Numerical simulation of heat transfer improvement of a new designed artificially roughened solar air heater using triangular ribs with semi-circular nooks

Zina, B.; Filali, Abdelkader; Benamara, Nabil; Laouedj, Samir; Ahmed, Hamdi E.

doi:10.1080/15567036.2020.1825564

Cited by 8 publications

(1 citation statement)

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“…The performance of both the models reported an accuracy of more than 97.00% on the test dataset. Some recent work on solar air-heaters with various types of turbulator inserts are reported by many researchers [69][70][71][72][73], but the authors failed to predict unknown parameters. Predicting unknown parameters is very important.…”

Section: Resultsmentioning

confidence: 99%

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

Bhattacharyya

Sarkar²,

Roy

et al. 2021

Sustainability

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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.

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