Chun-Lu Zhang

Approximate analytic solutions of adiabatic capillary tube

Approximate analytic solution of capillary tube is valuable for theoretical analysis and engineering calculation. In this work, two kinds of approximate analytic solutions of adiabatic capillary tube have been developed. One is the explicit function of capillary tube length. Another is the explicit function of refrigerant mass flow rate. In these solutions, the choked flow condition is taken into account without iterative calculations. The approximate predictions are found to agree reasonably well with experimental data in open literatures.

Dynamic simulation of air-to-water dual-mode heat pump with screw compressor

Abstract A dynamic model of air-to-water dual-mode heat pump with screw compressor is presented here. The high-pressure and low-pressure segments are divided into three control volumes, including the refrigerant inside tube, the tube wall and the fluid outside tube that is water or air. Time dependent ordinary differential equations are obtained from the mass and energy balances for each control volume. As the compressor, thermostatic expansion valve (TEV) body, and reversing valve have very small thermal inertias, steady-state models are applied for the compression, throttling, and leakage processes. The relationship between the temperature of the saturated liquid–vapor mixture in TEV’s bulb and the temperature of the refrigerant vapor at the evaporator exit is described with a time dependent ordinary differential equation. System simulation is finally carried out with ‘predictor–corrector’ and ‘adaptive integration step’ methods. Simulated results are in good agreement with the measured data, which lead to conclusion that the model can be used as a tool for the product development.

Compound fuzzy model for thermal performance of refrigeration compressors

The fuzzy method is introduced to the calculation of thermal performance of refrigeration compressors. A compound model combining classical thermodynamic theory and fuzzy theory is presented and compared with a simple fuzzy model without classical thermodynamic fundamentals. Case study of refrigeration compressors shows that the compound fuzzy model and the simple fuzzy model are both more efficient than the classical thermodynamic method. However, the compound fuzzy model is of better precision and adaptability.

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%.

Semiporous Media Approach for Numerical Simulation of Flow through Large-Scale Sparse Tubular Heat Exchangers

Steady-state, three-dimensional numerical simulations for airflow through large-scale sparse tubular heat exchangers are performed using Fluent (2006). A new porous media approach, semiporous media, is developed to simplify the computational fluid dynamics (CFD) modeling. In this approach, the tube bundle is simplified to a porous media zone that has half real tubes on both sides of it. Three CFD approaches, the real tube one, the semiporous media one, and the conventional porous media one, are compared in the predictions of the tubular heat exchanger airflow distributions. The comparison shows that the semiporous media approach predicts airflow distribution similar to the real tube distributions, reduces computational cost significantly, and can be easily implemented. The heat exchanger pressure drops predicted by the semiporous media approach are compared with the experimental data for eight designs. The results show that this new approach gives reasonable agreement with the experiments.

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.

Neural-Network-Based Polynomial Correlation of Single- and Variable-Speed Compressor Performance

The compressor is one of the major components in a vapor-compression refrigeration system. A neural-network-based polynomial correlation method of positive-displacement compressor performance has been developed that can be applied to both single-speed and variable-speed compressor families. The multi-layer perceptron neural network was used as a universal function approximator. To align with and extend the ARI ten-coefficient correlation method (ARI 1999), the third-order polynomial transfer function is customized in the hidden layer and the pure linear function is adopted in the output layer of the neural network. The ARI ten-coefficient correlation has been proven as a special case of the proposed neural network. The new neural network method can be easily extended to multi-input/multi-output cases. In particular, in modeling the performance of a single-speed or variable-speed compressor family, this method gives less than 1% standard deviations and ±3% maximum deviations against manufacturer data.

Hybrid numerical simulation of large-scale gas-fired tubular heat exchangers

This article presents a hybrid computational fluid dynamics (CFD) modeling approach to the large-scale gas-fired heat exchanger. As the full-grid simulation on this heat exchanger requires impractical computational cost, a simplified “1D + 2D + 3D” hybrid CFD model is developed to reduce the computational cost and make the simulation doable on the common workstations. In this model, the air side is simulated by the 3D CFD model with the porous media simplification on tube bundles. The gas side is modeled by a 2D axisymmetric combustion model and a 1D duct model, which is implemented by the user-defined function (UDF) method. Furthermore, the “1D + 2D” gas-side models are coupled with the “3D” air-side model, while the UDF is coded for updating the boundary conditions on both sides during iterations. Reasonable agreement is achieved between the simulations and the measured results of pressure drop, overall heat transfer rate, air temperature rise, and tube wall temperature distributions.