About this Abstract |
Meeting |
MS&T21: Materials Science & Technology
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Symposium
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High Entropy Materials: Concentrated Solid Solutions, Intermetallics, Ceramics, Functional Materials and Beyond II
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Presentation Title |
Joint Prediction of Mechanical Properties of Alloys with Enhanced Fidelity through Integration of Machine Learning (Data Analytics) and Multiscale Modeling |
Author(s) |
Baldur Steingrimsson, Peter K. Liaw, Jaafar El-Awady |
On-Site Speaker (Planned) |
Peter K. Liaw |
Abstract Scope |
This presentation addresses the design of advanced alloys systems that can enable new propulsion systems for subsonic transport vehicles with high levels of thermal, transmission, and propulsive efficiency. Organizations, such as the National Aeronautics and Space Administration, are seeking integrated computational and experimental approaches that can decrease the time necessary for development, testing, and validation of such alloy systems and components.To this effect, we address the design and development of high-entropy alloys (HEAs) for subsonic transport vehicle propulsion system structures and components. We are looking to jointly optimize the mechanical properties of HEAs for applications involving turbine blades capable of operating at higher temperature and with greater efficiency, resulting in improved fuel efficiency and reduced emissions. Temperature and thermo-mechanical performance, environmental durability, reliability, and cost-effectiveness are here important considerations. We combine strengths of machine learning and multiscale (physics-based) modeling, for purpose of making up for limitations of each approach. |