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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Thermodynamic modeling with uncertainty quantification using the modified quasichemical model in quadruplet approximation: Implementation into PyCalphad and ESPEI

The modified quasichemical model in the quadruplet approximation (MQMQA) considers the first- and the second-nearest-neighbor coordination and interactions, particularly useful in describing short-range ordering (SRO) in complex liquids such as molten salts, slag in metal processing, and electrolytic solutions. Here, the present work implements the MQMQA into the Python based open-source software PyCalphad for thermodynamic calculations. This endeavor facilitates the development of MQMQA-based thermodynamic database with uncertainty quantification (UQ) and propagation (UP) using the open-source software ESPEI. A new database structure based on Extensible Markup Language (XML) is proposed for ESPEI evaluation of MQMQA model parameters. Using the KF-NiF 2 , KCl-NaCl-MgCl 2 , and CaCl 2 -CaF 2 -LiCl-LiF salt systems as examples, we demonstrate the successful implementation of MQMQA in PyCalphad through thermodynamic calculations of Gibbs energy, equilibrium quadruplet fractions, and phase diagram, as well as database development with UQ and UP using ESPEI. Furthermore, as an application of the present implementation, both the LiF–TbF 3 and LiF-HoF 3 systems have been modeled by MQMQA for the first time, which are in good agreement with experiments. The present implementation hence offers an open-source capability for performing CALPHAD modeling for complex liquids with SRO using MQMQA plus a new XML database structure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Thermodynamic modeling of the Nb-Ni system with uncertainty quantification using PyCalphad and ESPEI

Here, the Nb–Ni system is remodeled with uncertainty quantification (UQ) using software tools of PyCalphad and ESPEI (the Extensible, Self-optimizing Phase Equilibria Infrastructure) with the presently implemented capability of modeling site fraction based on Wyckoff positions. The five- and three-sublattice models are used to model the topologically close pack (TCP) μ-Nb 7 Ni 6 and δ-NbNi 3 phases according to their Wyckoff positions. The inputs for CALPHAD-based thermodynamic modeling include the thermochemical data as a function of temperature predicted by first-principles and phonon calculations based on density functional theory (DFT), ab initio molecular dynamics (AIMD) simulations, together with phase equilibrium and site fraction data in the literature. In addition to phase diagram and thermodynamic properties, the CALPHAD-based predictions of site fractions of Nb in μ-Nb 7 Ni 6 agree well with experimental data. Furthermore, the UQ estimation using the Markov Chain Monte Carlo (MCMC) method as implemented in ESPEI is applied to study the uncertainty of site fraction in μ-Nb 7 Ni 6 and enthalpy of mixing (ΔH mix ) in liquid.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Demonstrating PyCalphad, ESPEI, and MSTDB-TC for MSR Applications

The utility of thermochemical modeling tools PyCalphad, ESPEI, and MSTDB-TC are demonstrated for applications relevant to molten salt reactor design, safety, and licensing. Given the availability of experimental data, the demonstration is predominantly focused on the use of MSTDB-TC v3.0 to model Li- and Be-bearing fluoride salt mixtures. Nonetheless, the methodology reported in using these tools for such applications can be applied to any salt system given requisite data availability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Implementation of an extensible property modeling framework in ESPEI with applications to molar volume and elastic stiffness models

Property models are becoming more widely adopted by commercial Calphad databases, but they are not nearly as common in non-commercial or traditional academic Calphad databases. A primary driver is that user-friendly Calphad modeling tools that support property models are not widely available. Here we present new property modeling capabilities that have been implemented in ESPEI (the Extensible, Self-optimizing Phase Equilibrium Infrastructure). These capabilities include both generating property model parameters from data and improvements to the algorithmic selection of the most appropriate model from a series of candidates. Additionally, two illustrative examples are given that use ESPEI to fit different property models. First, we generate molar volume model parameters for Group IV, V, and VI refractory BCC alloys based on the model by Lu et al. (2005). Second, we demonstrate the extensibility of ESPEI’s property modeling capabilities by implementing a custom PyCalphad model for BCC elastic stiffness parameters to generate and compare parameters to the ones assessed by Marker et al. (2018) using the same data. Property models generated by ESPEI can be used in PyCalphad or further optimized with uncertainty quantification using ESPEI.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Open-source Numerical Modeling of Solidification Cracking Susceptibility: Application to Refractory Alloy Systems

Introduction. Alloys such as aluminum, nickel-base, and austenitic stainless steels are susceptible to solidification cracking during welding and 3D printing. Compositional optimization is one method used to effectively mitigate solidification cracking of those alloy systems. With the surge in hypersonic and in-space propulsion activities, refractory metals (Nb, Mo, Ta, W, and Re) and their alloy derivatives are increasing in importance due to their extreme high melting point and retention of high-temperature strength; however, their chemistry was most typically optimized to promote ductility during mechanical operations such as drawing and forming. Welding of such alloys has been a challenge due to a number of issues including solidification cracking, atmospheric contamination (O, C, and N), as well as a shift in ductile-to-brittle transition to higher temperature following grain growth induced by welding. Compositional optimization of refractory alloys for solidification cracking resistance in particular is desirable as their usage increases with the advent of advanced manufacturing methods such as 3D printing. This work evaluates the effect of compositional variation in refractory metal systems on the solidification cracking susceptibility with the goals of optimizing existing alloys and joining process techniques, and formulating new alloys with increased solidification cracking resistance. Experimental Procedures. A python code was developed in a Jupyter notebook environment (Michael and Sowards, 2023) to facilitate the calculation of crack susceptibility index proposed by Kou (2015). Composition is entered as a single point, or as a 1-D or 2-D array. The notebook calls pycalphad (Otis and Liu, 2017 and Bocklund et al, 2020) to calculate the evolution of fraction solid as a function of temperature (under either Scheil or equilibrium assumptions) and then evaluates steepness of the fraction solid curve near the terminal stage of solidification to predict solidification cracking resistance. Open source thermodynamic databases available at online repositories are used (van de Walle). The process is setup in an automated fashion to generate plots that show variation in solidification cracking susceptibility according to composition on 1-D line plots or 2-D contour plots. The Jupyter notebook and crack susceptibility algorithm was also integrated with a widely used commercial CALPHAD code for validation and alloy exploration. Results and Discussion. The crack susceptibility model was first validated against a series of refractory alloy compositions evaluated in past work which utilized a specialized Varestraint test built inside a vacuum chamber environment (Lessman and Gold, 1971). The alloys tested in the Varestraint apparatus included T-111 (Ta-8W-2Hf), ASTAR-811C (Ta-8W-1Re-0.7Hf-0.025C), FS-85 (Nb-27Ta-10W-1Zr), T-222 (Ta-9.6W-2.4Hf-0.01C), Ta-10W, B-66 (Nb-5Mo-5V-1Zr), and SCb-291 (Nb-10W-10Ta). The initial test of the model showed a strong correlation with empirical Varestraint data, i.e., a Spearman rank correlation between model predictions and hot cracking measurements was observed to be greater than 0.8. Following the validation, a set of refractory metal binary mixtures was investigated to evaluate sensitivity of Nb, Mo, W, and Ta to C, N, and O content. A series of plots were produced that suggest ppmw ranges of C, N, and O where solidification cracking increases significantly and reaches a maximum. Also comparative ranking of each primary refractory metal to each interstitial was produced. For example C produces greater cracking response in Mo whereas O produces greater cracking response in Ta and Nb. Such compositional values have utility in setting limits on pickup of these interstitial elements during welding and printing rather than using a one-size-fits-all approach. Furthermore, the results have use in determining additive powder recycling requirements, which is especially pertinent for refractory metal powders due to their high cost compared to conventional alloys. Another application created thousands of hypothetical alloys within the nominal specified composition range of two widely used refractory alloys C103 (Nb-10Hf-1Ti) and TZM (Mo-0.5Ti-0.1Zr). The cracking index was calculated for the alloys and results were fed into machine learning regression techniques including Multiple Linear Regression, Ridge Regression, and Lasso Regression to determine relative potency each alloying element had on computed solidification cracking index. A series of linear equations were produced that relate composition of C103 and TZM to solidification cracking index. The crack susceptibility of C103 for example is described by an equation of the form: cracking index ~ O + 0.667*C + 0.635*N + 0.00037*Ta – 0.0008*Hf (in wt.%) From that equation, it is clear that O has strong propensity to induce solidification cracking. Interestingly, Hf is shown to reduce calculated cracking response. Finally, realizing the potential of this method to discover new refractory alloy formulations across the period table that have low solidification cracking sensitivity, the code was applied to new untested alloy systems including W-Zr-C, W-Ta-C, and others. Conclusions. In summary, an open source numerical method has been developed using Python code to calculate Kou’s crack susceptibility index. The method was applied to refractory metals which are inherently difficult to study from a weldability testing standpoint since inert shielding gas is not sufficient and welding is typically done in vacuum, especially in light of findings presented here where oxygen has profound influence on solidification cracking. This work revealed the effect of compositional variations on a series of refractory metals and showed the framework defined here will be useful in 1) the development of new alloys that have improved weldability and 3D printability, 2) placing compositional limits on existing alloys, and 3) ensuring adequate controls of manufacturing processes such as 3D printing where powder reuse is critical. Keywords. pycalphad; Python; refractory metals; solidification cracking. References. B. Bocklund et. al. (2020) http://doi.org/10.5281/zenodo.3630657. S. Kou. (2015) https://doi.org/10.1016/j.actamat.2015.01.034. G.G. Lessmann and R.E. Gold. Welding Journal, issue 1, pp. 1-s – 8-s (1971). F.N. Michael and J.W. Sowards. NASA/TM-20230002218 (2023). R. Otis and Z.-K. Liu. (2017) http://doi.org/10.5334/jors.140. A. Van de Wallle et. al. (2018) https://doi.org/10.1016/j.calphad.2018.04.003.

pycalphad↗
DOE OSTI2022

Thermodynamic modeling of the Pd–Zn system with uncertainty quantification and its implication to tailor catalysts

Pd–Zn intermetallic catalysts show encouraging combinations of activity and selectivity on well-defined active site ensembles. Thermodynamic description of the Pd–Zn system, delineating phase boundaries and enumerating site occupancies within intermediate alloy phases, is essential to determining the ensembles of Pd–Zn atoms as a function of composition and temperature. Combining the present extensive first-principles calculations based on density functional theory (DFT) and available experimental data, the Pd–Zn system was remodeled using the CALculation of PHAse Diagrams (CALPHAD) approach. High throughput modeling tools with uncertainty quantification, i.e., ESPEI and PyCalphad, were incorporated in the phase analysis. Here, the site occupancies across the γ phase composition region were given special attention. A four-sublattice model was used for the γ phase owing to its four Wyckoff positions, i.e., the outer tetrahedral (OT) site 8c, the inner tetrahedral (IT) site 8c, the octahedral (OH) site 12e, and the cuboctahedral (CO) site 24g. The site fractions of Pd and Zn calculated from the present thermodynamic model show the occupancy preference of Pd in the OT and OH sublattices in agreement with experimental observations. The force constants obtained from DFT-based phonon calculations further supports the tendency of Pd occupying the OH sublattice compared with the IT and CO sublattice. The catalytic ensembles changing from Pd monomers (Pd 1 ) to trimers (Pd 3 ) on the surface of γ phase are attributed to the increase of Pd occupancy in the OH sublattice.

36 MATERIALS SCIENCE↗
DOE OSTI2026

ML-AMD/exa-pd

Exa-pd is a highly parallelizable workflow for constructing multi-element phase diagrams (PDs). It uses standard sampling techniques—molecular dynamics (MD) and Monte Carlo (MC)—as implemented in the LAMMPS package, to simultaneously sample multiple phases on a fine temperature–composition mesh for free-energy calculations. The workflow uses Parsl as a global controller to manage the MD/MC jobs to achieve massive parallelization with almost ideal scalability. The resulting free energies of both liquid and solid phases (including solid solutions) are then fed to CALPHAD modeling using the PYCALPHAD package for the construction of a multi-element PD.

Zhang, Feng [Ames Laboratory (AMES), Ames, IA (Uni↗
DOE OSTI2022

High Throughput Computational Framework of Materials Properties for Extreme Environments

This project aims to establish a framework capable of efficiently predicting the properties of structural materials for service in harsh environments over a wide range of temperatures and over long periods of time. The approach is to develop and integrate high throughput first-principles calculations in combination with machine learning (ML) methods, perform high throughput CALPHAD (calculations of phase diagrams) modeling, and carry out finite element method (FEM) simulations. Relevant to high temperature service in fossil power system, nickel-based superalloys such as Inconel 740 and Haynes 282 as well as the associated (Ni-Cr-Co)-Al-C-Fe-Mn-Mo-Nb-Si-Ti system, were investigated. The present framework was built on the concept of phase-based property data, in which properties of individual phases are modeled as a function of internal and external independent variables. This project established an open-source infrastructure with the following capabilities: (1) High throughput implementation of first-principles calculations at finite temperatures and variable compositions using both accurate phonon calculations and the efficient Debye model for thermodynamic properties, elastic constants, diffusion coefficients, vacancy formation, stacking and twin faults, and dislocation mobility; i.e., using the developed code DFTTK; (2) Machine learning capabilities to predict the above properties so that the number of first-principles calculations can be significantly reduced; e.g., using the developed code SIPFENN; (3) High throughput CALPHAD modeling of the above properties as a function of temperature and composition using our unique capability based on ESPEI and PyCalphad; (4) New capabilities to predict the stress-strain behavior of individual phases; and (5) New models for tensile strength prediction in common FEM software with the crystal plasticity finite element simulations (CPFEM).

, Ni-based superalloys↗
DOE OSTI2025

Model form and sensitivity analysis of CALPHAD-based nucleation models in b-stabilized Ti alloys

Accurate prediction of α-phase nucleation and growth in β-stabilized titanium alloys is crucial for designing heat treatments to optimize mechanical properties in additively manufactured lightweight components. Ideally, predictions of nucleation and growth would incorporate both top-down observations of past experimental heat treatments and bottom-up modeling of phase transformations; however, the appropriate method of combining these information sources is not self-evident. Combining top-down and bottom-up information requires a unified form of model that can connect between spatiotemporal scales, as well as sets of fitting parameters that can be identified by each data source. The selection of which parameters to fit to which data source can be made based on expert opinion, or by performing a sensitivity analysis. In solid-solid nucleation, direct observation of the nucleation and growth process is challenging. Most data on the heat treatment-controlled phase transformations are not in-situ. To predict the process and outcome of the nucleation, growth and coarsening of precipitates, theoretical models of the nucleation pathway are used to bridge the gap. Many sources of uncertainty affect the modeling of this nucleation process. It can be influenced by small variations in the thermomechanical processing history, chemical composition, and initial microstructure. If molecular dynamics (MD) simulations are used to determine thermodynamic quantities and inform CALPHAD modeling, additional uncertainty can be introduced and accounted for using Bayesian methods. Top-down uncertainties require additional steps to quantify. The influence of nucleation model form on the sensitivity of predictions to input parameters and physical conditions is the focus of this study. Classical nucleation theory (CNT) allows modeling to formulate the nucleation as homogeneous or, more commonly, heterogeneous. Non-classical nucleation models are also increasingly explored as a means of reconciling top-down and bottom-up data. In this study, the sensitivity of the intragranular nucleation of α in a β-annealed, slow-cooled aging (BASCA) heat treatment of β-stabilized Ti5553 alloy is explored using CNT and both heterogeneous and homogeneous assumptions. The Kampmann-Wagner Numerical model of precipitate nucleation and growth is employed. Using open-source tools (pyCalphad and thermodynamic modeling of TiMo as a surrogate system, a sensitivity analysis is performed to measure variations in key parameters, including chemical driving force, interfacial energy, and diffusivity, as they relate to predictions of precipitate number density. The inclusion of top-down and bottom-up data in selection of nucleation model form is discussed.

Rodriguez Negron, A. M.↗
NASA NTRS2023

Numerical Study of Solidification Crack Susceptibility in Novel Refractory Alloy Systems

Calculation of Phase Diagrams (CALPHAD) -based solidification computa­tions, such as Scheil or equilibrium computations, have been utilized to propose crack solidification susceptibility indices (CSSIs) of cracking. These CSSIs have been proposed in order to predict the cracking susceptibility of an alloy in the solidification range, as a function of its solidification frac­tion and as dependent upon its wt.% elemental composition through the CALPHAD computation & via comparative methods between one composition & the next. Recently Kou at al. have proposed a novel CSSI where the gradient of the e.x. Scheil solidification curve is obtained in the critical solidification cracking region of greater than 0.95 fraction of solid. Therefore, a direct Temperature dependent metric is now available for the prediction, and presumably the control, of solidification cracking. In this TM, the researchers apply this Kou gradient method to refractory alloys for the first time & discuss justification for the approach via Spearman rank correlation with Varestraint cracking test data as well as by comparisons with Thermocalc based vulnerability time CSSI calculations. A Python Pycalphad module example of the approach is provided & can be utilized as an open-source resource that utilizes also open-source available thermodynamical databases, making the CSSI quantitative aspect of ICME more available and freely available to practitioners.

ICME integrated computational materials engineerin↗
NASA NTRSDate not supplied

An Open-Source Numerical Model for Mitigating Refractory Alloy Hot Cracking Susceptibility

Refractory alloys are susceptible to solidification cracking during welding and 3D printing. Composition control is an effective method of controlling solidification cracking. This work evaluates the effect of compositional variation in refractory metal systems on a computed solidification cracking susceptibility. A numerical model has been developed using Python code and open-source CALPHAD software to calculate Kou’s crack susceptibility index. The model is validated against past weldability studies performed on several refractory alloy systems. The approach is extended towards the development of new alloys with improved 3D printability and weldability and is shown to have utility in defining compositional limits for existing alloys and feedstocks. Furthermore, the model will aid in determining process controls for powder reuse and recycling.

pycalphad↗