Application of New Artificial Neural Network to Predict Heat Transfer and Thermal Performance of a Solar Air-Heater Tube

Abstract

In the present study, the heat transfer and thermal performance of a helical corrugation with perforated circular disc solar air-heater tubes are predicted using a machine learning regression technique. This paper describes a statistical analysis of heat transfer by developing an artificial neural network-based machine learning model. The effects of variation in the corrugation angle (θ), perforation ratio (k), corrugation pitch ratio (y), perforated disc pitch ratio (s), and Reynolds number have been analyzed. An artificial neural network model is used for regression analysis to predict the heat transfer in terms of Nusselt number and thermohydraulic efficiency, and the results showed high prediction accuracies. The artificial neural network model is robust and precise, and can be used by thermal system design engineers for predicting output variables. Two different models are trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The models are evaluated to have an accuracy of 97.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce.

Impact of the COVID-19 on the Destination Choices of Hungarian Tourists: A Comparative Analysis

“…The methodology applied in our study, similarly to these approaches, does not stop at correlation studies; our aim was to explore the question in more depth and to uncover the underlying motivations of changes in destination choice. The main method applied is artificial neural network analysis, which has been used, e.g., in forecasting erosion changes by Peponi et al [34], thermal performance by Bhattacharyya et al [35] and in tourism studies as well, mostly for forecasting tourism demand, e.g., by Claveria et al [36], and Adil et al [37]. In the next section, we present the research methodology and then discuss the evaluation of the results.…”

Correlated-informed neural networks: A new machine learning framework to predict pressure drop in micro-channels

“…A common feature of these works is the excellent correlation degree reported by the ANN models. This feature motivates engineers and designers to implement these models in thermal design applications [15,16,17]. However, these models are not easily reproducible for users, and their implementation is limited by the range of data used in training the ANN.…”

Correlated-informed neural networks: a new machine learning framework to predict pressure drop in micro-channels

“…A common feature of these works is the great correlation degree reported by the ANN models. This feature motivates engineers and designers to implement these models in thermal design applications [15,16,17]. However, these models are not easily reproducible for users, and their implementation is limited by the range of data used in training the ANN.…”