Mass Flow Rate Of R-134a Research Articles

Optimization of a microchannel condenser with separation circuitry

Experiments in the past proved that separation of vapor and liquid was beneficial for a condenser prototype with a specific separation circuitry. The prototype elaborated had the inlet in the middle of the height and separates into two flow paths downstream of the second header. The two paths recombined upstream of the subcooling pass of the condenser. This paper first explains the trade-off between the capacity in the upper passes and the lower passes, based on an experimentally validated steady-state condenser model built in a previous study by the same authors. The condenser model incorporates a mechanistic model for the second header to calculate the separation efficiency. Then, this paper presents a search for the optimal design of a microchannel condenser with the separation circuitry (separation condenser) using R134a. Parameters are optimized on pass circuitry, fin density, and air velocity distribution. An order for the tube number of each pass, in general, is given for the optimal pass circuitry. After the optimization, the separation condenser shows a 17.8% improvement in the mass flow rate of R134a compared to a conventional condenser with the same total airside area.

Depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system using artificial neural network approach

In this work, an experimental investigation is carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system. Various experiments were conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The results showed that mass flow rate through helical coil capillary tube was found lower than straight capillary tube by about 5–16%. Dimensionless correlation and Artificial Neural Network (ANN) models were developed to predict mass flow rate. It was found that dimensionless correlation and ANN model predictions agreed well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 and 0.275 and mean absolute percentage error of 4.75% and 2.31% respectively. The results suggested that ANN model shows better statistical prediction than dimensionless correlation model.

Flow of partially condensed R-134a vapour through an adiabatic capillary tube

This paper presents an experimental study of an adiabatic flow of R-134a through a capillary tube. The paper discusses the details of experimental facility for testing a capillary tube with sub-cooled and two phase inlet conditions of R-134a. The effect of diameter, capillary length, inlet refrigerant quality and inlet pressure on mass flow rate were investigated. The experiments for testing the capillary tubes were proceeded by an integrity test. The vapour quality varied from 0.05 to 0.25. All the experiments were done by varying the pressure from 655 to 724kPa. The effect of two phase inlet conditions on mass flow rate of R-134a through an adiabatic capillary tube is observed. A correlation to predict the mass flow rate of refrigerant flowing through an adiabatic capillary tube is proposed for two phase inlet conditions. The proposed correlation predicts the measured data with an error band of ±25%.

Use of artificial neural network approach for depicting mass flow rate of R134a /LPG refrigerant through straight and helical coiled adiabatic capillary tubes of vapor compression refrigeration system

In this work, an experimental investigation carried out with R134a and LPG refrigerant mixture for depicting mass flow rate through straight and helical coil adiabatic capillary tubes in a vapor compression refrigeration system.Various experiments conducted under steady-state conditions, by changing capillary tube length, inner diameter, coil diameter and degree of subcooling. The outcomes demonstrated that mass flow rate through helical coil capillary tube discovered lower than straight capillary tube by about 5−16%. Dimensionless correlation and Artificial Neural Network (ANN) models developed to predict the mass flow rate. It found that dimensionless correlation and ANN model predictions concurred well with experimental results and brought out an absolute fraction of variance of 0.961 and 0.988, root mean square error of 0.489 kg/h and 0.275 kg/h and mean absolute percentage error of 4.75% and 2.31%, respectively. The outcomes suggested that ANN model shows better statistical prediction than dimensionless correlation model.

Adaptive neuro-fuzzy inference system approach to predict the mass flow rate of R-134a/LPG refrigerant for straight and helical coiled adiabatic capillary tubes in the vapor compression refrigeration system

This study deals with predicting the mass flow rate of R-134a/LPG as refrigerant inside a straight and helical coiled adiabatic capillary tube of vapor compression refrigeration system by combining dimensionless analysis and Adaptive Neuro-Fuzzy Inference System techniques. For this purpose the experimental system was designed and tested under steady state conditions, by changing the length of the capillary tube, the inner diameter of the capillary tube, the coil diameter and the degree of subcooling of the refrigerant at the capillary tube inlet. Dimensional analysis was utilized to provide generalized dimensionless parameters and to reduce the number of input parameters, while Adaptive Neuro-Fuzzy Inference System was applied as a generalized approximator of the nonlinear multi-input and single-output function. The comparison of the absolute fraction of variance (R2) (0.998 and 0.961), the root mean square error (RMSE) (0.105 kg/h and 0.489 kg/h) and the mean absolute percentage error (MAPE) (0.954% and 4.75%) demonstrated the result for combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System and dimensionless correlation model predictions respectively. The results indicated that the combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System gave the best statistical prediction efficiency.

냉매 R1234yf의 모세관내 유동 특성에 관한 해석적 연구

R1234yf has been developed as an alternative refrigerant to R134a, which has been associated with global warming. The capillary tubes as expansion valves control the mass flow rate and balance system pressure in the refrigeration cycle. The present numerical model used the governing equations including the law of conservation of mass, momentum, and energy in a capillary tube. The mass flow rate of R1234yf decreased by 47.0% as the capillary tube length was increased from 1 to 4 m. As the inner diameter of the capillary tubes was changed from 1.3 to 1.7 mm, the mass flow rate of R134a and R1234yf increased by 117.9% and 121.0%, respectively. The mass flow rate of the R134a and R1234yf increased by 28.3% and 29.1% with subcooling increasing from 0 to 7°C. In addition, when the inlet temperature of the capillary tubes was changed from 35 to 60°C, the mass flow rate of R134a and R1234yf increased by 31.0% and 45.4%, respectively.

Assessment of exergy efficiency and Sustainability Index of an air water heat pump

This study is undertaken to predict the second law performance of an air/water heat pump. Through energy and exergy analyses, analytical expressions are derived for the Coefficient of Performance (COP) and Exergy Efficiency (ηexe). The system is analysed to predict how the mass flow rate of R-134a, condensation and evaporation temperatures influence the system performance at various operating conditions. The results indicate that a higher COP and ηexe may arise when increasing the mass flow rate of the refrigerant, increasing the evaporation temperature, or lowering the condensation temperature. Additional results are presented for the optimum performance of the system.

Experimental Study of the Flow of R-134a Through an Adiabatic Helically Coiled Capillary Tube

An experimental investigation was carried out to evaluate the flow characteristics of refrigerant R-134a through an adiabatic helically coiled capillary tube. The effect of various physical parameters like diameter and length of capillary tube, coil pitch, and inlet subcooling on the mass flow rate of R-134a was investigated. Moreover, the refrigerant mass flow rate through an instrumented capillary tube was also compared to that through a noninstrumented capillary tube. It was found that the provision of taps for pressure measurement on the capillary tube surface has a negligible effect on the mass flow rate of R-134a. Further, the coil pitch had a significant effect on the performance of the adiabatic helically coiled capillary tube. It was established that the coil pitch significantly influenced the mass flow rate through the adiabatic helically coiled capillary tube. It was concluded that the effect of coiling of the capillary tube reduces the mass flow rate by 5% to 15%, as compared to those of the straight capillary tube operating under similar conditions. The data obtained from the experiments were analyzed, and a semi-empirical correlation was developed. Almost 97% of the mass flow rate predicted by the proposed correlation lies in the error band of ±10%.

Experimental investigation on diabatic flow of R-134a through spiral capillary tube

The present experimental investigation has been carried out to investigate the effects of various geometric parameters on the mass flow rate of R-134a through diabatic spiral capillary tube. In diabatic flow, the capillary tube is bonded with the compressor suction-line to form a counter-flow exchanger. The lateral type of diabatic capillary tube has been investigated in the present experimental study. The major geometric parameters investigated are capillary tube diameter, capillary tube length and coil pitch. In addition, effect of inlet subcooling on the mass flow rate through diabatic spiral capillary tube is also done. A comparison of the performance of diabatic spiral capillary tube has been made with adiabatic spiral capillary tube. Generalized empirical correlation for diabatic spiral capillary tube has also been proposed. It has been found that the predictions of the proposed correlation lie in the error band of ±7%.

An experimental study of the flow of R-134a inside an adiabatic spirally coiled capillary tube

This paper presents an experimental investigation for the flow of R-134a inside an adiabatic spirally coiled capillary tube. The effect of various geometric parameters like capillary tube diameter, length and coil pitch for different capillary tube inlet subcoolings on the mass flow rate of R-134a through the spiral capillary tube geometry has been investigated. It has been established that the coil pitch significantly influences the mass flow rate of R-134a through the adiabatic spiral capillary tube. The effect of providing pressure taps on the capillary tube surface has a negligible effect on the mass flow rate through the capillary tube. It has been concluded that the effect of coiling of capillary tube reduces the mass flow rate by 5–15% as compared to those of the straight capillary tube operating under similar conditions. The data obtained from the experiments are analyzed and a semi-empirical correlation has been developed. The proposed correlation predicts more than 91% of the mass flow rate which is in agreement with measured data in an error band of ±10%.

A simple method of recording respiration

This study deals with predicting the mass flow rate of R-134a/LPG as refrigerant inside a straight and helical coiled adiabatic capillary tube of vapor compression refrigeration system by combining dimensionless analysis and Adaptive Neuro-Fuzzy Inference System techniques. For this purpose the experimental system was designed and tested under steady state conditions, by changing the length of the capillary tube, the inner diameter of the capillary tube, the coil diameter and the degree of subcooling of the refrigerant at the capillary tube inlet. Dimensional analysis was utilized to provide generalized dimensionless parameters and to reduce the number of input parameters, while Adaptive Neuro-Fuzzy Inference System was applied as a generalized approximator of the nonlinear multi-input and single-output function. The comparison of the absolute fraction of variance (R2) (0.998 and 0.961), the root mean square error (RMSE) (0.105 kg/h and 0.489 kg/h) and the mean absolute percentage error (MAPE) (0.954% and 4.75%) demonstrated the result for combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System and dimensionless correlation model predictions respectively. The results indicated that the combination of dimensional analysis and Adaptive Neuro-Fuzzy Inference System gave the best statistical prediction efficiency.