Application of Neural Networks to Predict Changing the Diameters of Bubbles in Pool Boiling Distilled Water

In this research, the capability of neural networks in 
modeling and learning complicated and nonlinear relations has been 
used to develop a model for the prediction of changes in the diameter 
of bubbles in pool boiling distilled water. The input parameters used 
in the development of this network include element temperature, heat 
flux, and retention time of bubbles. The test data obtained from the 
experiment of the pool boiling of distilled water, and the 
measurement of the bubbles form on the cylindrical element. The 
model was developed based on training algorithm, which is 
typologically of back-propagation type. Considering the correlation 
coefficient obtained from this model is 0.9633. This shows that this 
model can be trusted for the simulation and modeling of the size of 
bubble and thermal transfer of boiling.