Heat Transfer Correlations in an Air-Water Fin-Tube Compact Heat Exchanger by Symbolic Regression

In the present study we propose the application of evolutionary algorithms to find correlations that can predict the performance of a compact heat exchanger. Genetic programming (GP) is a search technique in which computer codes, representing functions as parse trees, evolve as the search proceeds. As a symbolic regression approach, GP looks for both the functional form and the coefficients that enable the closest fit to experimental data. Two different data sets are used to test the symbolic regression capability of genetic programming, the first being artificial data from a one-dimensional function, while the second are data generated by previously determined correlations from experimental measurements of a single-phase air-water heat exchanger. The results demonstrate that the correlations found by symbolic regression are able to predict well the data from which they were determined, and that the GP technique may be suitable for modeling the nonlinear behavior of heat exchangers. It is also shown that there is not a unique answer for the best-fit correlation from this procedure. The advantage of using genetic programming as symbolic regression is that no initial assumptions on the functional forms are needed, which is contrary to the traditional approach.