The hybrid artificial neural network-first principle modeling (ANN-FPM) is a powerful and flexible methodology that can be particularly useful for complex multi-phase flow processes. In this method the flow state variables are obtained from the solution of conservative equations using first principle-based closure relations whenever possible, and using trained artificial neural networks for poorly-understood rate processes. The ANN-FPM methodology is applied to a set of experimental data representing critical heat flux in a uniformly heated horizontal annular test section cooled with water. The first principle method is based on numerical solution of one-dimensional two-phase equilibrium conservation equations with velocity slip represented by an algebraic slip ratio correlation. The critical heat flux is predicted using trained neural networks that use local hydrodynamic parameters estimated by the first principle method. It is shown that the methodology works well for the studied data.

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