Comparison of different approaches for numerical modeling of nanofluid subcooled flow boiling and proposing predictive models using artificial neural network

Nanofluids subcooled flow boiling was numerically studied using the Two-fluid model and Three-phase model. The Eulerian approach simulated the contraction of the base fluid and vapor, and the interaction between base fluid and nanoparticles was simulated by the Euler-Lagrange approach; the two Euler-Euler and Euler-Lagrange methods were solved together. The simulations were done in three volume fractions of nanoparticles, 0.0935%, 0.28%, and 0.561%. In the low concentration of nanoparticles, the difference between the results of these ap-proaches was negligible, but by increasing the nanoparticles concentration, the difference increased. When the nanoparticles' volume fraction was 0.28%, the difference between average volume fractions of vapor at the outlet was 16.13%. When the nanoparticle volume fraction was increased up to 0.561%, the difference increased to 28.3%. Also, a comparative study of the two approaches is presented. The Three-phase modeling seems to be the more accurate model. Consequently, a large number of simulations have been carried out to propose pre-dictive models for heat transfer coefficient and vapor volume fraction based on artificial neural networks.