ANN modeling of compact heat exchangers

Abstract

SUMMARYThis paper focuses on the heat transfer analysis of compact heat exchangers through artificial neural network (ANN). The ANN analysis includes heat transfer coefficient, pressure drop and Nusselt number in the compact heat exchangers by using available experimental results in a case study. In this study, data sets are established in 15 different test channel configurations. A feed-forward back-propagation algorithm is used in the learning process and testing the network. The learning process is applied to correlate the heat transfer analysis for different ratios of rib spacing and height, various Reynolds numbers, different inlet-outlet temperatures, heat transfer areas and hydraulic diameters. Various hidden numbers of the network are trained for the best prediction of the heat transfer analysis. Heat transfer coefficient, pressure drop and Nusselt number values are predicted by the network algorithm. The results are then compared with the experimental results of the case. The trained ANN results perform well in predicting the heat transfer coefficient, pressure drop and Nusselt number with an average absolute mean relative error of less than 6% compared with the experimental results for staggered cylindrical ribbed and staggered triangular ribbed of test channels in the case study. The ANN approach is found to be a suitable method for heat transfer analysis in compact heat exchangers.

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“…The ANN1/2 and ANN2 models had an insignificant lack of fit tests, which means the models satisfactorily predict the municipal waste amounts in the City of Zagreb for 2017. A high r 2 is indicative that the variation was accounted for and that the data fitted the proposed model satisfactorily …”

“…A high r 2 is indicative that the variation was accounted for and that the data fitted the proposed model satisfactorily. [31][32][33]…”

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Application of artificial neural network in performance prediction of PEM fuel cell

“…Under these circumstances, ANN appears to be a suitable alternative for the prediction problems. In 2008, Ermis has focused on the heat transfer analysis of compact heat exchangers through ANN. The trained ANN results perform well in predicting the heat transfer coefficient, pressure drop, and Nusselt number with an average absolute mean relative error of less than 6% compared with the experimental results.…”