OPTIMIZATION OF THE RELATIONSHIP BETWEEN THE TWO-PHASE FRICTION FACTOR AND REYNOLDS EQUIVALENT NUMBER MODEL BY MEANS OF GENETIC ALGORITHM

Abstract

The two-phase friction factor of R134a in a copper smooth tube having an inner diameter of 8.1 mm and length of 0.5 m during downward condensation is investigated experimentally and numerically. The test runs were performed at average condensing temperatures of 40-50 degrees C. The mass fluxes were around 260, 340 and 456 kg m(-2)s(-1) and the heat fluxes were between 11.3 and 55.3 kW m(-2). Accuracy of the dataset was proven in many papers in the literature. The quality of the refrigerant in the test section is calculated considering the temperature and pressure obtained from the experiment. The pressure drop across the test section is directly measured by a differential pressure transducer. The equivalent Reynolds number is considered to be the significant variable for the analysis. A Genetic Algorithm (GA), is one of the most successful methods among evolution algorithms, is applied for the optimization of the relationship between the two-phase friction factor and Reynolds equivalent number model in this study. The annular flow condensation process in the vertical test tube is expressed using GA method successfully. Regression analysis was done including the Reynolds equivalent number and other measured values such as pressure drop and mass flux of R134a, and gave a convincing correlation based on 182 smooth tube data points for practical applications. The most suitable coefficients of the proposed correlations are depicted to be compatible with the experiment by way of the algorithm.