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Equation of state of Mo from shock compression experiments on preheated samples

Fat'yanov, O. V. and Asimow, P. D. (2017) Equation of state of Mo from shock compression experiments on preheated samples. Journal of Applied Physics, 121 (11). Art. No. 115904. ISSN 0021-8979. doi:10.1063/1.4978607.

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We present a reanalysis of reported Hugoniot data for Mo, including both experiments shocked from ambient temperature (T) and those preheated to 1673 K, using the most general methods of least-squares fitting to constrain the Grüneisen model. This updated Mie-Grüneisen equation of state (EOS) is used to construct a family of maximum likelihood Hugoniots of Mo from initial temperatures of 298 to 2350 K and a parameterization valid over this range. We adopted a single linear function at each initial temperature over the entire range of particle velocities considered. Total uncertainties of all the EOS parameters and correlation coefficients for these uncertainties are given. The improved predictive capabilities of our EOS for Mo are confirmed by (1) better agreement between calculated bulk sound speeds and published measurements along the principal Hugoniot, (2) good agreement between our Grüneisen data and three reported high-pressure γ(V) functions obtained from shock-compression of porous samples, and (3) very good agreement between our 1 bar Grüneisen values and γ(T) at ambient pressure recalculated from reported experimental data on the adiabatic bulk modulus K_s(T). Our analysis shows that an EOS constructed from shock compression data allows a much more accurate prediction of γ(T) values at 1 bar than those based on static compression measurements or first-principles calculations. Published calibrations of the Mie-Grüneisen EOS for Mo using static compression measurements only do not reproduce even low-pressure asymptotic values of γ(T) at 1 bar, where the most accurate experimental data are available.

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Asimow, P. D.0000-0001-6025-8925
Additional Information:© 2017 AIP Publishing. Received 22 November 2016; accepted 2 March 2017; published online 21 March 2017. O.V.F. thanks Professor I. V. Lomonosov of IPCP RAS, Russia, for numerous stimulating discussions on the subject and Professor S. Brandt of Siegen University, Germany, for his clarification on the origin of the most accurate least-squares fitting method reported. This work was supported by the U.S. NSF, Award Nos. EAR-0810116 and EAR-1426526.
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Issue or Number:11
Record Number:CaltechAUTHORS:20170324-093720621
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:75382
Deposited By: Tony Diaz
Deposited On:31 Mar 2017 15:44
Last Modified:15 Nov 2021 16:33

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