Liang Yang

A Generalized Neural Network Model of Refrigerant Mass Flow Through Adiabatic Capillary Tubes and Short Tube Orifices

Adiabatic capillary tubes and short tube orifices are widely used as expansive devices in refrigeration, residential air conditioners, and heat pumps. In this paper, a generalized neural network has been developed to predict the mass flow rate through adiabatic capillary tubes and short tube orifices. The input/output parameters of the neural network are dimensionless and derived from the homogeneous equilibrium flow model. Three-layer backpropagation (BP) neural network is selected as a universal function approximator. Log sigmoid and pure linear transfer functions are used in the hidden layer and the output layer, respectively. The experimental data of R12, R22, R134a, R404A, R407C, R410A, and R600a from the open literature covering capillary and short tube geometries, subcooled and two-phase inlet conditions, are collected for the BP network training and testing. Compared with experimental data, the overall average and standard deviations of the proposed neural network are 0.75% and 8.27% of the measured mass flow rates, respectively.

Network Modeling of Fin-and-Tube Evaporator Performance Under Dry and Wet Conditions

A new neural network modeling approach to the evaporator performance under dry and wet conditions has been developed. Not only the total cooling capacity but also the sensible heat ratio and pressure drops on both air and refrigerant sides are modeled. Since the evaporator performance under dry and wet conditions is, respectively, dominated by the dry-bulb temperature and the web-bulb temperature, two neural networks are used together for capturing the characteristics. Training of a multi-input multi-output neural network is separated into training of multi-input single-output neural networks for improving the modeling flexibility and training efficiency. Compared with a well-developed physics-based model, the standard deviations of trained neural networks under dry and wet conditions are less than 1% and 2%, respectively. Compared with the experimental data, errors fall into ±5%.

Modeling of Supercritical CO2 Flow Through Short Tube Orifices

The transcritical cycle of carbon dioxide (CO 2 ) is a promising alternative approach to heat pumps and automobile air conditioners. As an expansion device, the short tube orifice in a transcritical CO 2 system usually receives supercritical fluid at the entrance and discharges a two-phase mixture at the exit. In this work, a two-fluid model (TFM) is developed for modeling the flow characteristics of supercritical CO 2 through the short tube orifice. The deviations between the TFM predictions and the measured mass flow rates are within ±20%. Meanwhile, the TFM predicts reasonable pressure, temperature, and velocity distributions along the tube length. The small values of interphase temperature difference and velocity slip indicate that the nonequilibrium characteristics of the two-phase flow of CO 2 in the short tube orifice are not significant. Consequently, the homogeneous equilibrium model reduced from the TFM gives a good prediction of the mass flow rate as well.