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Johnson, Duane D. (ORCID:0000000307947283)

Publications and source records attributed to Johnson, Duane D. (ORCID:0000000307947283).

Design of refractory multi-principal-element alloys for high-temperature applications

Abstract Refractory multi-principal-element alloys (RMPEAs) exhibit high specific strength at elevated temperatures ( T ). However, current RMPEAs lack a balance of room-temperature (RT) ductility, high- T strength, and high- T creep resistance. Using density-functional theory methods, we scanned composition space using four criteria: (1) formation energies for operational stability: $$-150\le {E}_{{\rm {f}}}$$ − 150 ≤ E f ≤ +70 meV per atom; (2) higher strength found via interstitial electron density with Young’s moduli E > 250 GPa; (3) inverse Pugh ratio for ductility: G / B < 0.57; and (4) high melting points: T m > 2500 °C. Using rapid bulk alloy synthesis and characterization, we validated theory and down-selected promising alloy compositions and discovered Mo 72.3 W 12.8 Ta 10.0 Ti 2.5 Zr 2.5 having well-balanced RT and high- T mechanical properties. This alloy has comparable high- T compressive strength to well-known MoNbTaW but is more ductile and more creep resistant. It is also superior to a commercial Mo-based refractory alloy and a nickel-based superalloy (Haynes-282) with improved high- T tensile strength and creep resistance.

36 MATERIALS SCIENCE↗

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

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.

36 MATERIALS SCIENCE↗