Theoretical analysis on vapor quality incipience of dryout during flow boiling

This thesis presents a theoretical and data-analysis of vapor quality at the onset of dryout during flow boiling. The primary objective is to evaluate and compare the accuracy of existing theoratical models by comparing their predictions with experimental data. Several theoretical correlations are evaluated under different conditions to determine their reliability and limitations. In addition deep learning techniques are applied to enhance the prediction of vapor quality, using a neural network trained on experimental datasets. At the end a new correlation of dryout incipience quality has been developed.