Volume:8, Issue: 1, 2014 Page No: 20 - 22
ISSN: 2415-6620
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.