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.2006.01.010

Cited by 6 publications

(3 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of neurons in the hidden layer J can be varied searching for the optimization of the minimum value of the objective function FOB resulting from the regression procedure at each J value. Further details will be available in Part II of this work [11], where the proposed model is applied to draw the TC dedicated equation of two target fluids from their experimental data.…”

Section: The Proposed Ecs Modelmentioning

confidence: 99%

See 1 more Smart Citation

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

Scalabrin

Marchi

Stringari

et al. 2006

Fluid Phase Equilibria

Self Cite

View full text Add to dashboard Cite

Section: The Proposed Ecs Modelmentioning

confidence: 99%

“…After the testing step has been successfully completed, the shape functions can be directly regressed from experimental data to get dedicated TC equations for the fluids of interest. This second step will be developed in a further paper [11].…”

Section: Testing Of the New Ecs Modelmentioning

confidence: 99%

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

Scalabrin

Marchi

Stringari

et al. 2006

Fluid Phase Equilibria

Self Cite

View full text Add to dashboard Cite

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract