Assessment of heat transfer characteristics of a corrugated heat exchanger based on various corrugation parameters using artificial neural network approach

The complexity of the fluid flow process involved makes it difficult to estimate the characteristics of corrugated tubes. Heat exchangers are often designed to be more efficient by using numerical techniques. Recently, machine learning algorithms have become a viable method for assessing the behaviors of flow and heat transfer in corrugated tubes. Based on a set of data, machine learning algorithms can better estimate how efficient a heat exchanger is. In the present study, an artificial neural network is conducted to figure out the Nusselt number, friction factor, and performance evaluation criteria for heat transfer in straight corrugated tubes based on flow rate and corrugation parameters. The Reynolds number varies between 480 and 6100, spanning various flow regimes in the corrugated tubes, while the corrugation pitch and corrugation depth change between 6 mm and 18 mm and 0.6 and 1.0 mm, respectively. After totaling 220 data points, the network structure with a multilayer perceptron structure is trained. The Levenberg-Marquardt algorithm is performed for training with 17 neurons in the hidden layer. The established neural network structure forecasts Nusselt number, friction factor, and performance evaluation criteria parameters with deviation rates of 0.11 %, - 0.63 %, and 0.17 %, respectively. The neural network exhibits higher performance when compared to related correlations from the literature. This study is a novel one in open sources due to using artificial neural networks to estimate the flow and thermal behaviors in corrugated tubes operating at low flow rates. The current recommended approach may be regarded as a beneficial tool particularly for thermal systems as it aids designers in enhancing the system efficiency with accurate estimations.