  The critical heat flux (CHF) corresponding to the departure from nucleate
boiling (DNB) crisis is essential to the design and safety of a two-phase flow
boiling system. Despite the abundance of predictive tools available to the
thermal engineering community, the path for an accurate, robust CHF model
remains elusive due to lack of consensus on the DNB triggering mechanism. This
work aims to apply a physics-informed, machine learning (ML)-aided hybrid
framework to achieve superior predictive capabilities. Such a hybrid approach
takes advantage of existing understanding in the field of interest (i.e.,
domain knowledge) and uses ML to capture undiscovered information from the
mismatch between the actual and domain knowledge-predicted target. A detailed
case study is carried out with an extensive DNB-specific CHF database to
demonstrate (1) the improved performance of the hybrid approach as compared to
traditional domain knowledge-based models, and (2) the hybrid model's superior
generalization capabilities over standalone ML methods across a wide range of
flow conditions. The hybrid framework could also readily extend its
applicability domain and complexity on the fly, showing an elevated level of
flexibility and robustness. Based on the case study conclusions, the
window-type extrapolation mapping methodology is further proposed to better
inform high-cost experimental work.
