Genetic optimization of heat transfer correlations for evaporator tube flows

Two-phase heat transfer coefficients for internal flows play a critical role in the design and analysis of evaporators and condensers. Previous studies propose empirical relations that combine the effects of nucleate and convective boiling onto the overall heat transfer coefficient. Although these relatively simple empirical relations offer physical insight on the nucleation, boiling and flow processes, they come at the expense of some computational accuracy. In this work, we explored new techniques to determine two-phase heat transfer coefficients for refrigerants R-22, R-134a and R-404a. We used multiple functional forms for the heat transfer coefficients and considered multiple dimensionless parameters as inputs to the algebraic relations. We used genetic algorithms to search the solution space that consists of the input parameters plus the different functional forms, and obtained optimal empirical correlations that cover a wide range of heat transfer regimes. Then, we combined genetic algorithm and artificial neural networks to obtain a more universal correlation. Two versions were developed for each correlation: one that assumes a priori knowledge of the local heat flux and another that does not. Several error metrics were computed for all the correlations developed and compared against correlations from the literature. We conclude that substantial improvements can be achieved in both accuracy and robustness of the correlations by using advanced optimization techniques.