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

Suvanjan Bhattacharyya, Debraj Sarkar, Rahul Roy, Shramona Chakraborty, Varun Goel and Eydhah Almatrafi
Additional contact information
Suvanjan Bhattacharyya: Department of Mechanical Engineering, Birla Institute of Technology and Science Pilani, Pilani Campus, Vidya Vihar, Pilani 333031, Rajasthan, India
Debraj Sarkar: Department of Textile Technology, Government College of Engineering and Textile Technology, Berhampore, Murshidabad 742101, West Bengal, India
Rahul Roy: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Shramona Chakraborty: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India
Varun Goel: Department of Mechanical Engineering, National Institute of Technology Hamirpur, Hamirpur 177005, HP, India
Eydhah Almatrafi: Mechanical Engineering Department-Rabigh, Center of Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Sustainability, 2021, vol. 13, issue 13, 1-19

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 (?), 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.

Keywords: machine learning; ANN; prediction; fluid flow; heat transfer; enhancement (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:13:p:7477-:d:588544

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