Thermal conductivity equations for pure fluids in a heuristic extended corresponding states framework

Scalabrin, G.; Marchi, P.; Stringari, Paolo; Richon, Dominique

doi:10.1016/j.fluid.2006.01.009

Cited by 5 publications

(10 citation statements)

References 18 publications

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“…[1]. A concise description of the model formalism is here presented only for convenience of the reader of the present work to allow the new thermal conductivity equations to be easily applied.…”

Section: The Ecs-nn Modelmentioning

confidence: 99%

“…[1], suggest that it should be directly applied using experimental data for the development of dedicated thermal conductivity equations. In a previous work [6] a similar problem has been dealt with for the representation of the viscosity surface of pure fluids by means of the ECS-NN framework.…”

Section: Application Of the Ecs-nn Modelmentioning

confidence: 99%

“…In the first part of this work [1] an innovative extended corresponding states (ECS) model was proposed for the development of dedicated thermal conductivity (TC) equations on the whole λ, ρ, T surface of a fluid of interest. The goal of the method is to obtain the thermal conductivity equation of a target fluid by distorting the reduced independent variables of the thermal conductivity equation of a reference fluid through suitable shape functions.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed model was at first applied to data generated through dedicated thermal conductivity equations [1] in order to work in absence of experimental error noise and aiming at giving prominence only to the error due to the applied method.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Thermal conductivity equations for pure fluids in a heuristic extended corresponding states framework

Scalabrin¹,

Marchi²,

Stringari³

et al. 2006

Fluid Phase Equilibria

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Section: The Ecs-nn Modelmentioning

confidence: 99%

Section: Application Of the Ecs-nn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Thermal conductivity equations for pure fluids in a heuristic extended corresponding states framework

Scalabrin¹,

Marchi²,

Stringari³

et al. 2006

Fluid Phase Equilibria

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“…The chosen neural network in the MLFN format is an effective and powerful function approximator [21] with an a priori known functional form, and it was formerly applied in a similar framework for modeling the thermodynamic properties [16-18, 24, 25] and the transport properties [26][27][28][29] of pure fluids and mixtures. The use of neural networks for the representation of the scale factor functions of the model is indicated by the name "EEoS-NN" given to the proposed technique.…”

Section: Representation Of the Scale Factorsmentioning

confidence: 99%

An Extended Equation of State Modeling Method II. Mixtures

et al. 2006

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This work is the extension of previous work dedicated to pure fluids. The same method is extended to the representation of thermodynamic properties of a mixture through a fundamental equation of state in terms of the Helmholtz energy. The proposed technique exploits the extended correspondingstates concept of distorting the independent variables of a dedicated equation of state for a reference fluid using suitable scale factor functions to adapt the equation to experimental data of a target system. An existing equation of state for the target mixture is used instead of an equation for the reference fluid, completely avoiding the need for a reference fluid. In particular, a Soave-Redlich-Kwong cubic equation with van der Waals mixing rules is chosen. The scale factors, which are functions of temperature, density, and mole fraction of the target mixture, are expressed in the form of a multilayer feedforward neural network, whose coefficients are regressed by minimizing a suitable objective function involving different kinds of mixture thermodynamic data. As a preliminary test, the model is applied to five binary and two ternary haloalkane mixtures, using data generated from existing dedicated equations of state for the selected mixtures. The results show that the method is robust and straightforward for the effective development of a mixturespecific equation of state directly from experimental data.

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