کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7175393 | 1466379 | 2018 | 30 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
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
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی مکانیک
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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 5â16%. 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 0.489â¯kg/h and 0.275â¯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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Refrigeration - Volume 86, February 2018, Pages 228-238
Journal: International Journal of Refrigeration - Volume 86, February 2018, Pages 228-238
نویسندگان
Jatinder Gill, Jagdev Singh,