COMPARISON STUDY BETWEEN THEORETICAL ANALYSIS AND ARTIFICIAL NEURAL NETWORK OF THE CAPILLARY TUBE

Abstract

The main purpose of expansion devices is reduced the higher pressure of the working fluid from the condenser pressure to the evaporator pressure. There are several kinds of expansion devices, one of these types is capillary tube which is common utilized in small size refrigeration systems. In this work, the effect of the diameter of capillary tube and mass flow rate of the refrigerant on the physical properties of the refrigerant within the capillary tube have been conducted. Moreover, an artificial neural network (ANN) technique has been utilized in order to clarify the possibility of applying this theory to the effect of such parameters on the results of the capillary tube. The study has been shown that there is a very good agreement between experimental and numerical results. The diameter and mass flow rate have impact on the length of the capillary tube, increase diameter leads to increase the capillary tube length while increase mass flow rate leads to decrease the length. Furthermore, the results shown that ANN technique can be employed to study the effect of such as parameters that considered in this on length of capillary tube. So, it can be using latter technique with accuracy 95%.

Keywords

• Capillary Tube
• Refrigerant Cycle
• Physical Properties of Refrigerant
• ANN Technique

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