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A multiphase EOS for metals using mGLJ model AITranslate

Theoretical Physics Division, Bhabha Atomic Research Centre
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Publisher: Elsevier
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Abstract AITranslate

Highlights • An improved version of the mGLJ cold model is proposed. • Mean field theory for ions and TF model for electrons were used to generate the solid phase free energy. • The fluid phase free energy is generated using the CRIS model. • The multiphase EOS thus formulated is used to verify a large number of experimental as well as theoretical data. A multiphase equation of state (EOS) model for metals is presented using an improved form of the modified generalized Lennard Jones (mGLJ) cold curve which can account for the solid, liquid, and vapor phase including the two phase melting and evaporation region. The mglj cold curve has been used in the past to predict some thermodynamic properties of metals in the compressive phase. According to the authors, if the EOS model satisfies certain spinodal conditions in the liquid–vapor transition region, the EOS should be applicable in the compressive as well as expansion regions. Although the EOS satisfied the spinodal conditions but its validity in the expansion region was not verified. We found that the proposed model could not predict the cohesive energy of the solid properly and hence did not match with electronic structure calculations in the expansion region. So, we propose that instead of relating the parameter ‘n’ appearing in the mglj EOS to the pressure derivative of bulk modulus, one should choose it so as to get good agreement with the experimental value of cohesive energy. In doing so, the cold curve generated matches with electronic structure based calculations in the expansion region. The proposed model has been validated against data available in literature including shock hugoniot and release isentrope data for Aluminum. The melting curve, isobaric expansion curve, liquid–vapor phase diagram and the critical parameters of Aluminum have also been predicted in this work.

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DOI:https://doi.org/10.1016/j.commatsci.2013.09.059

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Citation Information:

Highlights • An improved version of the mGLJ cold model is proposed. • Mean field theory for ions and TF model for electrons were used to generate the solid phase free energy. • The fluid phase free energy is generated using the CRIS model. • The multiphase EOS thus formulated is used to verify a large number of experimental as well as theoretical data. A multiphase equation of state (EOS) model for metals is presented using an improved form of the modified generalized Lennard Jones (mGLJ) cold curve which can account for the solid, liquid, and vapor phase including the two phase melting and evaporation region. The mglj cold curve has been used in the past to predict some thermodynamic properties of metals in the compressive phase. According to the authors, if the EOS model satisfies certain spinodal conditions in the liquid–vapor transition region, the EOS should be applicable in the compressive as well as expansion regions. Although the EOS satisfied the spinodal conditions but its validity in the expansion region was not verified. We found that the proposed model could not predict the cohesive energy of the solid properly and hence did not match with electronic structure calculations in the expansion region. So, we propose that instead of relating the parameter ‘n’ appearing in the mglj EOS to the pressure derivative of bulk modulus, one should choose it so as to get good agreement with the experimental value of cohesive energy. In doing so, the cold curve generated matches with electronic structure based calculations in the expansion region. The proposed model has been validated against data available in literature including shock hugoniot and release isentrope data for Aluminum. The melting curve, isobaric expansion curve, liquid–vapor phase diagram and the critical parameters of Aluminum have also been predicted in this work.

quote

GB/T 7714-2015 [1] Chandrani Bhattacharya. Computational Materials Science, 2014(82). DOI:10.1016/j.commatsci.2013.09.059.
MLA [1] Chandrani Bhattacharya. Computational Materials Science, no. 82, 2014, https://doi.org/10.1016/j.commatsci.2013.09.059.
APA [1] Chandrani Bhattacharya. (2014). Computational Materials Science(82). https://doi.org/10.1016/j.commatsci.2013.09.059
IEEE [1] Chandrani Bhattacharya, Computational Materials Science, no. 82, 2014, doi: 10.1016/j.commatsci.2013.09.059.