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Title: Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit

Abstract

Classical nucleation theory (CNT) is a promising way to predictively model the submicrosecond kinetics of phase transitions that occur under dynamic compression, such as the suite of experiments performed over the past two decades on the solidification of liquid water to the high-pressure ice VII phase. Myint et al. [Phys. Rev. Lett. 121, 155701 (2018)] presented the first CNT-based model for these types of rapid phase transitions, but relied on an empirical scaling parameter in their transient induction model to simulate the lag time that occurs prior to the onset of significant formation of ice VII clusters in the system. To build on that study, we model the liquid water–ice VII phase transformation using a numerical discretization scheme to solve the Zel’dovich–Frenkel partial differential equation, which is a fundamental CNT-based kinetic equation that describes the statistical time-dependent behavior of solid cluster formation. The Zel’dovich–Frenkel equation inherently accounts for transience in the nucleation kinetics and eliminates the need for the empirical scaling factor used by Myint et al. One major result of this research is that transience is found to play a relatively small role in the nucleation process for the dynamic-compression time scales of the liquid water–ice VII experiments beingmore » simulated. Instead, we show that it is possible to properly model the lag time using steady-state CNT by making small refinements to the interfacial free energy value. In conclusion, we have also developed a new dimensionless parameter that may be applied a priori to predict whether or not transient nucleation will be important in a given dynamic-compression experiment.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [2]
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  1. Univ. of California, Davis, CA (United States). Dept. of Mechanical and Aerospace Engineering
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1572606
Report Number(s):
LLNL-JRNL-788078
Journal ID: ISSN 0021-9606; 985899; TRN: US2001248
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 151; Journal Issue: 16; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English
Subject:
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY

Citation Formats

Sterbentz, Dane M., Myint, Philip C., Delplanque, Jean-Pierre, and Belof, Jonathan L. Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit. United States: N. p., 2019. Web. doi:10.1063/1.5125948.
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Sterbentz, Dane M., Myint, Philip C., Delplanque, Jean-Pierre, & Belof, Jonathan L. Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit. United States. https://doi.org/10.1063/1.5125948
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Sterbentz, Dane M., Myint, Philip C., Delplanque, Jean-Pierre, and Belof, Jonathan L. Tue . "Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit". United States. https://doi.org/10.1063/1.5125948. https://www.osti.gov/servlets/purl/1572606.
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@article{osti_1572606,
title = {Numerical modeling of solid-cluster evolution applied to the nanosecond solidification of water near the metastable limit},
author = {Sterbentz, Dane M. and Myint, Philip C. and Delplanque, Jean-Pierre and Belof, Jonathan L.},
abstractNote = {Classical nucleation theory (CNT) is a promising way to predictively model the submicrosecond kinetics of phase transitions that occur under dynamic compression, such as the suite of experiments performed over the past two decades on the solidification of liquid water to the high-pressure ice VII phase. Myint et al. [Phys. Rev. Lett. 121, 155701 (2018)] presented the first CNT-based model for these types of rapid phase transitions, but relied on an empirical scaling parameter in their transient induction model to simulate the lag time that occurs prior to the onset of significant formation of ice VII clusters in the system. To build on that study, we model the liquid water–ice VII phase transformation using a numerical discretization scheme to solve the Zel’dovich–Frenkel partial differential equation, which is a fundamental CNT-based kinetic equation that describes the statistical time-dependent behavior of solid cluster formation. The Zel’dovich–Frenkel equation inherently accounts for transience in the nucleation kinetics and eliminates the need for the empirical scaling factor used by Myint et al. One major result of this research is that transience is found to play a relatively small role in the nucleation process for the dynamic-compression time scales of the liquid water–ice VII experiments being simulated. Instead, we show that it is possible to properly model the lag time using steady-state CNT by making small refinements to the interfacial free energy value. In conclusion, we have also developed a new dimensionless parameter that may be applied a priori to predict whether or not transient nucleation will be important in a given dynamic-compression experiment.},
doi = {10.1063/1.5125948},
journal = {Journal of Chemical Physics},
number = 16,
volume = 151,
place = {United States},
year = {Tue Oct 22 00:00:00 EDT 2019},
month = {Tue Oct 22 00:00:00 EDT 2019}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1063/1.5125948
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Cited by: 7 works
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Figures / Tables:

FIG. 1 FIG. 1: Two distribution functions relevant to the liquid water–ice Ⅶ phase transition as a function of cluster size $n$ at a pressure of approximately 7 GPa and a temperature of 450 K (here, $∆µ$/$k_BT$ = −1, $σ$ = 123 mJ/m2, vs = 10.214 cm 3/mol): (a) Gibbs energy ofmore » cluster formation; (b) equilibrium cluster concentration. The distribution functions shown here are computed from Equations (4)–(6) using the equation of state and interfacial free energy models developed by Myint et al. specifically for liquid water–ice Ⅶ system. The critical-cluster size $n$∗ is marked on the horizontal axis in both (a) and (b).« less
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    Figures / Tables found in this record:

    FIG. 1 (p. 8)figure
    FIG. 2 (p. 14)figure
    FIG. 3 (p. 15)figure
    FIG. 4 (p. 17)figure
    FIG. 5 (p. 19)figure
    FIG. 6 (p. 20)figure
    FIG. 7 (p. 21)figure
    FIG. 8 (p. 22)figure
    FIG. 9 (p. 24)figure
    FIG. 10 (p. 26)figure
    TABLE I (p. 27)table
    FIG. 11 (p. 30)figure
    FIG. 12 (p. 31)figure
    FIG. 13 (p. 32)figure
    FIG. 14 (p. 33)figure
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