A viscosity equation of state for R134a through a multi-layer feedforward neural network technique

Abstract A multi-layer feedforward neural network (MLFN) technique is adopted for developing a viscosity equation η = η ( ρ , T ) for R134a. The results obtained are very promising, with an average absolute deviation (AAD) of 0.63% for the currently available 571 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 in order to convert the experimental P , T into the independent variables ρ , T , but such an equation may not be available for the target fluid. Aiming at overcoming this difficulty, a viscosity implicit equation of state in the form T = T ( η , P ), avoiding the density variable, is developed for the liquid surface. The attained accuracy level is equivalent to that of the former equation. The proposed technique, being completely correlative and non theoretically founded, is also a powerful tool for experimental data screening.