  Boiling heat transfer occurs in many situations and can be used for thermal
management in various engineered systems with high energy density, from power
electronics to heat exchangers in power plants and nuclear reactors.
Essentially, boiling is a complex physical process that involves interactions
between heating surface, liquid, and vapor. For engineering applications, the
boiling heat transfer is usually predicted by empirical correlations or
semi-empirical models, which has relatively large uncertainty. In this paper, a
data-driven approach based on deep feedforward neural networks is studied. The
proposed networks use near wall local features to predict the boiling heat
transfer. The inputs of networks include the local momentum and energy
convective transport, pressure gradients, turbulent viscosity, and surface
information. The outputs of the networks are the quantities of interest of a
typical boiling system, including heat transfer components, wall superheat, and
near wall void fraction. The networks are trained by the high-fidelity data
processed from first principle simulation of pool boiling under varying input
heat fluxes. State-of-the-art algorithms are applied to prevent the overfitting
issue when training the deep networks. The trained networks are tested in
interpolation cases and extrapolation cases which both demonstrate good
agreement with the original high-fidelity simulation results.
