A correlation development for predicting the pressure drop of various refrigerants during condensation and evaporation in horizontal smooth and micro-fin tubes

International Communications in Heat and Mass Transfer

Volumn 39, Issue 7, 2012, Pages 937-944

  Balcilar, M ^a   Dalkilic, A S ^a   Agra, O ^a   Atayilmaz, S O ^a   Wongwises, S ^b,c  

^a YILDIZ TECHNICAL UNIVERSITY   (Turkey)

^b KING MONGKUT'S UNIVERSITY OF TECHNOLOGY THONBURI   (Thailand)

^c Academy of Science   (Thailand)

Author keywords

Artificial neural network (ANN); Condensation; Correlation development; Evaporation; Micro fin; Multi layer perceptron (MLP); Non linear least squares (NLS); Pressure drop

Indexed keywords

AVERAGE ERRORS; COPPER TUBES; COUNTER-CURRENT FLOW; CRITICAL PRESSURES; CROSS-VALIDATION TESTS; DATA POINTS; DOUBLE-TUBE HEAT EXCHANGER; DYNAMIC VISCOSITIES; EMPIRICAL CORRELATIONS; ERROR RATE; EVAPORATION AND CONDENSATION; EVAPORATION PRESSURE; EVAPORATION TESTS; EXPERIMENTAL DATA; EXPERIMENTAL SETUP; HYDRAULIC DIAMETER; IN-TUBE CONDENSATION; INPUT VALUES; MASS FRACTION; MICRO FINS; MICROFIN TUBE; MULTI LAYER PERCEPTRON; NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY; NON-LINEAR LEAST SQUARES; NUMERICAL TECHNIQUES; PRESSURE DROP CORRELATION; R32/R134A; SATURATION TEMPERATURE; TEST RUNS; TEST SECTIONS; TOTAL PRESSURE DROP; TUBE LENGTH; VAPOR PHASIS; VAPOR QUALITY;

CONDENSATION; EVAPORATION; FINS (HEAT EXCHANGE); LIQUIDS; NEURAL NETWORKS; PRESSURE DROP; REFRIGERANTS; SURFACE RECONSTRUCTION; TESTING; VAPORS;

TUBES (COMPONENTS);

EID: 84863873859     PISSN: 07351933     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.icheatmasstransfer.2012.05.005     Document Type: Article

References (10)

1. Balcilar M., Dalkilic A.S., Wongwises S. Artificial neural network (ANN) techniques for the determination of condensation heat transfer characteristics during downward annular flow of R134a inside a vertical smooth tube. International Communications in Heat and Mass Transfer 2011, 38:75-84.
2. Balcilar M., Dalkilic A.S., Wongwises S. Determination of condensation heat transfer characteristics of R134a by means of artificial intelligence method. International Mechanical Engineering Congress and Exposition, ASME November 12-18 2010, USA.
3. Balcilar M., Dalkilic A.S., Bolat B., Wongwises S. Investigation of empirical correlations on the determination of condensation heat transfer characteristics using computational numerical methods during downward annular flow of R134a inside a vertical smooth tube. Journal of Mechanical Science and Technology 2011, 25(10):1-20.
4. Bolat B., Balcilar M., Dalkilic A.S., Wongwises S. Optimization of the relationship between the two-phase friction factor and Reynolds equivalent number model by the means of Genetic Algorithm. ASME/JSME 8th Thermal Engineering Joint Conference, ASME March 13-17 2011, Hawaii.
5. 2 nanofluids in a double-tube counter flow heat exchanger. International Communications in Heat and Mass Transfer 2011, 38:218-228.
6. Demir H., Agra O., Atayilmaz Ş.Ö. Generalized neural network model of alternative refrigerant (R600a) inside a smooth tube. International Communications in Heat and Mass Transfer 2009, 36:744-749.
7. Agra O., Demir H., Ozgur Atayilmaz S., Kantas F., Dalkilic A.S. Numerical investigation of heat transfer and pressure drop in enhanced tubes. International Communications in Heat and Mass Transfer 2011, 38:1384-1391.
8. Choi J.Y., Kedzierski M.A., Domanski P.A. A generalized pressure drop correlation for evaporation and condensation of alternative refrigerants in smooth and micro-fin tubes. NISTIR 1999, 6333:1-50.
9. Eckels S.J., Pate M.B. In-tube evaporation and condensation of refrigerant-lubricant mixtures of HFC-134a and CFC-12. ASHRAE Transaction 1991, 97:62-67.
10. Butterworth D. A comparison of some void-fraction relationships for co-current gas-liquid flow. International Journal of Multiphase Flow 1975, 1:845-850.