The critical heat flux (CHF) condition is characterized by a sharp reduction of the local heat transfer coefficient that results from the replacement of liquid by vapor adjacent to the heat transfer surface. We used High Order Neural Network (HONN) as a stronger open box intelligent unit than traditional black box neural networks to predict of CHF at near critical pressures. The process of training and testing this model is done using a set of available published filed data. The CHF values predicted by the HONN model are satisfying compared with the measured data. The predicted values were also compared with those predicted using Feed Forward Neural Network (FNN) and available empirical equations that have been suggested in the literature. It was found that the HONN model with Root Mean Square (RMS) errors of 3.23% in critical pressures conditions has superior performance in predicting the CHF than the best accurate prediction of the methods. Results show by having an HONN model of our nonlinear input-output mapping, there are many advantageous than ANN model including faster running for new data, lesser RMS error and better fitting properties.

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