A conformable artificial neural network model to improve the void fraction prediction in helical heat exchangers

This study proposes a conformable artificial neural network model to improve the void fraction prediction in helical heat exchangers. The obtained model had only one neuron in the hidden layer, achieving an algebraic structure simpler than the classic training. Furthermore, this model satisfies the interval condition of [0-1] and is a function of vapor fraction, density ratio, and viscosity ratio. The new conformable ANN void fraction satisfactorily described the two-phase flow in the systems mentioned above because the outlet temperatures were predicted with 2.96% of RMSE, lower than those obtained with other void fraction model analyses. This paper describes the conformable Logistic Sigmoid Transfer Function (CLOGSIG) and its application advantages in the ANN training process. Using CLOGSIG as a transfer function can get different sinusoidal behaviors that can modify the data distribution around the function's sinusoidal area, allowing better data adaptability and improving network performance.