Generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes using artificial neural network |
| |
Authors: | Chun-Lu Zhang |
| |
Affiliation: | aInstitute of Refrigeration and Cryogenics, Shanghai Jiaotong University, Shanghai 200030, China;bChina R&D Center, Carrier Corporation, 3/F, Financial Square, 333 Jiu Jiang Road, Shanghai 200001, China |
| |
Abstract: | A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems. Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and artificial neural network (ANN). Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a three-layer feedforward ANN is served as a universal approximator of the nonlinear multi-input and single-output function. For ANN training and test, measured data for R12, R134a, R22, R290, R407C, R410A, and R600a in the open literature are employed. The trained ANN with just one hidden neuron is good enough for the training data with average and standard deviations of 0.4 and 6.6%, respectively. By comparison, for two test data sets, the trained ANN gives two different results. It is well interpreted by evaluating the outlier with a homogeneous equilibrium model. |
| |
Keywords: | Tube Capillary Modelling Neural network Flow Refrigerant R12 R290 R600a R134a R407C R22 R410AMots clé s: Tube Capillaire Modé lisation Ré seau neuronal Dé bit Frigorigè ne R12 R290 R600a R134a R407C R22 R410A |
本文献已被 ScienceDirect 等数据库收录! |
|