Empirical correlation study of dryout heat transfer at high pressure using high order neural network and feed forward neural network

Abstract

A hybrid high order neural network (HHONN) and a feed forward neural network (FNN) are developed and applied to find an optimized empirical correlation for prediction of dryout (DO) heat transfer. The values predicted by the HHONN and FNN models are compared with each other and also with the previous values of empirical correlation. HONN successfully provides an efficient open-box model of nonlinear input–output mapping which provides easier understanding of data mining. By removing the hidden layers, HONN structures become simpler than FNNs and initialization of learning parameters (weights) will not be catastrophic. The RMS results show that the HHONN model has superior fitting specification for prediction of DO heat transfer problem compared to the other prediction methods.