Bayesian optimization with active learning of design constraints using an entropy-based approach

Khatamsaz, Danial; Vela, Brent; Singh, Prashant; Johnson, Duane D.; Allaire, Douglas; Arróyave, Raymundo

doi:10.1038/s41524-023-01006-7

Bayesian optimization with active learning of design constraints using an entropy-based approach

Journal Article · Sat Apr 01 00:00:00 EDT 2023 · npj Computational Materials

DOI:https://doi.org/10.1038/s41524-023-01006-7· OSTI ID:1968081

; ; Singh, Prashant; ; Allaire, Douglas;

Abstract

The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.

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Research Organization:: Ames Laboratory (AMES), Ames, IA (United States); Texas A&M University, College Station, TX (United States)

Sponsoring Organization:: National Science Foundation (NSF); US Army Research Laboratory (USARL); USDOE; USDOE Advanced Research Projects Agency - Energy (ARPA-E); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: AC02-07CH11358; AR0001427

OSTI ID:: 1968081

Alternate ID(s):: OSTI ID: 2202739
OSTI ID: 1969491

Report Number(s):: IS-J--11,026; 49; PII: 1006

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 9; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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