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- W2393188894 abstract "A lumped parameter model simplified from a general distributed parameter model was established. The weighted factor of average specific volume in two phase region was presented as a characteristic variable. The correlative factors of the characteristic variable were analyzed and transformed into dimensionless form. The nonlinear mapping between the characteristic variable and its correlative factors was built by a forward neural network. The learning samples were generated with the working fluid R12. The checking samples covers the working fluids R12, R22, R134a and R600a. In the common range of refrigeration and air conditioning working conditions, the new model was compared with the general distributed parameter model. The average deviation falls into 0.3%, while the maximum deviations are not greater than 5% except R600a being used as the working fluid. The computation speed of the new model is one order of magnitude higher than that of the distributed parameter's one. Therefore, the new simple model for adiabatic capillary tube is of good theoretical precision and generalization. It is suitable for simulation and optimization of refrigeration systems." @default.
- W2393188894 created "2016-06-24" @default.
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- W2393188894 date "2000-01-01" @default.
- W2393188894 modified "2023-09-25" @default.
- W2393188894 title "General Simple Model for Adiabatic Capillary Tubes Based on the Identification of Average Specific Volume Using Artificial Neural Network" @default.
- W2393188894 hasPublicationYear "2000" @default.
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