via machine learning (ML), with considerations of aerodynamics, structures and additive manufacturability through their framework, MULTI-LEADER. The framework addresses four design challenges in current industrial practices: (1) concurrent optimization of multiple stages under non-linear constraints; (2) evaluation of high-fidelity and expensive solvers and their gradients during optimization convergence in high-dimensional design spaces; (3) multi-disciplinary design to maximize aerodynamic performance while guaranteeing structural integrity and additive manufacturability; and (4) use of multiple fidelity of solvers with disparate parameterization and modeling assumptions. MULTI-LEADER has the potential to cut design costs by 80% while generating more energy-efficient designs of multi-stage compressors through faster and fewer design iterations, improved empiricism and performance evaluation, and quicker concurrent design processes. The proposed framework will deploy novel machine learning algorithms for multi-source learning of universal surrogate, physics-constrained data augmented modeling, generative manifold embedding, and budget-constrained fidelity-adaptive sampling to achieve the project goals.

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:
Dr. Soumalya Sarkar
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Project Contact Email:


University of Michigan
University of Maryland
University of Pennsylvania

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