Interdiffusion in the Ni-Re System: Evaluation of Uncertainties

Boettinger, William J.; Williams, Maureen E.; Moon, Kil-Won; McFadden, Geoffrey B.; Patrone, Paul N.; Perepezko, John H.

doi:10.1007/s11669-017-0562-7

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…See also Refs. [11, 24, 52] for examples and practical implementations applied to cases in which the deterministic component of the data is not constant.…”

Section: Computing Error In Specific Observablesmentioning

confidence: 99%

Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]

et al. 2019

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The quantitative assessment of uncertainty and sampling quality is essential in molecular simulation. Many systems of interest are highly complex, often at the edge of current computational capabilities. Modelers must therefore analyze and communicate statistical uncertainties so that “consumers” of simulated data understand its significance and limitations. This article covers key analyses appropriate for trajectory data generated by conventional simulation methods such as molecular dynamics and (single Markov chain) Monte Carlo. It also provides guidance for analyzing some ‘enhanced’ sampling approaches. We do not discuss systematic errors arising, e.g., from inaccuracy in the chosen model or force field.

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“…See also Refs. [11, 24, 52] for examples and practical implementations applied to cases in which the deterministic component of the data is not constant.…”

Section: Computing Error In Specific Observablesmentioning

confidence: 99%

Best Practices for Quantification of Uncertainty and Sampling Quality in Molecular Simulations [Article v1.0]

et al. 2019

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“…The calculated profile is compared with the measured profile and the diffusion coefficients are altered and reused in the simulation until the total summation of the least square errors is minimized. A similar approach has been used by Boettinger et al [34] when evaluating uncertainties associated with diffusion coefficient evaluations in the Ni-Re system. Usually, diffusion coefficients are used as the input data when assessing diffusion mobilities for CALPHAD mobility descriptions.…”

Section: Determination Of Diffusion Coefficients From Experimental Datamentioning

confidence: 99%

Diffusion in the Ti-Al-V System

Lindwall

Moon

Chen

et al. 2018

J. Phase Equilib. Diffus.

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Diffusion in the Ti-Al-V system is studied and a CALculation of Phase Diagrams (CALPHAD) diffusion mobility description is developed. Diffusion couple experiments are performed to obtain diffusion paths in the hcp phase at 923 K, 1023 K and 1123 K. The diffusion coefficient of V in the hcp-Ti phase is found to decrease with increasing Al alloying. A forward-simulation analysis is used to evaluate the impurity diffusion coefficient for Al and V diffusion in the hcp Ti-V and the Ti-Al systems which are used as input in the mobility modeling. The composition dependency for the diffusion in the hcp phase in the ternary system is accounted for and a CALPHAD diffusion mobility description is obtained by directly optimizing the mobility parameters as a function of the experimental composition profiles from the diffusion couples. Both experimental data and previous diffusion mobility descriptions in the literature for the bcc Ti-Al-V phase are adopted. A complete description of diffusion in both the hcp and bcc phases for the Ti-Al-V system is presented with the aim to be used for design of Ti alloys and processes. Keywords CALPHAD Á diffusion couples Á diffusion modeling Á interdiffusion Á Ti-Al-V This invited article is part of a special issue of the Journal of Phase Equilibria and Diffusion in honor of Prof. Zhanpeng Jin's 80th birthday. The special issue was organized by Prof. Ji-Cheng (JC) Zhao,

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“…For experiments conducted at ~1450ºC and similar run times, Ni losses increase with decreasing fO 2 for both container materials (Table 2); for our experiments, they are greater for Re than Pt but it should be kept in mind that Re loops were used under more reducing conditions. Given the correlation of Ni losses with fO 2 and the fact that impurity diffusion of Ni in Re is slower than is Ni in Pt (Boettinger et al, 2017;Eastman and Zhao, 2019) it seems likely that observed losses of Ni to sample containers are related to increasing Fe contents in the container alloys with decreasing fO 2 .…”

Section: Resultsmentioning

confidence: 99%