A general equation of state for dense liquid alkali metals

Eslami, Hossein

doi:10.1016/j.jnucmat.2003.12.006

Cited by 8 publications

(14 citation statements)

References 25 publications

(38 reference statements)

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“…Calculation of thermophysical properties using theoretical models is another alternative to remedy the experimental difficulties. At present there are a number of successful equations of state on simple liquid metals, such as alkali metals [4,5] and a number of the other main group metals [6,7]. In comparison with simple metals, such as alkali metals with s electrons, liquid transition metals with partially filled d electrons are more complicated systems and successful prediction could hardly be done for liquid transition metals due to the presence of d electrons.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the density of molten metals

Mehdipour

Boushehri

Eslami

2005

Journal of Non-Crystalline Solids

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Section: Introductionmentioning

confidence: 99%

Prediction of the density of molten metals

Mehdipour

Boushehri

Eslami

2005

Journal of Non-Crystalline Solids

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“…In fact because of the near pressure-independency of liquid density, isotherms of a function of Z vs. a function of density are linear. This is proved by the existence of the afore-cited linear isotherms [13,14,30], all observed for liquid metals, and also for ordinary fluids [31][32][33], but none of these regularities results in a specific potential energy function.…”

Section: Methodsmentioning

confidence: 95%

“…But the conclusion obtained based on this linearity, i.e., obeying a simple LJ (8.5-4) potential energy function, is obviously not correct. It is worth mentioning that in the liquid-like density region, many other isotherms, like (Z-1)V 2 vs. U 2 [14], or PV 2 vs. U 2 [30] for liquid alkali metals, including liquid potassium, have already been reported to be linear. This means that the linearity of (Z-1)V -8.5/3 vs. U -4.5/3 isotherms does not prove the existence of a unique potential, like LJ (8.5-4) potential, for liquids.…”

Section: Methodsmentioning

confidence: 99%

Molecular dynamics simulation of potassium along the liquid-vapor coexistence curve

Eslami

Mozaffari

Moghadasi

2010

JICS

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The applicability of pair potential functions to liquid alkali metals is questionable. On the one hand, some recent reports in the literature suggest the validity of two-parameter pair-wise additive Lennard-Jones (LJ) potentials for liquid alkali metals. On the other hand, there are some reports suggesting the inaccuracy of pair potential functions for liquid metals. In this work, we have performed extensive molecular dynamics simulations of vapor-liquid phase equilibria in potassium to check the validity of the proposed LJ potentials and to improve their accuracy by changing the LJ exponents and taking into account the temperaturedependencies of the potential parameters. We have calculated the orthobaric liquid and vapor densities of potassium using LJ (12-6), LJ (8.5-4) and LJ (5-4), effective pair potential energy functions. The results show that using an LJ (8.5-4) potential energy function with temperature-independent parameters, İ and ı, is inadequate to account for the vapor-liquid coexistence properties of potassium. Taking into account the temperature-dependencies of the LJ parameters, İ(T) and ı(T), we obtained the densities of coexisting liquid and vapor potassium in a much better agreement with experimental data. Changing the magnitude of repulsive and attractive contributions to the potential energy function shows that a two-parameter LJ (5-4) potential can well reproduce the densities of liquid and vapor potassium. The results show that LJ (5-4) potential with temperature-dependent parameters produces the densities of liquid and vapor potassium more accurately, compared to the results obtained using LJ (12-6) and LJ (8.5-4) potential energy functions.

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“…Ghatee et al developed the linear exp-6 isotherm and virial-like equation to estimate thermodynamic properties of alkali systems [13,14]. Eslami used the Parsafar equation of state to estimate isothermal compressibility, isobaric expansion, and density of liquid metals in high temperature and pressure [15]. Sabzevari and Mousavi predicted the density of these liquids by using artificial neural network and a new equation of state in different operational conditions [16,17].…”

Section: Introductionmentioning

confidence: 99%

Evolving Machine Learning Methods for Density Estimation of Liquid Alkali Metals over the Wide Ranges

Lin

Seraj

2022

International Journal of Chemical Engineering

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Alkali metals are widely used as industrial materials in products such as electrochemical cells because of their properties that make them suited to high temperatures. In this study, three computational approaches including gene expression programming (GEP), least squares support vector machine (LSSVM), and adaptive neuro fuzzy inference system (ANFIS) have been suggested to estimate density of different liquid alkali metals in extensive ranges of pressure and temperature. An experimental databank involving 595 experimental alkali metals’ densities has been gathered to prepare and test the models. The mathematical and visual comparisons of these models’ outputs and real density values are used to assess capacities of GEP, LSSVM, and ANFIS models in prediction of alkali metals’ density. The determined R-squared values for GEP, LSSVM, and ANFIS are 0.9999, 1, and 1, respectively. The MSE values are estimated to be 0.9184, 0.815, and 0.154 for GEP, ANFIS, and LSSVM, respectively. According to these results, these models can be suggested as simple and accurate ways for determining alkali metals’ properties. Results showed that LSSVM has the best performance in comparison with GEP and ANFIS. Moreover, the parametric analysis of input parameters is carried out to show the impact of them on alkali metals’ density. According to this analysis, the amount of lithium can be the most effective parameter on the mixture density.

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A general equation of state for dense liquid alkali metals

Cited by 8 publications

References 25 publications

Prediction of the density of molten metals

Prediction of the density of molten metals

Molecular dynamics simulation of potassium along the liquid-vapor coexistence curve

Evolving Machine Learning Methods for Density Estimation of Liquid Alkali Metals over the Wide Ranges

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