Tuesday 2:30 PM

March 1, 2022

Room: 255A

Location: Anaheim Convention Center

Harmonic models are convenient for free energy, F, and entropy, S. It is tempting to obtain the temperature dependence of F and S by correcting harmonic frequencies, ω, for effects of volume and temperature, V, T. This can be done using perturbation theory, which is often successful for S(ω(V,T)) [1]. When the anharmonicity is large, however, new behaviors arise. In a highly nonlinear medium, the input vibrational amplitude, Ψ, becomes Ψ' = Ψ+ηΨ

Computing mesoscale diffusivity requires scaling up from microstates and transition rates at the atomistic scale. In particular, we need to solve the master equation, which describes the time-evolution of probability, and find the long-time "near equilibrium" steady-state solution. The general solution involves the inversion of rate matrix: the Green function. However, while the rate matrix is typically sparse, its inverse is not; moreover, efficient calculation of the Green function is tractable only for dilute concentrations. By recasting the calculation of transport coefficients as a variational problem, we can compute transport coefficients from thermal average quantities instead of trajectory-based calculations. Moreover, it provides a framework for understanding and improving kinetic Monte Carlo calculations of diffusivity. A "hybrid" approach to diffusivity, using kinetic Monte Carlo of short trajectories followed by machine learning optimization can produce diffusivity values that are equivalent to those from significantly longer time trajectories.

Building on a previously developed Ni-based diffusion mobility database and using a newly developed Co-based thermodynamic database, a new Co-based diffusion database that includes Co-Al-W-Ni-Cr-Ta-Ti is presented. Available literature data, first-principal simulation data, and new experimental and computational diffusion data are combined to construct this multicomponent diffusion mobility description. Diffusion couple stacks are used to obtain additional diffusion mobility data for 9 binary and 14 ternary systems at three different temperatures. The diffusion couple composition profiles are analyzed and then used to optimize the FCC phase mobility parameters. Specific results for these optimizations will be presented for the Co-Al-W-Ni, Co-Al-Ni-Cr and Co-Al-Ni-Ta systems.

Computational, predictive tools greatly add to the information from traditional data sources. Within computational thermodynamics the CALPHAD method has established itself as a pillar for computational materials and process design. Recently, the CALPHAD method has been expanded beyond modeling of thermodynamics and diffusion mobility to include many other phase-based properties. The general methodology is description of the compositional dependence using the same models as those for thermodynamic properties. Some CALPHAD models are very complex because they are designed to describe special effects, such as chemical order, and require determination of many model parameter values. However, this model complexity may not be necessary for accurate description of all phase-based properties. For example, the effect of chemical order on the molar volume can be minimal while it can be significant for other properties, such as electric or thermal conductivity. An overview of model requirements will be presented.

Nickel- and emerging Co-base superalloys derive their excellent mechanical properties from ordered intermetallic precipitates that are resistant to shearing by dislocations that initially glide through the disordered fcc matrix of these two phase materials. Planar fault energies, including anti-phase boundary and superlattice intrinsic and extrinsic faults are important to the design of new compositions. Progress in using first-principles calculations to understand these fault energies will be reviewed. Implications for the design of new CoNi-base superalloys will be discussed.

Molten silicate degradation of coatings designed for thermal and/or environmental protection of gas turbine structural components represents a fundamental barrier to progress by limiting the allowa-ble material temperatures. At the root failure mechanisms are thermomechanical in nature, driven by strain incompatibility during thermal cycling. The genesis of failure scenarios, however, is strongly influenced by thermochemical interactions with the environment and intrinsic evolution of the system. The silicate attack problem is complex, involving glassy melts containing at least five major oxide components, interacting with a coating material that can involve two or more oxides. Moreover, the melts originate from ingested mineral debris that can have a wide variability in com-position depending on geographical origin and distribution in the atmosphere. A proposed strategy is inspired on ICME principles and combines Calphad, machine learning, and models connecting composition and relevant properties. The challenges involved and progress in implementing the proposed strategy are discussed.