A Viscosity Equation of State for R123 in the Form of a Multilayer Feedforward Neural Network |
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Authors: | G Scalabrin C Corbetti G Cristofoli |
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Affiliation: | (1) Dipartimento di Fisica Tecnica, Università di Padova, via Venezia 1, I-35131 Padova, Italy |
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Abstract: | A multilayer feedforward neural network (MLFN) technique is adopted for developing a viscosity equation = (T, ) for R123. The results obtained are very promising, with an average absolute deviation (AAD) of 1.02% for the currently available 169 primary data points, and are a significant improvement over those of a corresponding conventional equation in the literature. The method requires a high-accuracy equation of state for the fluid to be known to convert the experimental P, T into the independent variables , T, but such equation may not be available for the target fluid. With a view to overcoming this difficulty, a viscosity implicit equation of state in the form of T=T(P, ), avoiding the density variable, is obtained using the MLFN technique, starting from the same data sets as before. The prediction accuracy achieved is comparable with that of the former equation, = (T, ). |
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Keywords: | 2 2-dichloro-1 1 1-trifluoroethane feedforward neural networks R123 viscosity correlation techniques viscosity equation |
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