MULTI-source Learning-Accelerated Design of high-Efficiency multi-stage compRessor (MULTI-LEADER)
Project Innovation + Advantages:
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.
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:
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.